{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"# Getting started\n",
"\n",
"Once you've chosen your scenario, download the data from the Iowa website in csv format. Start by loading the data with pandas. You may need to parse the date columns appropriately."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"#sales = pd.read_csv(\"iowa_liquor_sales_proj_2.csv\")\n",
"sales = pd.read_csv(\"Iowa_Liquor_sales_sample_10pct.csv\")\n",
"## Load the data into a DataFrame\n",
"# pd.read_csv()\n",
"\n",
"## Transform the dates if needed, e.g.\n",
"# df[\"Date\"] = pd.to_datetime(df[\"Date\"], format=\"%m-%d-%y\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Explore the data\n",
"\n",
"Perform some exploratory statistical analysis and make some plots, such as histograms of transaction totals, bottles sold, etc."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\compat\\pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
" from pandas.core import datetools\n"
]
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import scipy.stats as stats\n",
"\n",
"plt.style.use('fivethirtyeight')\n",
"# plt.style.use('ggplot')\n",
"%matplotlib inline\n",
"%config InlineBackend.figure_format = 'retina'\n",
"\n",
"import sklearn.linear_model as skl\n",
"import statsmodels.api as sm\n",
"from sklearn import datasets, linear_model\n",
"from sklearn.model_selection import cross_val_score, cross_val_predict\n",
"from sklearn import metrics\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.metrics import roc_auc_score as roc_auc\n",
"from sklearn.model_selection import train_test_split\n",
"from matplotlib.colors import ListedColormap\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.datasets import make_moons, make_circles, make_classification\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.svm import SVC\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n",
"from sklearn.naive_bayes import GaussianNB"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def eda_tool(df):\n",
"\n",
" import pandas as pd\n",
" \n",
" dict_list = []\n",
" for col in df.columns:\n",
" data = df[col]\n",
" dict_ = {}\n",
" # The null count for a column. Columns with no nulls are generally more interesting\n",
" dict_.update({\"null_count\" : data.isnull().sum()})\n",
" # Counting the unique values in a column\n",
" # This is useful for seeing how interesting the column might be as a feature\n",
" dict_.update({\"unique_count\" : len(data.unique())})\n",
" # Finding the types of data in the column\n",
" # This is useful for finding out potential problems with a column having strings and ints\n",
" dict_.update({\"data_type\" : set([type(d).__name__ for d in data])})\n",
" #dict_.update({\"score\" : match[1]})\n",
" dict_list.append(dict_)\n",
" eda_df = pd.DataFrame(dict_list)\n",
" eda_df.index = df.columns\n",
" eda_df = eda_df.sort_values(['null_count','unique_count'], ascending=[True, False])\n",
"\n",
" print(\"dataframe shape \\n\", df.shape, '\\n')\n",
" print(\"dataframe index \\n\", df.index[:5], '\\n')\n",
" print(\"dataframe describe \\n\", df.describe())\n",
"\n",
" return eda_df"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"dataframe shape \n",
" (270955, 18) \n",
"\n",
"dataframe index \n",
" RangeIndex(start=0, stop=5, step=1) \n",
"\n",
"dataframe describe \n",
" Store Number County Number Category Vendor Number \\\n",
"count 270955.000000 269878.000000 2.708870e+05 270955.00000 \n",
"mean 3590.263701 57.231642 1.043888e+06 256.43443 \n",
"std 947.662050 27.341205 5.018211e+04 141.01489 \n",
"min 2106.000000 1.000000 1.011100e+06 10.00000 \n",
"25% 2604.000000 31.000000 1.012200e+06 115.00000 \n",
"50% 3722.000000 62.000000 1.031200e+06 260.00000 \n",
"75% 4378.000000 77.000000 1.062310e+06 380.00000 \n",
"max 9023.000000 99.000000 1.701100e+06 978.00000 \n",
"\n",
" Item Number Bottle Volume (ml) Bottles Sold Volume Sold (Liters) \\\n",
"count 270955.000000 270955.000000 270955.000000 270955.000000 \n",
"mean 45974.963300 924.830341 9.871285 8.981351 \n",
"std 52757.043086 493.088489 24.040912 28.913690 \n",
"min 168.000000 50.000000 1.000000 0.100000 \n",
"25% 26827.000000 750.000000 2.000000 1.500000 \n",
"50% 38176.000000 750.000000 6.000000 5.250000 \n",
"75% 64573.000000 1000.000000 12.000000 10.500000 \n",
"max 995507.000000 6000.000000 2508.000000 2508.000000 \n",
"\n",
" Volume Sold (Gallons) \n",
"count 270955.000000 \n",
"mean 2.372830 \n",
"std 7.638182 \n",
"min 0.030000 \n",
"25% 0.400000 \n",
"50% 1.390000 \n",
"75% 2.770000 \n",
"max 662.540000 \n"
]
},
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" data_type | \n",
" null_count | \n",
" unique_count | \n",
"
\n",
" \n",
" \n",
" \n",
" Sale (Dollars) | \n",
" {str} | \n",
" 0 | \n",
" 6580 | \n",
"
\n",
" \n",
" Item Number | \n",
" {int64} | \n",
" 0 | \n",
" 2696 | \n",
"
\n",
" \n",
" Item Description | \n",
" {str} | \n",
" 0 | \n",
" 2173 | \n",
"
\n",
" \n",
" Store Number | \n",
" {int64} | \n",
" 0 | \n",
" 1400 | \n",
"
\n",
" \n",
" State Bottle Retail | \n",
" {str} | \n",
" 0 | \n",
" 1112 | \n",
"
\n",
" \n",
" State Bottle Cost | \n",
" {str} | \n",
" 0 | \n",
" 1086 | \n",
"
\n",
" \n",
" Zip Code | \n",
" {str} | \n",
" 0 | \n",
" 415 | \n",
"
\n",
" \n",
" City | \n",
" {str} | \n",
" 0 | \n",
" 385 | \n",
"
\n",
" \n",
" Date | \n",
" {str} | \n",
" 0 | \n",
" 274 | \n",
"
\n",
" \n",
" Volume Sold (Liters) | \n",
" {float64} | \n",
" 0 | \n",
" 265 | \n",
"
\n",
" \n",
" Volume Sold (Gallons) | \n",
" {float64} | \n",
" 0 | \n",
" 261 | \n",
"
\n",
" \n",
" Bottles Sold | \n",
" {int64} | \n",
" 0 | \n",
" 137 | \n",
"
\n",
" \n",
" Vendor Number | \n",
" {int64} | \n",
" 0 | \n",
" 116 | \n",
"
\n",
" \n",
" Bottle Volume (ml) | \n",
" {int64} | \n",
" 0 | \n",
" 29 | \n",
"
\n",
" \n",
" Category | \n",
" {float64} | \n",
" 68 | \n",
" 84 | \n",
"
\n",
" \n",
" Category Name | \n",
" {str, float} | \n",
" 632 | \n",
" 72 | \n",
"
\n",
" \n",
" County Number | \n",
" {float64} | \n",
" 1077 | \n",
" 100 | \n",
"
\n",
" \n",
" County | \n",
" {str, float} | \n",
" 1077 | \n",
" 100 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" data_type null_count unique_count\n",
"Sale (Dollars) {str} 0 6580\n",
"Item Number {int64} 0 2696\n",
"Item Description {str} 0 2173\n",
"Store Number {int64} 0 1400\n",
"State Bottle Retail {str} 0 1112\n",
"State Bottle Cost {str} 0 1086\n",
"Zip Code {str} 0 415\n",
"City {str} 0 385\n",
"Date {str} 0 274\n",
"Volume Sold (Liters) {float64} 0 265\n",
"Volume Sold (Gallons) {float64} 0 261\n",
"Bottles Sold {int64} 0 137\n",
"Vendor Number {int64} 0 116\n",
"Bottle Volume (ml) {int64} 0 29\n",
"Category {float64} 68 84\n",
"Category Name {str, float} 632 72\n",
"County Number {float64} 1077 100\n",
"County {str, float} 1077 100"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eda_tool(sales)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Invoice/Item Number 0\n",
"Date 0\n",
"Store Number 0\n",
"Store Name 0\n",
"Address 0\n",
"City 0\n",
"Zip Code 0\n",
"Store Location 0\n",
"County Number 10913\n",
"County 10913\n",
"Category 779\n",
"Category Name 6109\n",
"Vendor Number 0\n",
"Vendor Name 0\n",
"Item Number 0\n",
"Item Description 0\n",
"Pack 0\n",
"Bottle Volume (ml) 0\n",
"State Bottle Cost 0\n",
"State Bottle Retail 0\n",
"Bottles Sold 0\n",
"Sale (Dollars) 0\n",
"Volume Sold (Liters) 0\n",
"Volume Sold (Gallons) 0\n",
"dtype: int64"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sales.isnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are around 10,000 null values, as this data frame is 2.7 million rows I am dropping the null rows"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sales = sales.dropna()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"35"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sales.duplicated().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are no duplicated rows"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Date', 'Store Number', 'City', 'Zip Code', 'County Number', 'County',\n",
" 'Category', 'Category Name', 'Vendor Number', 'Item Number',\n",
" 'Item Description', 'Bottle Volume (ml)', 'State Bottle Cost',\n",
" 'State Bottle Retail', 'Bottles Sold', 'Sale (Dollars)',\n",
" 'Volume Sold (Liters)', 'Volume Sold (Gallons)'],\n",
" dtype='object')"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sales.columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cleaning up the column names"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sales.columns = map(str.lower, sales.columns)\n",
"sales.columns = sales.columns.str.replace(r\"[()]\", \"\")\n",
"sales.columns = sales.columns.str.replace(r\"[ ]\", \"_\")\n",
"sales.columns = sales.columns.str.replace(r\"[/]\", \"_\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cleaning the columns and switching them to the proper data type"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"for col in sales.select_dtypes([np.object]):\n",
" sales[col] = sales[col].str.lstrip('$')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sales[\"state_bottle_retail\"] = sales[\"state_bottle_retail\"].astype(float)\n",
"sales[\"sale_dollars\"] = sales[\"sale_dollars\"].astype(float)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Exploring the requirements of the project (Build models of total sales based on location, price per bottle, \n",
"total bottles sold. You may find it useful to build models for each county, ZIP code, or city.) I will explore the following categories: Total Sales, County, Zip Code, City, Retail Price per bottle, Total Bottles Sold."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['date', 'store_number', 'city', 'zip_code', 'county_number', 'county',\n",
" 'category', 'category_name', 'vendor_number', 'item_number',\n",
" 'item_description', 'bottle_volume_ml', 'state_bottle_cost',\n",
" 'state_bottle_retail', 'bottles_sold', 'sale_dollars',\n",
" 'volume_sold_liters', 'volume_sold_gallons'],\n",
" dtype='object')"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sales.columns"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"162.00 3468\n",
"148.56 2536\n",
"64.80 2066\n",
"94.20 2006\n",
"70.56 1889\n",
"90.00 1772\n",
"60.12 1711\n",
"73.80 1626\n",
"62.28 1624\n",
"117.00 1594\n",
"60.72 1556\n",
"188.88 1556\n",
"64.56 1548\n",
"180.00 1516\n",
"135.00 1511\n",
"45.00 1446\n",
"126.00 1433\n",
"30.00 1363\n",
"270.00 1311\n",
"161.64 1281\n",
"99.00 1260\n",
"72.00 1246\n",
"81.00 1228\n",
"124.20 1182\n",
"132.78 1052\n",
"58.50 1051\n",
"22.50 1004\n",
"24.76 992\n",
"67.26 982\n",
"40.50 979\n",
" ... \n",
"139.44 1\n",
"36392.40 1\n",
"90.90 1\n",
"45.70 1\n",
"71.05 1\n",
"4500.00 1\n",
"1398.42 1\n",
"575.82 1\n",
"584.94 1\n",
"314.64 1\n",
"114.06 1\n",
"2052.48 1\n",
"6285.96 1\n",
"291.12 1\n",
"1137.24 1\n",
"967.20 1\n",
"661.50 1\n",
"547.20 1\n",
"327.12 1\n",
"7927.20 1\n",
"230.94 1\n",
"537.60 1\n",
"753.72 1\n",
"7198.50 1\n",
"180.45 1\n",
"8.46 1\n",
"109.04 1\n",
"1079.04 1\n",
"1107.12 1\n",
"127.20 1\n",
"Name: sale_dollars, Length: 6552, dtype: int64"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Counting Total Sales\n",
"sales[\"sale_dollars\"].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"Polk 48944\n",
"Linn 23462\n",
"Scott 16630\n",
"Black Hawk 15030\n",
"Johnson 13163\n",
"Pottawattamie 9088\n",
"Story 8944\n",
"Woodbury 8541\n",
"Dubuque 7739\n",
"Cerro Gordo 6360\n",
"Des Moines 4082\n",
"Muscatine 3975\n",
"Clinton 3569\n",
"Wapello 3522\n",
"Dickinson 3409\n",
"Lee 3319\n",
"Webster 3144\n",
"Marshall 2984\n",
"Jasper 2828\n",
"Buena Vista 2737\n",
"Dallas 2707\n",
"Marion 2601\n",
"Warren 2460\n",
"Bremer 2240\n",
"Boone 2105\n",
"Poweshiek 2087\n",
"Clay 1917\n",
"Carroll 1911\n",
"Jones 1871\n",
"O'Brien 1720\n",
" ... \n",
"Greene 675\n",
"Wright 671\n",
"Shelby 661\n",
"Ida 634\n",
"Howard 606\n",
"Humboldt 588\n",
"Adair 584\n",
"Grundy 566\n",
"Pocahontas 525\n",
"Mills 507\n",
"Louisa 484\n",
"Lucas 475\n",
"Chickasaw 464\n",
"Guthrie 437\n",
"Calhoun 424\n",
"Butler 402\n",
"Worth 387\n",
"Hancock 363\n",
"Monroe 352\n",
"Osceola 351\n",
"Keokuk 343\n",
"Taylor 298\n",
"Van Buren 245\n",
"Adams 234\n",
"Audubon 227\n",
"Decatur 223\n",
"Davis 203\n",
"Ringgold 201\n",
"Wayne 160\n",
"Fremont 27\n",
"Name: county, Length: 99, dtype: int64"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Counting individual Counties\n",
"sales[\"county\"].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"50010 7077\n",
"52402 6938\n",
"52240 6128\n",
"50613 5267\n",
"52001 4755\n",
"51501 4652\n",
"50314 4519\n",
"50317 4425\n",
"50265 4356\n",
"52404 4242\n",
"50401 4119\n",
"52722 3699\n",
"52807 3530\n",
"52405 3502\n",
"52241 3446\n",
"52761 3389\n",
"50311 3384\n",
"51503 3382\n",
"50320 3237\n",
"52501 3206\n",
"50702 3175\n",
"50315 3091\n",
"52804 2973\n",
"50501 2972\n",
"52601 2952\n",
"50703 2885\n",
"50322 2880\n",
"50266 2843\n",
"52732 2822\n",
"50158 2682\n",
" ... \n",
"52625 17\n",
"50514 17\n",
"50044 16\n",
"50541 16\n",
"52623 15\n",
"50542 15\n",
"51053 15\n",
"50261 15\n",
"51466 14\n",
"51002 14\n",
"50071 13\n",
"50251 12\n",
"50830 12\n",
"50150 11\n",
"52223 11\n",
"51005 11\n",
"51038 10\n",
"52337 9\n",
"51553 9\n",
"50452 8\n",
"51338 7\n",
"50162 6\n",
"50540 6\n",
"50061 6\n",
"50634 6\n",
"51535 5\n",
"51453 3\n",
"51530 3\n",
"52801 2\n",
"52328 2\n",
"Name: zip_code, Length: 412, dtype: int64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Counting individual zip codes\n",
"sales[\"zip_code\"].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"DES MOINES 23618\n",
"CEDAR RAPIDS 18736\n",
"DAVENPORT 11469\n",
"WATERLOO 8376\n",
"COUNCIL BLUFFS 8037\n",
"IOWA CITY 7938\n",
"SIOUX CITY 7888\n",
"AMES 7534\n",
"WEST DES MOINES 7148\n",
"DUBUQUE 6854\n",
"CEDAR FALLS 5719\n",
"ANKENY 4823\n",
"MASON CITY 4119\n",
"BETTENDORF 3699\n",
"CORALVILLE 3446\n",
"MUSCATINE 3389\n",
"BURLINGTON 3137\n",
"CLINTON 3077\n",
"FORT DODGE 2972\n",
"WINDSOR HEIGHTS 2797\n",
"MARSHALLTOWN 2682\n",
"NEWTON 2538\n",
"STORM LAKE 2522\n",
"MARION 2485\n",
"URBANDALE 2424\n",
"OTTUMWA 2290\n",
"JOHNSTON 2137\n",
"ALTOONA 2103\n",
"CLEAR LAKE 2080\n",
"SPENCER 1910\n",
" ... \n",
"ARMSTRONG 17\n",
"DONNELLSON 17\n",
"GILMORE CITY 16\n",
"BUSSEY 16\n",
"GOLDFIELD 15\n",
"SCHALLER 15\n",
"DANVILLE 15\n",
"VAN METER 15\n",
"WASHBURN 15\n",
"WALL LAKE 14\n",
"ALTA 14\n",
"DOWS 13\n",
"AFTON 12\n",
"SULLY 12\n",
"AURELIA 11\n",
"LOVILIA 11\n",
"DELHI 11\n",
"MERRILL 10\n",
"STANWOOD 9\n",
"MINDEN 9\n",
"LATIMER 8\n",
"EVERLY 7\n",
"Cumming 6\n",
"MELBOURNE 6\n",
"GILBERTVILLE 6\n",
"FONDA 6\n",
"GRISWOLD 5\n",
"LOHRVILLE 3\n",
"ROBINS 2\n",
"Carroll 1\n",
"Name: city, Length: 382, dtype: int64"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Counting individual Cities\n",
"sales[\"city\"].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"12.38 6110\n",
"9.75 4783\n",
"13.50 4415\n",
"15.00 4222\n",
"22.50 3814\n",
"15.74 3809\n",
"10.76 2991\n",
"11.21 2944\n",
"7.50 2796\n",
"9.45 2777\n",
"10.50 2761\n",
"7.85 2618\n",
"17.24 2498\n",
"10.35 2473\n",
"11.24 2342\n",
"5.01 2334\n",
"11.43 2282\n",
"10.38 2227\n",
"12.30 2162\n",
"13.47 2142\n",
"5.06 2122\n",
"18.75 2116\n",
"16.50 2063\n",
"27.74 2051\n",
"7.13 2024\n",
"10.80 1928\n",
"5.25 1911\n",
"8.25 1869\n",
"27.00 1855\n",
"15.75 1740\n",
" ... \n",
"19.00 1\n",
"8.75 1\n",
"42.11 1\n",
"76.50 1\n",
"109.82 1\n",
"93.03 1\n",
"75.00 1\n",
"12.45 1\n",
"42.75 1\n",
"6.90 1\n",
"57.08 1\n",
"103.41 1\n",
"149.97 1\n",
"16.44 1\n",
"23.70 1\n",
"19.38 1\n",
"286.86 1\n",
"9.27 1\n",
"27.95 1\n",
"22.02 1\n",
"1.98 1\n",
"17.22 1\n",
"20.01 1\n",
"78.70 1\n",
"21.54 1\n",
"111.32 1\n",
"17.36 1\n",
"48.26 1\n",
"180.33 1\n",
"32.33 1\n",
"Name: state_bottle_retail, Length: 1104, dtype: int64"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Counting individual bottle retail price\n",
"sales[\"state_bottle_retail\"].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/plain": [
"12 72607\n",
"6 51887\n",
"2 37321\n",
"1 31058\n",
"3 27758\n",
"4 15051\n",
"24 14798\n",
"48 3765\n",
"5 3189\n",
"36 2115\n",
"18 1734\n",
"10 1383\n",
"8 1227\n",
"60 1098\n",
"30 591\n",
"72 488\n",
"7 397\n",
"120 379\n",
"96 302\n",
"84 200\n",
"9 168\n",
"144 151\n",
"15 117\n",
"180 108\n",
"42 107\n",
"240 107\n",
"300 100\n",
"90 89\n",
"150 86\n",
"44 66\n",
" ... \n",
"1116 1\n",
"157 1\n",
"372 1\n",
"2400 1\n",
"88 1\n",
"354 1\n",
"594 1\n",
"81 1\n",
"615 1\n",
"504 1\n",
"588 1\n",
"75 1\n",
"840 1\n",
"1128 1\n",
"624 1\n",
"57 1\n",
"282 1\n",
"1080 1\n",
"378 1\n",
"564 1\n",
"816 1\n",
"390 1\n",
"396 1\n",
"39 1\n",
"37 1\n",
"97 1\n",
"402 1\n",
"28 1\n",
"1050 1\n",
"33 1\n",
"Name: bottles_sold, Length: 136, dtype: int64"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#Counting individual bottles sold\n",
"sales[\"bottles_sold\"].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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SJKknDICSJEmS1BPLZQbOJDsAjwPuA6zH1JO9VFW9bHmcgyRJkiTpz3UaAJM8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\nAZQkSZKkhvj/dnUPzEinmX0AAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 277,
"width": 448
}
},
"output_type": "display_data"
}
],
"source": [
"#Most sales are under 1,000 dollars\n",
"pd.DataFrame.hist(sales, column='sale_dollars', bins=100)\n",
"plt.xlabel('Sale Dollars')\n",
"plt.ylabel('Number of Sales')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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WY5IkSZKkxaDxANgtMx8BRrsuoCRJkiRpIWr0FlBJkiRJ0uQ1rhHAiNikqQvI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\nGLsTeExVXdDSjwPOBQ5P8syqOnWozEr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"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 277,
"width": 448
}
},
"output_type": "display_data"
}
],
"source": [
"#Most bottles sold are under quantity of 100\n",
"pd.DataFrame.hist(sales, column='bottles_sold', bins=100)\n",
"plt.xlabel('Sale Dollars')\n",
"plt.ylabel('Number of Bottles')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Mine the data\n",
"\n",
"Now you are ready to compute the variables you will use for your regression from the data. For example, you may want to compute total sales per store from Jan to March of 2015, mean price per bottle, etc. Refer to the readme for more ideas appropriate to your scenario.\n",
"\n",
"Pandas is your friend for this task. Take a look at the operations here for ideas on how to make the best use of pandas and feel free to search for blog and Stack Overflow posts to help you group data by certain variables and compute sums, means, etc. You may find it useful to create a new data frame to house this summary data.\n",
" \n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" state_bottle_retail | \n",
" bottles_sold | \n",
" sale_dollars | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" 6.75 | \n",
" 12 | \n",
" 81.00 | \n",
"
\n",
" \n",
" 1 | \n",
" 20.63 | \n",
" 2 | \n",
" 41.26 | \n",
"
\n",
" \n",
" 2 | \n",
" 18.89 | \n",
" 24 | \n",
" 453.36 | \n",
"
\n",
" \n",
" 3 | \n",
" 14.25 | \n",
" 6 | \n",
" 85.50 | \n",
"
\n",
" \n",
" 4 | \n",
" 10.80 | \n",
" 12 | \n",
" 129.60 | \n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" state_bottle_retail bottles_sold sale_dollars\n",
"0 6.75 12 81.00\n",
"1 20.63 2 41.26\n",
"2 18.89 24 453.36\n",
"3 14.25 6 85.50\n",
"4 10.80 12 129.60"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"variables = sales[['state_bottle_retail', 'bottles_sold', 'sale_dollars']]\n",
"variables.head()"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
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},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dummy_counties = pd.get_dummies(sales.county)\n",
"dummy_counties.head()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
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},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dummy_city = pd.get_dummies(sales.city)\n",
"dummy_city.head()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
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"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
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},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dummy_zip = pd.get_dummies(sales.zip_code)\n",
"dummy_zip.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Refine the data\n",
"\n",
"Look for any statistical relationships, correlations, or other relevant properties of the dataset."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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sswV8/PGnldqJCK65+lJ69NiQ0aNfqTHTLxVj1tRZjH32VQbtPZAjTj2i0qZR37jof2jb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\n4pdn+6vq1DH3F+iCCr8FmDd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XtKuHfiVJkqRmJ+vVbGYA29ZxzQBgcsb9SpIkqUSUaha9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\nvY6KZEoymH8OOMTM9sm20cwOA/YGnk6wzCozs9fMLLGhfMz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"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 254,
"width": 377
}
},
"output_type": "display_data"
}
],
"source": [
"sns.heatmap(variables.corr(), annot = True, linewidths = 0.5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There appears to be a correlation between bottles sold and sale dollars"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Build your models\n",
"\n",
"Using scikit-learn or statsmodels, build the necessary models for your scenario. Evaluate model fit."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"I am going to start prepping my data to go into the model. I will need to drop polk county since in the eda I found that it was the largest contributor to counties and since I added dummy variables I need to remove the most represented county then add a constant."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['Adair', 'Adams', 'Allamakee', 'Appanoose', 'Audubon', 'Benton',\n",
" 'Black Hawk', 'Boone', 'Bremer', 'Buchanan', 'Buena Vista', 'Butler',\n",
" 'Calhoun', 'Carroll', 'Cass', 'Cedar', 'Cerro Gordo', 'Cherokee',\n",
" 'Chickasaw', 'Clarke', 'Clay', 'Clayton', 'Clinton', 'Crawford',\n",
" 'Dallas', 'Davis', 'Decatur', 'Delaware', 'Des Moines', 'Dickinson',\n",
" 'Dubuque', 'Emmet', 'Fayette', 'Floyd', 'Franklin', 'Fremont', 'Greene',\n",
" 'Grundy', 'Guthrie', 'Hamilton', 'Hancock', 'Hardin', 'Harrison',\n",
" 'Henry', 'Howard', 'Humboldt', 'Ida', 'Iowa', 'Jackson', 'Jasper',\n",
" 'Jefferson', 'Johnson', 'Jones', 'Keokuk', 'Kossuth', 'Lee', 'Linn',\n",
" 'Louisa', 'Lucas', 'Lyon', 'Madison', 'Mahaska', 'Marion', 'Marshall',\n",
" 'Mills', 'Mitchell', 'Monona', 'Monroe', 'Montgomery', 'Muscatine',\n",
" 'O'Brien', 'Osceola', 'Page', 'Palo Alto', 'Plymouth', 'Pocahontas',\n",
" 'Polk', 'Pottawattamie', 'Poweshiek', 'Ringgold', 'Sac', 'Scott',\n",
" 'Shelby', 'Sioux', 'Story', 'Tama', 'Taylor', 'Union', 'Van Buren',\n",
" 'Wapello', 'Warren', 'Washington', 'Wayne', 'Webster', 'Winnebago',\n",
" 'Winneshiek', 'Woodbury', 'Worth', 'Wright'],\n",
" dtype='object')"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dummy_counties.columns"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dummy_counties = dummy_counties.drop('Polk', axis=1)\n",
"variables = variables.drop('sale_dollars', axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" state_bottle_retail | \n",
" bottles_sold | \n",
" Adair | \n",
" Adams | \n",
" Allamakee | \n",
" Appanoose | \n",
" Audubon | \n",
" Benton | \n",
" Black Hawk | \n",
" Boone | \n",
" ... | \n",
" Wapello | \n",
" Warren | \n",
" Washington | \n",
" Wayne | \n",
" Webster | \n",
" Winnebago | \n",
" Winneshiek | \n",
" Woodbury | \n",
" Worth | \n",
" Wright | \n",
"
\n",
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" 0 | \n",
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" 0 | \n",
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" 0 | \n",
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\n",
" \n",
" 3 | \n",
" 14.25 | \n",
" 6 | \n",
" 0 | \n",
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" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" ... | \n",
" 0 | \n",
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" 0 | \n",
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" 0 | \n",
" 0 | \n",
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\n",
" \n",
" 4 | \n",
" 10.80 | \n",
" 12 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" 0 | \n",
" ... | \n",
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5 rows × 100 columns
\n",
"
"
],
"text/plain": [
" state_bottle_retail bottles_sold Adair Adams Allamakee Appanoose \\\n",
"0 6.75 12 0 0 0 0 \n",
"1 20.63 2 0 0 0 0 \n",
"2 18.89 24 0 0 0 0 \n",
"3 14.25 6 0 0 0 0 \n",
"4 10.80 12 0 0 0 0 \n",
"\n",
" Audubon Benton Black Hawk Boone ... Wapello Warren Washington \\\n",
"0 0 0 0 0 ... 0 0 0 \n",
"1 0 0 0 0 ... 0 0 0 \n",
"2 0 0 1 0 ... 0 0 0 \n",
"3 0 0 0 0 ... 0 0 0 \n",
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"\n",
" Wayne Webster Winnebago Winneshiek Woodbury Worth Wright \n",
"0 0 0 0 0 0 0 0 \n",
"1 0 0 0 0 0 0 0 \n",
"2 0 0 0 0 0 0 0 \n",
"3 0 0 0 0 0 0 0 \n",
"4 0 0 0 0 0 0 1 \n",
"\n",
"[5 rows x 100 columns]"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sales_county = pd.concat([variables, dummy_counties], axis=1)\n",
"sales_county.head()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dummy_city = dummy_city.drop('DES MOINES', axis=1)\n",
"dummy_zip = dummy_zip.drop('50010', axis=1)\n"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
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" state_bottle_retail bottles_sold ACKLEY ADAIR ADEL AFTON AKRON \\\n",
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"[5 rows x 383 columns]"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sales_city = pd.concat([variables, dummy_city], axis=1)\n",
"sales_city.head()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"collapsed": true
},
"outputs": [
{
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},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sales_zip = pd.concat([variables, dummy_zip], axis=1)\n",
"sales_zip.head()"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"y=sales[\"sale_dollars\"]\n",
"sales_county = sm.add_constant(sales_county)\n",
"X_county = sales_county\n",
"sales_city = sm.add_constant(sales_city)\n",
"X_city = sales_city\n",
"sales_zip = sm.add_constant(sales_zip)\n",
"X_zip = sales_zip"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [],
"source": [
"# Note the argument order\n",
"model_county = sm.OLS(y, X_county).fit() ## sm.OLS(output, input)\n",
"predictions_county = model_county.predict(X_county)"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"# Note the argument order\n",
"model_city = sm.OLS(y, X_city).fit() ## sm.OLS(output, input)\n",
"predictions_city = model_city.predict(X_city)"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
"# Note the argument order\n",
"model_zip = sm.OLS(y, X_zip).fit() ## sm.OLS(output, input)\n",
"predictions_zip = model_zip.predict(X_zip)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"OLS Regression Results\n",
"\n",
" Dep. Variable: | sale_dollars | R-squared: | 0.718 | \n",
"
\n",
"\n",
" Model: | OLS | Adj. R-squared: | 0.718 | \n",
"
\n",
"\n",
" Method: | Least Squares | F-statistic: | 6854. | \n",
"
\n",
"\n",
" Date: | Sun, 22 Oct 2017 | Prob (F-statistic): | 0.00 | \n",
"
\n",
"\n",
" Time: | 22:31:25 | Log-Likelihood: | -1.8135e+06 | \n",
"
\n",
"\n",
" No. Observations: | 269258 | AIC: | 3.627e+06 | \n",
"
\n",
"\n",
" Df Residuals: | 269157 | BIC: | 3.628e+06 | \n",
"
\n",
"\n",
" Df Model: | 100 | | | \n",
"
\n",
"\n",
" Covariance Type: | nonrobust | | | \n",
"
\n",
"
\n",
"\n",
"\n",
" | coef | std err | t | P>|t| | [0.025 | 0.975] | \n",
"
\n",
"\n",
" const | -103.7964 | 1.105 | -93.925 | 0.000 | -105.962 | -101.630 | \n",
"
\n",
"\n",
" state_bottle_retail | 6.8393 | 0.037 | 183.278 | 0.000 | 6.766 | 6.912 | \n",
"
\n",
"\n",
" bottles_sold | 13.3495 | 0.016 | 813.993 | 0.000 | 13.317 | 13.382 | \n",
"
\n",
"\n",
" Adair | -3.0402 | 8.478 | -0.359 | 0.720 | -19.657 | 13.577 | \n",
"
\n",
"\n",
" Adams | -1.8369 | 13.347 | -0.138 | 0.891 | -27.996 | 24.322 | \n",
"
\n",
"\n",
" Allamakee | 0.9446 | 6.371 | 0.148 | 0.882 | -11.542 | 13.431 | \n",
"
\n",
"\n",
" Appanoose | -4.2531 | 6.383 | -0.666 | 0.505 | -16.763 | 8.257 | \n",
"
\n",
"\n",
" Audubon | 1.0510 | 13.550 | 0.078 | 0.938 | -25.506 | 27.608 | \n",
"
\n",
"\n",
" Benton | 1.6322 | 6.578 | 0.248 | 0.804 | -11.261 | 14.525 | \n",
"
\n",
"\n",
" Black Hawk | -17.3436 | 1.900 | -9.129 | 0.000 | -21.067 | -13.620 | \n",
"
\n",
"\n",
" Boone | 5.4154 | 4.534 | 1.194 | 0.232 | -3.472 | 14.302 | \n",
"
\n",
"\n",
" Bremer | 3.3418 | 4.401 | 0.759 | 0.448 | -5.285 | 11.969 | \n",
"
\n",
"\n",
" Buchanan | 1.5694 | 5.172 | 0.303 | 0.762 | -8.567 | 11.706 | \n",
"
\n",
"\n",
" Buena Vista | -2.2199 | 4.002 | -0.555 | 0.579 | -10.063 | 5.623 | \n",
"
\n",
"\n",
" Butler | 4.1962 | 10.200 | 0.411 | 0.681 | -15.796 | 24.188 | \n",
"
\n",
"\n",
" Calhoun | 8.1722 | 9.935 | 0.823 | 0.411 | -11.299 | 27.644 | \n",
"
\n",
"\n",
" Carroll | 18.1781 | 4.749 | 3.828 | 0.000 | 8.870 | 27.486 | \n",
"
\n",
"\n",
" Cass | 2.6457 | 5.976 | 0.443 | 0.658 | -9.066 | 14.358 | \n",
"
\n",
"\n",
" Cedar | 7.5866 | 6.345 | 1.196 | 0.232 | -4.850 | 20.023 | \n",
"
\n",
"\n",
" Cerro Gordo | -4.8350 | 2.715 | -1.781 | 0.075 | -10.157 | 0.487 | \n",
"
\n",
"\n",
" Cherokee | -12.0638 | 6.939 | -1.739 | 0.082 | -25.664 | 1.536 | \n",
"
\n",
"\n",
" Chickasaw | 7.7258 | 9.500 | 0.813 | 0.416 | -10.894 | 26.346 | \n",
"
\n",
"\n",
" Clarke | 12.8217 | 7.786 | 1.647 | 0.100 | -2.439 | 28.082 | \n",
"
\n",
"\n",
" Clay | 1.3912 | 4.743 | 0.293 | 0.769 | -7.904 | 10.687 | \n",
"
\n",
"\n",
" Clayton | 0.9740 | 5.812 | 0.168 | 0.867 | -10.418 | 12.366 | \n",
"
\n",
"\n",
" Clinton | -0.7251 | 3.532 | -0.205 | 0.837 | -7.648 | 6.198 | \n",
"
\n",
"\n",
" Crawford | 3.9955 | 6.160 | 0.649 | 0.517 | -8.079 | 16.070 | \n",
"
\n",
"\n",
" Dallas | 53.1908 | 4.021 | 13.227 | 0.000 | 45.309 | 61.073 | \n",
"
\n",
"\n",
" Davis | 4.6525 | 14.325 | 0.325 | 0.745 | -23.424 | 32.729 | \n",
"
\n",
"\n",
" Decatur | -2.2049 | 13.670 | -0.161 | 0.872 | -28.998 | 24.588 | \n",
"
\n",
"\n",
" Delaware | 3.1519 | 7.758 | 0.406 | 0.685 | -12.054 | 18.358 | \n",
"
\n",
"\n",
" Des Moines | -5.8023 | 3.318 | -1.749 | 0.080 | -12.306 | 0.702 | \n",
"
\n",
"\n",
" Dickinson | 5.0831 | 3.608 | 1.409 | 0.159 | -1.988 | 12.154 | \n",
"
\n",
"\n",
" Dubuque | 2.6042 | 2.492 | 1.045 | 0.296 | -2.279 | 7.488 | \n",
"
\n",
"\n",
" Emmet | -0.1118 | 7.309 | -0.015 | 0.988 | -14.438 | 14.215 | \n",
"
\n",
"\n",
" Fayette | 2.7125 | 6.097 | 0.445 | 0.656 | -9.237 | 14.662 | \n",
"
\n",
"\n",
" Floyd | 1.2147 | 6.506 | 0.187 | 0.852 | -11.537 | 13.966 | \n",
"
\n",
"\n",
" Franklin | -3.3577 | 7.732 | -0.434 | 0.664 | -18.511 | 11.796 | \n",
"
\n",
"\n",
" Fremont | -30.1678 | 39.207 | -0.769 | 0.442 | -107.013 | 46.677 | \n",
"
\n",
"\n",
" Greene | 2.6740 | 7.893 | 0.339 | 0.735 | -12.797 | 18.145 | \n",
"
\n",
"\n",
" Grundy | 4.1345 | 8.611 | 0.480 | 0.631 | -12.743 | 21.012 | \n",
"
\n",
"\n",
" Guthrie | 0.1955 | 9.787 | 0.020 | 0.984 | -18.986 | 19.377 | \n",
"
\n",
"\n",
" Hamilton | 3.8102 | 6.121 | 0.622 | 0.534 | -8.187 | 15.808 | \n",
"
\n",
"\n",
" Hancock | -8.2983 | 10.730 | -0.773 | 0.439 | -29.328 | 12.732 | \n",
"
\n",
"\n",
" Hardin | 8.7106 | 5.090 | 1.711 | 0.087 | -1.267 | 18.688 | \n",
"
\n",
"\n",
" Harrison | 4.3795 | 6.214 | 0.705 | 0.481 | -7.799 | 16.558 | \n",
"
\n",
"\n",
" Henry | 8.5531 | 6.210 | 1.377 | 0.168 | -3.618 | 20.724 | \n",
"
\n",
"\n",
" Howard | 12.8337 | 8.325 | 1.542 | 0.123 | -3.482 | 29.150 | \n",
"
\n",
"\n",
" Humboldt | -1.5956 | 8.450 | -0.189 | 0.850 | -18.157 | 14.966 | \n",
"
\n",
"\n",
" Ida | 5.6097 | 8.141 | 0.689 | 0.491 | -10.347 | 21.566 | \n",
"
\n",
"\n",
" Iowa | 13.5064 | 5.634 | 2.397 | 0.017 | 2.464 | 24.549 | \n",
"
\n",
"\n",
" Jackson | 4.9213 | 5.199 | 0.947 | 0.344 | -5.268 | 15.111 | \n",
"
\n",
"\n",
" Jasper | 1.2888 | 3.940 | 0.327 | 0.744 | -6.434 | 9.012 | \n",
"
\n",
"\n",
" Jefferson | -5.1507 | 7.229 | -0.713 | 0.476 | -19.318 | 9.017 | \n",
"
\n",
"\n",
" Johnson | 6.7503 | 2.000 | 3.376 | 0.001 | 2.831 | 10.670 | \n",
"
\n",
"\n",
" Jones | 7.3444 | 4.799 | 1.530 | 0.126 | -2.062 | 16.751 | \n",
"
\n",
"\n",
" Keokuk | 5.7851 | 11.037 | 0.524 | 0.600 | -15.846 | 27.416 | \n",
"
\n",
"\n",
" Kossuth | 2.0699 | 5.061 | 0.409 | 0.683 | -7.850 | 11.989 | \n",
"
\n",
"\n",
" Lee | 5.3702 | 3.653 | 1.470 | 0.142 | -1.790 | 12.530 | \n",
"
\n",
"\n",
" Linn | 0.4465 | 1.618 | 0.276 | 0.783 | -2.724 | 3.617 | \n",
"
\n",
"\n",
" Louisa | -36.1366 | 9.304 | -3.884 | 0.000 | -54.372 | -17.901 | \n",
"
\n",
"\n",
" Lucas | 5.4799 | 9.390 | 0.584 | 0.560 | -12.925 | 23.885 | \n",
"
\n",
"\n",
" Lyon | 0.9456 | 6.028 | 0.157 | 0.875 | -10.870 | 12.761 | \n",
"
\n",
"\n",
" Madison | -1.6523 | 6.774 | -0.244 | 0.807 | -14.930 | 11.625 | \n",
"
\n",
"\n",
" Mahaska | -4.9971 | 5.981 | -0.836 | 0.403 | -16.719 | 6.725 | \n",
"
\n",
"\n",
" Marion | 5.0274 | 4.099 | 1.226 | 0.220 | -3.007 | 13.062 | \n",
"
\n",
"\n",
" Marshall | 1.8498 | 3.841 | 0.482 | 0.630 | -5.678 | 9.378 | \n",
"
\n",
"\n",
" Mills | -20.2372 | 9.092 | -2.226 | 0.026 | -38.057 | -2.417 | \n",
"
\n",
"\n",
" Mitchell | 0.5647 | 6.476 | 0.087 | 0.931 | -12.127 | 13.257 | \n",
"
\n",
"\n",
" Monona | -0.3446 | 5.824 | -0.059 | 0.953 | -11.759 | 11.070 | \n",
"
\n",
"\n",
" Monroe | 5.6772 | 10.895 | 0.521 | 0.602 | -15.676 | 27.031 | \n",
"
\n",
"\n",
" Montgomery | -4.7861 | 7.189 | -0.666 | 0.506 | -18.877 | 9.305 | \n",
"
\n",
"\n",
" Muscatine | 3.4120 | 3.360 | 1.015 | 0.310 | -3.174 | 9.998 | \n",
"
\n",
"\n",
" O'Brien | 8.7803 | 4.997 | 1.757 | 0.079 | -1.014 | 18.575 | \n",
"
\n",
"\n",
" Osceola | 2.5360 | 10.910 | 0.232 | 0.816 | -18.848 | 23.920 | \n",
"
\n",
"\n",
" Page | 5.1122 | 5.998 | 0.852 | 0.394 | -6.644 | 16.868 | \n",
"
\n",
"\n",
" Palo Alto | 0.0944 | 6.258 | 0.015 | 0.988 | -12.171 | 12.360 | \n",
"
\n",
"\n",
" Plymouth | 3.0856 | 5.232 | 0.590 | 0.555 | -7.169 | 13.340 | \n",
"
\n",
"\n",
" Pocahontas | 4.5471 | 8.937 | 0.509 | 0.611 | -12.969 | 22.063 | \n",
"
\n",
"\n",
" Pottawattamie | 7.6726 | 2.327 | 3.297 | 0.001 | 3.112 | 12.233 | \n",
"
\n",
"\n",
" Poweshiek | 4.0197 | 4.553 | 0.883 | 0.377 | -4.905 | 12.944 | \n",
"
\n",
"\n",
" Ringgold | 12.4488 | 14.395 | 0.865 | 0.387 | -15.766 | 40.663 | \n",
"
\n",
"\n",
" Sac | -4.5672 | 6.874 | -0.664 | 0.506 | -18.041 | 8.906 | \n",
"
\n",
"\n",
" Scott | -11.6747 | 1.828 | -6.386 | 0.000 | -15.258 | -8.091 | \n",
"
\n",
"\n",
" Shelby | 11.7838 | 7.975 | 1.478 | 0.140 | -3.847 | 27.415 | \n",
"
\n",
"\n",
" Sioux | 18.1440 | 5.664 | 3.203 | 0.001 | 7.042 | 29.246 | \n",
"
\n",
"\n",
" Story | 3.9777 | 2.343 | 1.698 | 0.090 | -0.614 | 8.569 | \n",
"
\n",
"\n",
" Tama | 0.9065 | 6.562 | 0.138 | 0.890 | -11.955 | 13.768 | \n",
"
\n",
"\n",
" Taylor | 3.8606 | 11.835 | 0.326 | 0.744 | -19.335 | 27.057 | \n",
"
\n",
"\n",
" Union | 5.7824 | 6.286 | 0.920 | 0.358 | -6.538 | 18.102 | \n",
"
\n",
"\n",
" Van Buren | 10.2299 | 13.045 | 0.784 | 0.433 | -15.338 | 35.797 | \n",
"
\n",
"\n",
" Wapello | 9.0755 | 3.555 | 2.553 | 0.011 | 2.108 | 16.043 | \n",
"
\n",
"\n",
" Warren | -0.0940 | 4.209 | -0.022 | 0.982 | -8.343 | 8.155 | \n",
"
\n",
"\n",
" Washington | -2.8096 | 5.265 | -0.534 | 0.594 | -13.128 | 7.509 | \n",
"
\n",
"\n",
" Wayne | -8.7035 | 16.128 | -0.540 | 0.589 | -40.314 | 22.907 | \n",
"
\n",
"\n",
" Webster | -5.2554 | 3.747 | -1.402 | 0.161 | -12.600 | 2.089 | \n",
"
\n",
"\n",
" Winnebago | -3.8053 | 6.669 | -0.571 | 0.568 | -16.876 | 9.265 | \n",
"
\n",
"\n",
" Winneshiek | -1.1398 | 5.713 | -0.200 | 0.842 | -12.337 | 10.057 | \n",
"
\n",
"\n",
" Woodbury | 0.3815 | 2.388 | 0.160 | 0.873 | -4.300 | 5.063 | \n",
"
\n",
"\n",
" Worth | 8.8589 | 10.394 | 0.852 | 0.394 | -11.514 | 29.231 | \n",
"
\n",
"\n",
" Wright | -0.8212 | 7.916 | -0.104 | 0.917 | -16.337 | 14.695 | \n",
"
\n",
"
\n",
"\n",
"\n",
" Omnibus: | 366597.951 | Durbin-Watson: | 2.001 | \n",
"
\n",
"\n",
" Prob(Omnibus): | 0.000 | Jarque-Bera (JB): | 66620643079.357 | \n",
"
\n",
"\n",
" Skew: | 5.938 | Prob(JB): | 0.00 | \n",
"
\n",
"\n",
" Kurtosis: | 2439.804 | Cond. No. | 2.68e+03 | \n",
"
\n",
"
"
],
"text/plain": [
"\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: sale_dollars R-squared: 0.718\n",
"Model: OLS Adj. R-squared: 0.718\n",
"Method: Least Squares F-statistic: 6854.\n",
"Date: Sun, 22 Oct 2017 Prob (F-statistic): 0.00\n",
"Time: 22:31:25 Log-Likelihood: -1.8135e+06\n",
"No. Observations: 269258 AIC: 3.627e+06\n",
"Df Residuals: 269157 BIC: 3.628e+06\n",
"Df Model: 100 \n",
"Covariance Type: nonrobust \n",
"=======================================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"---------------------------------------------------------------------------------------\n",
"const -103.7964 1.105 -93.925 0.000 -105.962 -101.630\n",
"state_bottle_retail 6.8393 0.037 183.278 0.000 6.766 6.912\n",
"bottles_sold 13.3495 0.016 813.993 0.000 13.317 13.382\n",
"Adair -3.0402 8.478 -0.359 0.720 -19.657 13.577\n",
"Adams -1.8369 13.347 -0.138 0.891 -27.996 24.322\n",
"Allamakee 0.9446 6.371 0.148 0.882 -11.542 13.431\n",
"Appanoose -4.2531 6.383 -0.666 0.505 -16.763 8.257\n",
"Audubon 1.0510 13.550 0.078 0.938 -25.506 27.608\n",
"Benton 1.6322 6.578 0.248 0.804 -11.261 14.525\n",
"Black Hawk -17.3436 1.900 -9.129 0.000 -21.067 -13.620\n",
"Boone 5.4154 4.534 1.194 0.232 -3.472 14.302\n",
"Bremer 3.3418 4.401 0.759 0.448 -5.285 11.969\n",
"Buchanan 1.5694 5.172 0.303 0.762 -8.567 11.706\n",
"Buena Vista -2.2199 4.002 -0.555 0.579 -10.063 5.623\n",
"Butler 4.1962 10.200 0.411 0.681 -15.796 24.188\n",
"Calhoun 8.1722 9.935 0.823 0.411 -11.299 27.644\n",
"Carroll 18.1781 4.749 3.828 0.000 8.870 27.486\n",
"Cass 2.6457 5.976 0.443 0.658 -9.066 14.358\n",
"Cedar 7.5866 6.345 1.196 0.232 -4.850 20.023\n",
"Cerro Gordo -4.8350 2.715 -1.781 0.075 -10.157 0.487\n",
"Cherokee -12.0638 6.939 -1.739 0.082 -25.664 1.536\n",
"Chickasaw 7.7258 9.500 0.813 0.416 -10.894 26.346\n",
"Clarke 12.8217 7.786 1.647 0.100 -2.439 28.082\n",
"Clay 1.3912 4.743 0.293 0.769 -7.904 10.687\n",
"Clayton 0.9740 5.812 0.168 0.867 -10.418 12.366\n",
"Clinton -0.7251 3.532 -0.205 0.837 -7.648 6.198\n",
"Crawford 3.9955 6.160 0.649 0.517 -8.079 16.070\n",
"Dallas 53.1908 4.021 13.227 0.000 45.309 61.073\n",
"Davis 4.6525 14.325 0.325 0.745 -23.424 32.729\n",
"Decatur -2.2049 13.670 -0.161 0.872 -28.998 24.588\n",
"Delaware 3.1519 7.758 0.406 0.685 -12.054 18.358\n",
"Des Moines -5.8023 3.318 -1.749 0.080 -12.306 0.702\n",
"Dickinson 5.0831 3.608 1.409 0.159 -1.988 12.154\n",
"Dubuque 2.6042 2.492 1.045 0.296 -2.279 7.488\n",
"Emmet -0.1118 7.309 -0.015 0.988 -14.438 14.215\n",
"Fayette 2.7125 6.097 0.445 0.656 -9.237 14.662\n",
"Floyd 1.2147 6.506 0.187 0.852 -11.537 13.966\n",
"Franklin -3.3577 7.732 -0.434 0.664 -18.511 11.796\n",
"Fremont -30.1678 39.207 -0.769 0.442 -107.013 46.677\n",
"Greene 2.6740 7.893 0.339 0.735 -12.797 18.145\n",
"Grundy 4.1345 8.611 0.480 0.631 -12.743 21.012\n",
"Guthrie 0.1955 9.787 0.020 0.984 -18.986 19.377\n",
"Hamilton 3.8102 6.121 0.622 0.534 -8.187 15.808\n",
"Hancock -8.2983 10.730 -0.773 0.439 -29.328 12.732\n",
"Hardin 8.7106 5.090 1.711 0.087 -1.267 18.688\n",
"Harrison 4.3795 6.214 0.705 0.481 -7.799 16.558\n",
"Henry 8.5531 6.210 1.377 0.168 -3.618 20.724\n",
"Howard 12.8337 8.325 1.542 0.123 -3.482 29.150\n",
"Humboldt -1.5956 8.450 -0.189 0.850 -18.157 14.966\n",
"Ida 5.6097 8.141 0.689 0.491 -10.347 21.566\n",
"Iowa 13.5064 5.634 2.397 0.017 2.464 24.549\n",
"Jackson 4.9213 5.199 0.947 0.344 -5.268 15.111\n",
"Jasper 1.2888 3.940 0.327 0.744 -6.434 9.012\n",
"Jefferson -5.1507 7.229 -0.713 0.476 -19.318 9.017\n",
"Johnson 6.7503 2.000 3.376 0.001 2.831 10.670\n",
"Jones 7.3444 4.799 1.530 0.126 -2.062 16.751\n",
"Keokuk 5.7851 11.037 0.524 0.600 -15.846 27.416\n",
"Kossuth 2.0699 5.061 0.409 0.683 -7.850 11.989\n",
"Lee 5.3702 3.653 1.470 0.142 -1.790 12.530\n",
"Linn 0.4465 1.618 0.276 0.783 -2.724 3.617\n",
"Louisa -36.1366 9.304 -3.884 0.000 -54.372 -17.901\n",
"Lucas 5.4799 9.390 0.584 0.560 -12.925 23.885\n",
"Lyon 0.9456 6.028 0.157 0.875 -10.870 12.761\n",
"Madison -1.6523 6.774 -0.244 0.807 -14.930 11.625\n",
"Mahaska -4.9971 5.981 -0.836 0.403 -16.719 6.725\n",
"Marion 5.0274 4.099 1.226 0.220 -3.007 13.062\n",
"Marshall 1.8498 3.841 0.482 0.630 -5.678 9.378\n",
"Mills -20.2372 9.092 -2.226 0.026 -38.057 -2.417\n",
"Mitchell 0.5647 6.476 0.087 0.931 -12.127 13.257\n",
"Monona -0.3446 5.824 -0.059 0.953 -11.759 11.070\n",
"Monroe 5.6772 10.895 0.521 0.602 -15.676 27.031\n",
"Montgomery -4.7861 7.189 -0.666 0.506 -18.877 9.305\n",
"Muscatine 3.4120 3.360 1.015 0.310 -3.174 9.998\n",
"O'Brien 8.7803 4.997 1.757 0.079 -1.014 18.575\n",
"Osceola 2.5360 10.910 0.232 0.816 -18.848 23.920\n",
"Page 5.1122 5.998 0.852 0.394 -6.644 16.868\n",
"Palo Alto 0.0944 6.258 0.015 0.988 -12.171 12.360\n",
"Plymouth 3.0856 5.232 0.590 0.555 -7.169 13.340\n",
"Pocahontas 4.5471 8.937 0.509 0.611 -12.969 22.063\n",
"Pottawattamie 7.6726 2.327 3.297 0.001 3.112 12.233\n",
"Poweshiek 4.0197 4.553 0.883 0.377 -4.905 12.944\n",
"Ringgold 12.4488 14.395 0.865 0.387 -15.766 40.663\n",
"Sac -4.5672 6.874 -0.664 0.506 -18.041 8.906\n",
"Scott -11.6747 1.828 -6.386 0.000 -15.258 -8.091\n",
"Shelby 11.7838 7.975 1.478 0.140 -3.847 27.415\n",
"Sioux 18.1440 5.664 3.203 0.001 7.042 29.246\n",
"Story 3.9777 2.343 1.698 0.090 -0.614 8.569\n",
"Tama 0.9065 6.562 0.138 0.890 -11.955 13.768\n",
"Taylor 3.8606 11.835 0.326 0.744 -19.335 27.057\n",
"Union 5.7824 6.286 0.920 0.358 -6.538 18.102\n",
"Van Buren 10.2299 13.045 0.784 0.433 -15.338 35.797\n",
"Wapello 9.0755 3.555 2.553 0.011 2.108 16.043\n",
"Warren -0.0940 4.209 -0.022 0.982 -8.343 8.155\n",
"Washington -2.8096 5.265 -0.534 0.594 -13.128 7.509\n",
"Wayne -8.7035 16.128 -0.540 0.589 -40.314 22.907\n",
"Webster -5.2554 3.747 -1.402 0.161 -12.600 2.089\n",
"Winnebago -3.8053 6.669 -0.571 0.568 -16.876 9.265\n",
"Winneshiek -1.1398 5.713 -0.200 0.842 -12.337 10.057\n",
"Woodbury 0.3815 2.388 0.160 0.873 -4.300 5.063\n",
"Worth 8.8589 10.394 0.852 0.394 -11.514 29.231\n",
"Wright -0.8212 7.916 -0.104 0.917 -16.337 14.695\n",
"==============================================================================\n",
"Omnibus: 366597.951 Durbin-Watson: 2.001\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 66620643079.357\n",
"Skew: 5.938 Prob(JB): 0.00\n",
"Kurtosis: 2439.804 Cond. No. 2.68e+03\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"[2] The condition number is large, 2.68e+03. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n",
"\"\"\""
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# County statistics\n",
"model_county.summary()"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"OLS Regression Results\n",
"\n",
" Dep. Variable: | sale_dollars | R-squared: | 0.718 | \n",
"
\n",
"\n",
" Model: | OLS | Adj. R-squared: | 0.718 | \n",
"
\n",
"\n",
" Method: | Least Squares | F-statistic: | 1791. | \n",
"
\n",
"\n",
" Date: | Sun, 22 Oct 2017 | Prob (F-statistic): | 0.00 | \n",
"
\n",
"\n",
" Time: | 22:32:18 | Log-Likelihood: | -1.8133e+06 | \n",
"
\n",
"\n",
" No. Observations: | 269258 | AIC: | 3.627e+06 | \n",
"
\n",
"\n",
" Df Residuals: | 268874 | BIC: | 3.631e+06 | \n",
"
\n",
"\n",
" Df Model: | 383 | | | \n",
"
\n",
"\n",
" Covariance Type: | nonrobust | | | \n",
"
\n",
"
\n",
"\n",
"\n",
" | coef | std err | t | P>|t| | [0.025 | 0.975] | \n",
"
\n",
"\n",
" const | -111.2714 | 1.465 | -75.970 | 0.000 | -114.142 | -108.401 | \n",
"
\n",
"\n",
" state_bottle_retail | 6.8100 | 0.037 | 181.877 | 0.000 | 6.737 | 6.883 | \n",
"
\n",
"\n",
" bottles_sold | 13.3641 | 0.016 | 812.038 | 0.000 | 13.332 | 13.396 | \n",
"
\n",
"\n",
" ACKLEY | 12.6694 | 21.388 | 0.592 | 0.554 | -29.250 | 54.589 | \n",
"
\n",
"\n",
" ADAIR | -16.2146 | 31.082 | -0.522 | 0.602 | -77.134 | 44.705 | \n",
"
\n",
"\n",
" ADEL | 21.9562 | 11.496 | 1.910 | 0.056 | -0.576 | 44.488 | \n",
"
\n",
"\n",
" AFTON | 9.7649 | 58.798 | 0.166 | 0.868 | -105.478 | 125.007 | \n",
"
\n",
"\n",
" AKRON | 7.8456 | 24.550 | 0.320 | 0.749 | -40.273 | 55.964 | \n",
"
\n",
"\n",
" ALBIA | 13.9177 | 11.107 | 1.253 | 0.210 | -7.852 | 35.688 | \n",
"
\n",
"\n",
" ALDEN | 4.5391 | 18.484 | 0.246 | 0.806 | -31.689 | 40.768 | \n",
"
\n",
"\n",
" ALGONA | 10.4988 | 6.497 | 1.616 | 0.106 | -2.236 | 23.233 | \n",
"
\n",
"\n",
" ALLISON | 16.4433 | 22.391 | 0.734 | 0.463 | -27.443 | 60.329 | \n",
"
\n",
"\n",
" ALTA | -16.2747 | 54.439 | -0.299 | 0.765 | -122.973 | 90.424 | \n",
"
\n",
"\n",
" ALTOONA | 9.2230 | 4.634 | 1.990 | 0.047 | 0.140 | 18.306 | \n",
"
\n",
"\n",
" AMES | 12.1338 | 2.696 | 4.501 | 0.000 | 6.850 | 17.418 | \n",
"
\n",
"\n",
" ANAMOSA | 16.3067 | 8.538 | 1.910 | 0.056 | -0.427 | 33.040 | \n",
"
\n",
"\n",
" ANITA | 10.1520 | 16.791 | 0.605 | 0.545 | -22.759 | 43.063 | \n",
"
\n",
"\n",
" ANKENY | 17.5795 | 3.219 | 5.461 | 0.000 | 11.270 | 23.889 | \n",
"
\n",
"\n",
" ANTHON | 13.1279 | 15.957 | 0.823 | 0.411 | -18.147 | 44.403 | \n",
"
\n",
"\n",
" ARLINGTON | 28.7329 | 23.397 | 1.228 | 0.219 | -17.125 | 74.591 | \n",
"
\n",
"\n",
" ARMSTRONG | -28.7348 | 49.406 | -0.582 | 0.561 | -125.568 | 68.099 | \n",
"
\n",
"\n",
" ARNOLD'S PARK | 1.3154 | 13.158 | 0.100 | 0.920 | -24.475 | 27.106 | \n",
"
\n",
"\n",
" ARNOLDS PARK | 11.8707 | 9.231 | 1.286 | 0.198 | -6.221 | 29.963 | \n",
"
\n",
"\n",
" ATLANTIC | 10.5251 | 6.462 | 1.629 | 0.103 | -2.139 | 23.190 | \n",
"
\n",
"\n",
" AUDUBON | 12.3132 | 14.284 | 0.862 | 0.389 | -15.684 | 40.310 | \n",
"
\n",
"\n",
" AURELIA | -6.9803 | 61.411 | -0.114 | 0.910 | -127.344 | 113.384 | \n",
"
\n",
"\n",
" AVOCA | 11.3464 | 9.618 | 1.180 | 0.238 | -7.505 | 30.197 | \n",
"
\n",
"\n",
" BALDWIN | -19.7364 | 39.957 | -0.494 | 0.621 | -98.052 | 58.579 | \n",
"
\n",
"\n",
" BANCROFT | 8.9044 | 10.655 | 0.836 | 0.403 | -11.980 | 29.789 | \n",
"
\n",
"\n",
" BAXTER | 20.9895 | 26.772 | 0.784 | 0.433 | -31.482 | 73.461 | \n",
"
\n",
"\n",
" BEDFORD | 17.5580 | 16.306 | 1.077 | 0.282 | -14.402 | 49.518 | \n",
"
\n",
"\n",
" BELLE PLAINE | 4.5213 | 10.096 | 0.448 | 0.654 | -15.267 | 24.309 | \n",
"
\n",
"\n",
" BELLEVUE | 7.7428 | 13.266 | 0.584 | 0.559 | -18.258 | 33.744 | \n",
"
\n",
"\n",
" BELMOND | 0.3506 | 15.194 | 0.023 | 0.982 | -29.429 | 30.130 | \n",
"
\n",
"\n",
" BETTENDORF | 16.6460 | 3.602 | 4.621 | 0.000 | 9.586 | 23.706 | \n",
"
\n",
"\n",
" BEVINGTON | 14.0133 | 23.709 | 0.591 | 0.554 | -32.456 | 60.483 | \n",
"
\n",
"\n",
" BLOOMFIELD | 12.4578 | 14.354 | 0.868 | 0.385 | -15.676 | 40.592 | \n",
"
\n",
"\n",
" BLUE GRASS | 23.7859 | 9.969 | 2.386 | 0.017 | 4.247 | 43.325 | \n",
"
\n",
"\n",
" BONDURANT | 18.6969 | 10.772 | 1.736 | 0.083 | -2.415 | 39.809 | \n",
"
\n",
"\n",
" BOONE | 14.2969 | 5.528 | 2.586 | 0.010 | 3.462 | 25.132 | \n",
"
\n",
"\n",
" BRITT | 6.4683 | 16.306 | 0.397 | 0.692 | -25.491 | 38.427 | \n",
"
\n",
"\n",
" BROOKLYN | 8.1753 | 14.681 | 0.557 | 0.578 | -20.598 | 36.949 | \n",
"
\n",
"\n",
" BUFFALO | -17.0115 | 33.965 | -0.501 | 0.616 | -83.581 | 49.558 | \n",
"
\n",
"\n",
" BUFFALO CENTER | 10.2996 | 16.625 | 0.620 | 0.536 | -22.285 | 42.884 | \n",
"
\n",
"\n",
" BURLINGTON | -0.6583 | 3.870 | -0.170 | 0.865 | -8.244 | 6.928 | \n",
"
\n",
"\n",
" BUSSEY | -140.3028 | 50.926 | -2.755 | 0.006 | -240.116 | -40.489 | \n",
"
\n",
"\n",
" CAMANCHE | -9.6778 | 17.323 | -0.559 | 0.576 | -43.630 | 24.274 | \n",
"
\n",
"\n",
" CAMBRIDGE | 21.5206 | 28.546 | 0.754 | 0.451 | -34.428 | 77.469 | \n",
"
\n",
"\n",
" CARLISLE | 7.7813 | 15.194 | 0.512 | 0.609 | -21.998 | 37.561 | \n",
"
\n",
"\n",
" CARROLL | 30.8795 | 5.463 | 5.652 | 0.000 | 20.171 | 41.588 | \n",
"
\n",
"\n",
" CARTER LAKE | -45.0795 | 15.909 | -2.834 | 0.005 | -76.260 | -13.899 | \n",
"
\n",
"\n",
" CASCADE | 14.8590 | 16.679 | 0.891 | 0.373 | -17.832 | 47.550 | \n",
"
\n",
"\n",
" CASEY | -17.8852 | 33.503 | -0.534 | 0.593 | -83.550 | 47.780 | \n",
"
\n",
"\n",
" CEDAR FALLS | 8.2666 | 3.003 | 2.753 | 0.006 | 2.381 | 14.152 | \n",
"
\n",
"\n",
" CEDAR RAPIDS | 8.5375 | 1.994 | 4.282 | 0.000 | 4.630 | 12.445 | \n",
"
\n",
"\n",
" CENTER POINT | 18.8264 | 15.451 | 1.218 | 0.223 | -11.457 | 49.110 | \n",
"
\n",
"\n",
" CENTERVILLE | 3.2231 | 6.662 | 0.484 | 0.629 | -9.835 | 16.281 | \n",
"
\n",
"\n",
" CENTRAL CITY | -7.2243 | 17.774 | -0.406 | 0.684 | -42.061 | 27.612 | \n",
"
\n",
"\n",
" CHARITON | 13.2592 | 9.437 | 1.405 | 0.160 | -5.238 | 31.756 | \n",
"
\n",
"\n",
" CHARLES CITY | 6.2152 | 7.217 | 0.861 | 0.389 | -7.929 | 20.360 | \n",
"
\n",
"\n",
" CHEROKEE | -6.3219 | 7.370 | -0.858 | 0.391 | -20.767 | 8.123 | \n",
"
\n",
"\n",
" CLARINDA | 9.7266 | 9.115 | 1.067 | 0.286 | -8.138 | 27.591 | \n",
"
\n",
"\n",
" CLARION | 8.2250 | 15.861 | 0.519 | 0.604 | -22.862 | 39.312 | \n",
"
\n",
"\n",
" CLARKSVILLE | 9.4002 | 25.101 | 0.375 | 0.708 | -39.796 | 58.597 | \n",
"
\n",
"\n",
" CLEAR LAKE | 8.4477 | 4.659 | 1.813 | 0.070 | -0.684 | 17.579 | \n",
"
\n",
"\n",
" CLINTON | 6.7116 | 3.904 | 1.719 | 0.086 | -0.940 | 14.363 | \n",
"
\n",
"\n",
" CLIVE | -1.0321 | 6.798 | -0.152 | 0.879 | -14.356 | 12.291 | \n",
"
\n",
"\n",
" COLFAX | 12.9306 | 14.756 | 0.876 | 0.381 | -15.991 | 41.853 | \n",
"
\n",
"\n",
" COLO | -7.7096 | 39.211 | -0.197 | 0.844 | -84.562 | 69.143 | \n",
"
\n",
"\n",
" COLUMBUS JUNCTION | -56.5840 | 12.031 | -4.703 | 0.000 | -80.165 | -33.003 | \n",
"
\n",
"\n",
" CONRAD | 10.9844 | 20.509 | 0.536 | 0.592 | -29.213 | 51.182 | \n",
"
\n",
"\n",
" COON RAPIDS | 2.6825 | 18.793 | 0.143 | 0.886 | -34.151 | 39.516 | \n",
"
\n",
"\n",
" CORALVILLE | 31.6748 | 3.714 | 8.528 | 0.000 | 24.395 | 38.954 | \n",
"
\n",
"\n",
" CORNING | 9.1684 | 9.756 | 0.940 | 0.347 | -9.953 | 28.289 | \n",
"
\n",
"\n",
" CORWITH | 23.9953 | 37.201 | 0.645 | 0.519 | -48.918 | 96.909 | \n",
"
\n",
"\n",
" CORYDON | 1.7810 | 17.909 | 0.099 | 0.921 | -33.320 | 36.883 | \n",
"
\n",
"\n",
" COUNCIL BLUFFS | 17.3579 | 2.631 | 6.599 | 0.000 | 12.202 | 22.514 | \n",
"
\n",
"\n",
" CRESCENT | 8.6032 | 39.957 | 0.215 | 0.830 | -69.712 | 86.918 | \n",
"
\n",
"\n",
" CRESCO | 19.7904 | 8.722 | 2.269 | 0.023 | 2.696 | 36.885 | \n",
"
\n",
"\n",
" CRESTON | 13.6301 | 6.391 | 2.133 | 0.033 | 1.103 | 26.157 | \n",
"
\n",
"\n",
" Carroll | 244.1771 | 203.635 | 1.199 | 0.230 | -154.941 | 643.295 | \n",
"
\n",
"\n",
" Cumming | -127.8339 | 83.144 | -1.538 | 0.124 | -290.793 | 35.125 | \n",
"
\n",
"\n",
" DAKOTA CITY | -89.7085 | 40.748 | -2.202 | 0.028 | -169.574 | -9.843 | \n",
"
\n",
"\n",
" DANVILLE | -15.6578 | 52.594 | -0.298 | 0.766 | -118.741 | 87.425 | \n",
"
\n",
"\n",
" DAVENPORT | -12.5572 | 2.318 | -5.417 | 0.000 | -17.101 | -8.014 | \n",
"
\n",
"\n",
" DAYTON | 18.7383 | 21.158 | 0.886 | 0.376 | -22.730 | 60.206 | \n",
"
\n",
"\n",
" DE SOTO | 7.0988 | 17.774 | 0.399 | 0.690 | -27.737 | 41.935 | \n",
"
\n",
"\n",
" DECORAH | 4.9740 | 6.163 | 0.807 | 0.420 | -7.105 | 17.053 | \n",
"
\n",
"\n",
" DELAWARE | 0.4450 | 46.735 | 0.010 | 0.992 | -91.154 | 92.044 | \n",
"
\n",
"\n",
" DELHI | 21.3423 | 61.412 | 0.348 | 0.728 | -99.023 | 141.707 | \n",
"
\n",
"\n",
" DELMAR | -6.1927 | 37.837 | -0.164 | 0.870 | -80.351 | 67.966 | \n",
"
\n",
"\n",
" DENISON | 13.6400 | 6.879 | 1.983 | 0.047 | 0.157 | 27.123 | \n",
"
\n",
"\n",
" DENVER | 13.7529 | 18.335 | 0.750 | 0.453 | -22.183 | 49.689 | \n",
"
\n",
"\n",
" DEWITT | 23.1845 | 14.424 | 1.607 | 0.108 | -5.086 | 51.455 | \n",
"
\n",
"\n",
" DONNELLSON | 10.5607 | 49.405 | 0.214 | 0.831 | -86.272 | 107.394 | \n",
"
\n",
"\n",
" DOWS | -9.0705 | 56.493 | -0.161 | 0.872 | -119.794 | 101.653 | \n",
"
\n",
"\n",
" DUBUQUE | 9.8759 | 2.795 | 3.534 | 0.000 | 4.398 | 15.353 | \n",
"
\n",
"\n",
" DUMONT | 11.5202 | 25.896 | 0.445 | 0.656 | -39.234 | 62.275 | \n",
"
\n",
"\n",
" DUNLAP | 8.8208 | 15.030 | 0.587 | 0.557 | -20.638 | 38.280 | \n",
"
\n",
"\n",
" DURANT | 17.2065 | 13.026 | 1.321 | 0.187 | -8.324 | 42.737 | \n",
"
\n",
"\n",
" DYERSVILLE | 17.7488 | 8.953 | 1.982 | 0.047 | 0.202 | 35.296 | \n",
"
\n",
"\n",
" DYSART | 6.3149 | 33.503 | 0.188 | 0.850 | -59.350 | 71.980 | \n",
"
\n",
"\n",
" Des Moines | -7.9060 | 20.719 | -0.382 | 0.703 | -48.514 | 32.702 | \n",
"
\n",
"\n",
" Dubuque | -11.0442 | 26.106 | -0.423 | 0.672 | -62.211 | 40.123 | \n",
"
\n",
"\n",
" EAGLE GROVE | 14.9995 | 11.910 | 1.259 | 0.208 | -8.344 | 38.343 | \n",
"
\n",
"\n",
" EARLHAM | 11.3089 | 33.503 | 0.338 | 0.736 | -54.356 | 76.974 | \n",
"
\n",
"\n",
" EARLY | 5.7175 | 36.597 | 0.156 | 0.876 | -66.012 | 77.447 | \n",
"
\n",
"\n",
" EDDYVILLE | 1.9253 | 36.022 | 0.053 | 0.957 | -68.676 | 72.526 | \n",
"
\n",
"\n",
" EDGEWOOD | 18.5049 | 21.999 | 0.841 | 0.400 | -24.612 | 61.621 | \n",
"
\n",
"\n",
" ELDON | -7.9854 | 32.634 | -0.245 | 0.807 | -71.947 | 55.976 | \n",
"
\n",
"\n",
" ELDORA | 14.5795 | 11.532 | 1.264 | 0.206 | -8.023 | 37.182 | \n",
"
\n",
"\n",
" ELDRIDGE | 0.8908 | 7.855 | 0.113 | 0.910 | -14.504 | 16.285 | \n",
"
\n",
"\n",
" ELKADER | 13.2149 | 18.560 | 0.712 | 0.476 | -23.162 | 49.591 | \n",
"
\n",
"\n",
" ELLSWORTH | 21.6375 | 21.272 | 1.017 | 0.309 | -20.055 | 63.330 | \n",
"
\n",
"\n",
" ELMA | 30.0380 | 29.422 | 1.021 | 0.307 | -27.627 | 87.703 | \n",
"
\n",
"\n",
" ELY | 0.4269 | 31.082 | 0.014 | 0.989 | -60.492 | 61.346 | \n",
"
\n",
"\n",
" EMMETSBURG | 7.5235 | 6.775 | 1.111 | 0.267 | -5.754 | 20.801 | \n",
"
\n",
"\n",
" ESTHERVILLE | 8.5041 | 7.449 | 1.142 | 0.254 | -6.095 | 23.104 | \n",
"
\n",
"\n",
" EVANSDALE | -10.0774 | 9.370 | -1.075 | 0.282 | -28.443 | 8.288 | \n",
"
\n",
"\n",
" EVERLY | 4.7498 | 76.976 | 0.062 | 0.951 | -146.122 | 155.621 | \n",
"
\n",
"\n",
" EXIRA | -23.9271 | 43.434 | -0.551 | 0.582 | -109.057 | 61.203 | \n",
"
\n",
"\n",
" FAIRBANK | 5.2703 | 21.998 | 0.240 | 0.811 | -37.846 | 48.387 | \n",
"
\n",
"\n",
" FAIRFAX | -6.9754 | 33.965 | -0.205 | 0.837 | -73.545 | 59.595 | \n",
"
\n",
"\n",
" FAIRFIELD | 2.6637 | 7.290 | 0.365 | 0.715 | -11.625 | 16.952 | \n",
"
\n",
"\n",
" FARLEY | -6.3658 | 37.837 | -0.168 | 0.866 | -80.525 | 67.794 | \n",
"
\n",
"\n",
" FARMINGTON | 12.0277 | 36.022 | 0.334 | 0.738 | -58.574 | 82.629 | \n",
"
\n",
"\n",
" FAYETTE | -4.8607 | 24.550 | -0.198 | 0.843 | -52.978 | 43.257 | \n",
"
\n",
"\n",
" FLOYD | 26.8015 | 17.773 | 1.508 | 0.132 | -8.034 | 61.637 | \n",
"
\n",
"\n",
" FONDA | -16.0151 | 83.142 | -0.193 | 0.847 | -178.972 | 146.942 | \n",
"
\n",
"\n",
" FONTANELLE | 5.4821 | 25.896 | 0.212 | 0.832 | -45.273 | 56.237 | \n",
"
\n",
"\n",
" FOREST CITY | 2.5327 | 8.460 | 0.299 | 0.765 | -14.048 | 19.114 | \n",
"
\n",
"\n",
" FORT ATKINSON | 18.6753 | 16.154 | 1.156 | 0.248 | -12.985 | 50.336 | \n",
"
\n",
"\n",
" FORT DODGE | 6.7896 | 3.964 | 1.713 | 0.087 | -0.979 | 14.558 | \n",
"
\n",
"\n",
" FORT MADISON | 4.3457 | 5.635 | 0.771 | 0.441 | -6.698 | 15.390 | \n",
"
\n",
"\n",
" FREDERICKSBURG | 20.6748 | 23.095 | 0.895 | 0.371 | -24.591 | 65.941 | \n",
"
\n",
"\n",
" GARNER | -11.0150 | 15.407 | -0.715 | 0.475 | -41.211 | 19.181 | \n",
"
\n",
"\n",
" GEORGE | 14.2376 | 18.637 | 0.764 | 0.445 | -22.290 | 50.765 | \n",
"
\n",
"\n",
" GILBERTVILLE | 25.6181 | 83.142 | 0.308 | 0.758 | -137.339 | 188.575 | \n",
"
\n",
"\n",
" GILMORE CITY | -33.7885 | 50.925 | -0.663 | 0.507 | -133.601 | 66.024 | \n",
"
\n",
"\n",
" GLADBROOK | 22.4710 | 23.709 | 0.948 | 0.343 | -23.999 | 68.941 | \n",
"
\n",
"\n",
" GLENWOOD | -2.3770 | 10.889 | -0.218 | 0.827 | -23.719 | 18.965 | \n",
"
\n",
"\n",
" GLIDDEN | 16.4634 | 28.003 | 0.588 | 0.557 | -38.421 | 71.348 | \n",
"
\n",
"\n",
" GOLDFIELD | -73.1030 | 52.594 | -1.390 | 0.165 | -176.186 | 29.980 | \n",
"
\n",
"\n",
" GOWRIE | 2.4441 | 14.533 | 0.168 | 0.866 | -26.040 | 30.928 | \n",
"
\n",
"\n",
" GRAETTINGER | 11.3875 | 20.719 | 0.550 | 0.583 | -29.220 | 51.995 | \n",
"
\n",
"\n",
" GRAND JUNCTION | -23.8708 | 38.505 | -0.620 | 0.535 | -99.340 | 51.599 | \n",
"
\n",
"\n",
" GRAND MOUND | 11.7441 | 25.489 | 0.461 | 0.645 | -38.213 | 61.701 | \n",
"
\n",
"\n",
" GRANGER | 2.4954 | 25.896 | 0.096 | 0.923 | -48.259 | 53.250 | \n",
"
\n",
"\n",
" GREENE | 10.9579 | 19.823 | 0.553 | 0.580 | -27.896 | 49.811 | \n",
"
\n",
"\n",
" GREENFIELD | 4.3470 | 14.680 | 0.296 | 0.767 | -24.426 | 33.120 | \n",
"
\n",
"\n",
" GRIMES | 10.5076 | 5.365 | 1.958 | 0.050 | -0.008 | 21.023 | \n",
"
\n",
"\n",
" GRINNELL | 12.1846 | 5.451 | 2.235 | 0.025 | 1.500 | 22.869 | \n",
"
\n",
"\n",
" GRISWOLD | 2.1828 | 91.076 | 0.024 | 0.981 | -176.323 | 180.689 | \n",
"
\n",
"\n",
" GRUNDY CENTER | 14.6975 | 10.109 | 1.454 | 0.146 | -5.116 | 34.510 | \n",
"
\n",
"\n",
" GUTHRIE CENTER | 17.6120 | 17.386 | 1.013 | 0.311 | -16.463 | 51.687 | \n",
"
\n",
"\n",
" GUTTENBERG | 4.9218 | 12.897 | 0.382 | 0.703 | -20.355 | 30.199 | \n",
"
\n",
"\n",
" GUTTENBURG | 20.4662 | 20.406 | 1.003 | 0.316 | -19.530 | 60.462 | \n",
"
\n",
"\n",
" HAMBURG | -22.5931 | 39.211 | -0.576 | 0.564 | -99.446 | 54.260 | \n",
"
\n",
"\n",
" HAMPTON | 5.1508 | 7.985 | 0.645 | 0.519 | -10.499 | 20.801 | \n",
"
\n",
"\n",
" HARLAN | 19.5010 | 8.031 | 2.428 | 0.015 | 3.761 | 35.241 | \n",
"
\n",
"\n",
" HARPERS FERRY | -21.1814 | 29.421 | -0.720 | 0.472 | -78.847 | 36.484 | \n",
"
\n",
"\n",
" HARTLEY | 21.5626 | 11.891 | 1.813 | 0.070 | -1.744 | 44.869 | \n",
"
\n",
"\n",
" HAWARDEN | 6.4730 | 15.539 | 0.417 | 0.677 | -23.982 | 36.929 | \n",
"
\n",
"\n",
" HAZLETON | -138.5507 | 36.598 | -3.786 | 0.000 | -210.281 | -66.820 | \n",
"
\n",
"\n",
" HIAWATHA | -24.0826 | 10.358 | -2.325 | 0.020 | -44.384 | -3.781 | \n",
"
\n",
"\n",
" HOLSTEIN | 12.6384 | 11.461 | 1.103 | 0.270 | -9.825 | 35.102 | \n",
"
\n",
"\n",
" HOLY CROSS | 17.5875 | 26.322 | 0.668 | 0.504 | -34.003 | 69.178 | \n",
"
\n",
"\n",
" HOSPERS | 23.5566 | 39.211 | 0.601 | 0.548 | -53.297 | 100.410 | \n",
"
\n",
"\n",
" HUBBARD | 21.4838 | 23.244 | 0.924 | 0.355 | -24.075 | 67.042 | \n",
"
\n",
"\n",
" HUDSON | 5.3643 | 32.224 | 0.166 | 0.868 | -57.794 | 68.523 | \n",
"
\n",
"\n",
" HUMBOLDT | 11.7142 | 8.808 | 1.330 | 0.184 | -5.548 | 28.977 | \n",
"
\n",
"\n",
" HUMESTON | -12.5408 | 37.202 | -0.337 | 0.736 | -85.455 | 60.374 | \n",
"
\n",
"\n",
" HUXLEY | 13.0762 | 18.714 | 0.699 | 0.485 | -23.603 | 49.755 | \n",
"
\n",
"\n",
" IDA GROVE | 14.2222 | 11.568 | 1.229 | 0.219 | -8.450 | 36.895 | \n",
"
\n",
"\n",
" INDEPENDENCE | 16.3160 | 6.928 | 2.355 | 0.019 | 2.738 | 29.894 | \n",
"
\n",
"\n",
" INDIANOLA | 3.8883 | 5.030 | 0.773 | 0.439 | -5.970 | 13.747 | \n",
"
\n",
"\n",
" INWOOD | 15.1291 | 13.436 | 1.126 | 0.260 | -11.204 | 41.463 | \n",
"
\n",
"\n",
" IOWA CITY | 8.3816 | 2.642 | 3.172 | 0.002 | 3.203 | 13.560 | \n",
"
\n",
"\n",
" IOWA FALLS | 18.5881 | 7.904 | 2.352 | 0.019 | 3.097 | 34.080 | \n",
"
\n",
"\n",
" IRETON | 15.8366 | 33.964 | 0.466 | 0.641 | -50.733 | 82.406 | \n",
"
\n",
"\n",
" Inwood | 21.0536 | 36.597 | 0.575 | 0.565 | -50.676 | 92.783 | \n",
"
\n",
"\n",
" JEFFERSON | 12.2832 | 8.272 | 1.485 | 0.138 | -3.930 | 28.497 | \n",
"
\n",
"\n",
" JESUP | 9.8436 | 15.195 | 0.648 | 0.517 | -19.937 | 39.625 | \n",
"
\n",
"\n",
" JEWELL | 22.1007 | 21.999 | 1.005 | 0.315 | -21.017 | 65.218 | \n",
"
\n",
"\n",
" JOHNSTON | 16.1114 | 4.602 | 3.501 | 0.000 | 7.091 | 25.132 | \n",
"
\n",
"\n",
" KELLOG | 10.5823 | 45.553 | 0.232 | 0.816 | -78.700 | 99.865 | \n",
"
\n",
"\n",
" KELLOGG | 5.9998 | 22.665 | 0.265 | 0.791 | -38.423 | 50.423 | \n",
"
\n",
"\n",
" KEOKUK | 20.4061 | 5.105 | 3.997 | 0.000 | 10.400 | 30.412 | \n",
"
\n",
"\n",
" KEOSAUQUA | 18.9282 | 14.016 | 1.350 | 0.177 | -8.543 | 46.399 | \n",
"
\n",
"\n",
" KEOTA | 4.9876 | 30.728 | 0.162 | 0.871 | -55.238 | 65.213 | \n",
"
\n",
"\n",
" KINGSLEY | 1.6400 | 28.828 | 0.057 | 0.955 | -54.863 | 58.143 | \n",
"
\n",
"\n",
" KNOXVILLE | 12.4646 | 5.619 | 2.218 | 0.027 | 1.452 | 23.477 | \n",
"
\n",
"\n",
" LA PORTE CITY | 16.4268 | 12.999 | 1.264 | 0.206 | -9.052 | 41.905 | \n",
"
\n",
"\n",
" LAKE CITY | 10.7820 | 17.448 | 0.618 | 0.537 | -23.416 | 44.980 | \n",
"
\n",
"\n",
" LAKE MILLS | 3.5394 | 14.250 | 0.248 | 0.804 | -24.390 | 31.469 | \n",
"
\n",
"\n",
" LAKE PARK | 16.7145 | 35.472 | 0.471 | 0.637 | -52.810 | 86.239 | \n",
"
\n",
"\n",
" LAKE VIEW | 0.8195 | 14.680 | 0.056 | 0.955 | -27.953 | 29.592 | \n",
"
\n",
"\n",
" LAMONI | 12.2978 | 19.824 | 0.620 | 0.535 | -26.556 | 51.151 | \n",
"
\n",
"\n",
" LANSING | 10.4263 | 16.680 | 0.625 | 0.532 | -22.266 | 43.118 | \n",
"
\n",
"\n",
" LARCHWOOD | 8.2965 | 11.755 | 0.706 | 0.480 | -14.742 | 31.336 | \n",
"
\n",
"\n",
" LATIMER | 17.8635 | 72.006 | 0.248 | 0.804 | -123.267 | 158.994 | \n",
"
\n",
"\n",
" LAURENS | 10.1463 | 14.911 | 0.680 | 0.496 | -19.079 | 39.372 | \n",
"
\n",
"\n",
" LAWLER | 44.4770 | 31.829 | 1.397 | 0.162 | -17.908 | 106.862 | \n",
"
\n",
"\n",
" LE CLAIRE | 3.4458 | 12.397 | 0.278 | 0.781 | -20.851 | 27.743 | \n",
"
\n",
"\n",
" LE GRAND | 15.9082 | 12.243 | 1.299 | 0.194 | -8.088 | 39.904 | \n",
"
\n",
"\n",
" LE MARS | 4.6337 | 9.990 | 0.464 | 0.643 | -14.945 | 24.213 | \n",
"
\n",
"\n",
" LECLAIRE | 34.4375 | 11.496 | 2.996 | 0.003 | 11.906 | 56.969 | \n",
"
\n",
"\n",
" LEMARS | 14.0501 | 6.988 | 2.011 | 0.044 | 0.353 | 27.747 | \n",
"
\n",
"\n",
" LENOX | 5.1152 | 17.201 | 0.297 | 0.766 | -28.598 | 38.828 | \n",
"
\n",
"\n",
" LEON | -0.6040 | 18.872 | -0.032 | 0.974 | -37.593 | 36.386 | \n",
"
\n",
"\n",
" LISBON | 12.1819 | 17.323 | 0.703 | 0.482 | -21.771 | 46.134 | \n",
"
\n",
"\n",
" LOGAN | 17.3734 | 20.109 | 0.864 | 0.388 | -22.039 | 56.786 | \n",
"
\n",
"\n",
" LOHRVILLE | 26.8624 | 117.573 | 0.228 | 0.819 | -203.578 | 257.303 | \n",
"
\n",
"\n",
" LOST NATION | 5.2824 | 35.472 | 0.149 | 0.882 | -64.243 | 74.807 | \n",
"
\n",
"\n",
" LOVILIA | -1.3982 | 61.411 | -0.023 | 0.982 | -121.763 | 118.966 | \n",
"
\n",
"\n",
" MADRID | 15.4158 | 15.237 | 1.012 | 0.312 | -14.447 | 45.279 | \n",
"
\n",
"\n",
" MALVERN | -47.8482 | 37.202 | -1.286 | 0.198 | -120.763 | 25.067 | \n",
"
\n",
"\n",
" MANCHESTER | 11.0398 | 7.984 | 1.383 | 0.167 | -4.608 | 26.688 | \n",
"
\n",
"\n",
" MANLY | -18.0850 | 32.225 | -0.561 | 0.575 | -81.244 | 45.074 | \n",
"
\n",
"\n",
" MANNING | 9.6254 | 12.627 | 0.762 | 0.446 | -15.124 | 34.375 | \n",
"
\n",
"\n",
" MANSON | 18.7842 | 15.584 | 1.205 | 0.228 | -11.760 | 49.328 | \n",
"
\n",
"\n",
" MAPLETON | 9.3938 | 9.854 | 0.953 | 0.340 | -9.920 | 28.708 | \n",
"
\n",
"\n",
" MAQUOKETA | 15.4257 | 5.869 | 2.628 | 0.009 | 3.922 | 26.929 | \n",
"
\n",
"\n",
" MARCUS | 16.8299 | 23.244 | 0.724 | 0.469 | -28.728 | 62.388 | \n",
"
\n",
"\n",
" MARENGO | 27.6627 | 9.956 | 2.779 | 0.005 | 8.150 | 47.175 | \n",
"
\n",
"\n",
" MARION | 12.5049 | 4.296 | 2.911 | 0.004 | 4.086 | 20.924 | \n",
"
\n",
"\n",
" MARSHALLTOWN | 8.9378 | 4.150 | 2.154 | 0.031 | 0.804 | 17.072 | \n",
"
\n",
"\n",
" MARTENSDALE | -6.4502 | 45.552 | -0.142 | 0.887 | -95.732 | 82.831 | \n",
"
\n",
"\n",
" MASON CITY | -0.5532 | 3.439 | -0.161 | 0.872 | -7.294 | 6.188 | \n",
"
\n",
"\n",
" MAXWELL | 16.8655 | 39.957 | 0.422 | 0.673 | -61.450 | 95.181 | \n",
"
\n",
"\n",
" MECHANICSVILLE | -42.9215 | 36.022 | -1.192 | 0.233 | -113.523 | 27.680 | \n",
"
\n",
"\n",
" MEDIAPOLIS | 5.8980 | 11.076 | 0.533 | 0.594 | -15.811 | 27.607 | \n",
"
\n",
"\n",
" MELBOURNE | 18.2901 | 83.142 | 0.220 | 0.826 | -144.666 | 181.246 | \n",
"
\n",
"\n",
" MELCHER-DALLAS | 6.9854 | 15.629 | 0.447 | 0.655 | -23.646 | 37.617 | \n",
"
\n",
"\n",
" MERRILL | 1.0184 | 64.407 | 0.016 | 0.987 | -125.218 | 127.255 | \n",
"
\n",
"\n",
" MILFORD | 15.0328 | 6.742 | 2.230 | 0.026 | 1.819 | 28.247 | \n",
"
\n",
"\n",
" MINDEN | 14.2626 | 67.890 | 0.210 | 0.834 | -118.799 | 147.325 | \n",
"
\n",
"\n",
" MISSOURI VALLEY | 10.5286 | 8.062 | 1.306 | 0.192 | -5.273 | 26.330 | \n",
"
\n",
"\n",
" MONONA | 3.1677 | 9.877 | 0.321 | 0.748 | -16.191 | 22.526 | \n",
"
\n",
"\n",
" MONROE | 9.5374 | 18.560 | 0.514 | 0.607 | -26.840 | 45.914 | \n",
"
\n",
"\n",
" MONTEZUMA | 11.2643 | 11.044 | 1.020 | 0.308 | -10.381 | 32.910 | \n",
"
\n",
"\n",
" MONTICELLO | 14.6337 | 5.830 | 2.510 | 0.012 | 3.208 | 26.060 | \n",
"
\n",
"\n",
" MONTROSE | -14.5274 | 33.061 | -0.439 | 0.660 | -79.325 | 50.271 | \n",
"
\n",
"\n",
" MORAVIA | 7.9187 | 24.913 | 0.318 | 0.751 | -40.911 | 56.748 | \n",
"
\n",
"\n",
" MOUNT AYR | 20.2394 | 14.425 | 1.403 | 0.161 | -8.032 | 48.511 | \n",
"
\n",
"\n",
" MOUNT PLEASANT | 15.2371 | 6.815 | 2.236 | 0.025 | 1.879 | 28.595 | \n",
"
\n",
"\n",
" MOUNT VERNON | 8.7258 | 6.191 | 1.409 | 0.159 | -3.409 | 20.860 | \n",
"
\n",
"\n",
" MT PLEASANT | 21.9054 | 15.583 | 1.406 | 0.160 | -8.637 | 52.448 | \n",
"
\n",
"\n",
" MT VERNON | -35.2418 | 24.036 | -1.466 | 0.143 | -82.351 | 11.867 | \n",
"
\n",
"\n",
" MUSCATINE | 10.5240 | 3.742 | 2.812 | 0.005 | 3.189 | 17.859 | \n",
"
\n",
"\n",
" NASHUA | 11.7494 | 21.998 | 0.534 | 0.593 | -31.367 | 54.866 | \n",
"
\n",
"\n",
" NEOLA | 23.4626 | 25.690 | 0.913 | 0.361 | -26.889 | 73.814 | \n",
"
\n",
"\n",
" NEVADA | 9.0361 | 6.831 | 1.323 | 0.186 | -4.353 | 22.425 | \n",
"
\n",
"\n",
" NEW HAMPTON | 10.6192 | 12.723 | 0.835 | 0.404 | -14.317 | 35.556 | \n",
"
\n",
"\n",
" NEW SHARON | 11.7852 | 38.505 | 0.306 | 0.760 | -63.684 | 87.255 | \n",
"
\n",
"\n",
" NEW VIRGINIA | 17.2334 | 34.446 | 0.500 | 0.617 | -50.279 | 84.746 | \n",
"
\n",
"\n",
" NEWTON | 5.1702 | 4.256 | 1.215 | 0.224 | -3.171 | 13.512 | \n",
"
\n",
"\n",
" NORA SPRINGS | 6.5939 | 30.727 | 0.215 | 0.830 | -53.631 | 66.819 | \n",
"
\n",
"\n",
" NORTH ENGLISH | 20.1190 | 36.022 | 0.559 | 0.576 | -50.484 | 90.721 | \n",
"
\n",
"\n",
" NORTH LIBERTY | -0.5936 | 5.789 | -0.103 | 0.918 | -11.940 | 10.753 | \n",
"
\n",
"\n",
" NORTHWOOD | 16.5553 | 13.919 | 1.189 | 0.234 | -10.726 | 43.837 | \n",
"
\n",
"\n",
" NORWALK | 23.7449 | 9.628 | 2.466 | 0.014 | 4.875 | 42.615 | \n",
"
\n",
"\n",
" Northwood | 27.3407 | 17.841 | 1.532 | 0.125 | -7.626 | 62.308 | \n",
"
\n",
"\n",
" OAKLAND | 3.9275 | 12.821 | 0.306 | 0.759 | -21.202 | 29.057 | \n",
"
\n",
"\n",
" OELWEIN | 8.9083 | 8.398 | 1.061 | 0.289 | -7.551 | 25.368 | \n",
"
\n",
"\n",
" OGDEN | 9.9022 | 18.409 | 0.538 | 0.591 | -26.179 | 45.983 | \n",
"
\n",
"\n",
" OKOBOJI | 13.8150 | 32.224 | 0.429 | 0.668 | -49.344 | 76.974 | \n",
"
\n",
"\n",
" ONAWA | 6.4833 | 7.236 | 0.896 | 0.370 | -7.698 | 20.665 | \n",
"
\n",
"\n",
" ORANGE CITY | 31.1245 | 11.870 | 2.622 | 0.009 | 7.859 | 54.390 | \n",
"
\n",
"\n",
" OSAGE | 13.0410 | 7.368 | 1.770 | 0.077 | -1.399 | 27.481 | \n",
"
\n",
"\n",
" OSCEOLA | 20.6072 | 7.843 | 2.627 | 0.009 | 5.235 | 35.980 | \n",
"
\n",
"\n",
" OSKALOOSA | 3.0222 | 5.400 | 0.560 | 0.576 | -7.561 | 13.605 | \n",
"
\n",
"\n",
" OTHO | -21.9789 | 45.553 | -0.482 | 0.629 | -111.261 | 67.303 | \n",
"
\n",
"\n",
" OTTUMWA | 17.3559 | 4.459 | 3.893 | 0.000 | 8.617 | 26.095 | \n",
"
\n",
"\n",
" OTTUWMA | 19.6167 | 6.859 | 2.860 | 0.004 | 6.173 | 33.060 | \n",
"
\n",
"\n",
" PACIFIC JUNCTION | -33.2145 | 18.484 | -1.797 | 0.072 | -69.442 | 3.013 | \n",
"
\n",
"\n",
" PALO | -26.9541 | 33.060 | -0.815 | 0.415 | -91.750 | 37.842 | \n",
"
\n",
"\n",
" PANORA | 6.4980 | 12.651 | 0.514 | 0.607 | -18.297 | 31.293 | \n",
"
\n",
"\n",
" PARKERSBURG | 11.0946 | 22.127 | 0.501 | 0.616 | -32.273 | 54.462 | \n",
"
\n",
"\n",
" PAULLINA | -4.4437 | 18.048 | -0.246 | 0.806 | -39.817 | 30.929 | \n",
"
\n",
"\n",
" PELLA | 21.8905 | 7.074 | 3.095 | 0.002 | 8.027 | 35.754 | \n",
"
\n",
"\n",
" PEOSTA | 13.3592 | 27.244 | 0.490 | 0.624 | -40.038 | 66.756 | \n",
"
\n",
"\n",
" PERRY | 11.9243 | 7.265 | 1.641 | 0.101 | -2.315 | 26.164 | \n",
"
\n",
"\n",
" PLEASANT HILL | 0.2158 | 6.905 | 0.031 | 0.975 | -13.319 | 13.750 | \n",
"
\n",
"\n",
" PLEASANTVILLE | -10.8642 | 16.005 | -0.679 | 0.497 | -42.234 | 20.506 | \n",
"
\n",
"\n",
" POCAHONTAS | 13.8810 | 11.891 | 1.167 | 0.243 | -9.424 | 37.186 | \n",
"
\n",
"\n",
" POLK CITY | 16.2014 | 12.396 | 1.307 | 0.191 | -8.094 | 40.497 | \n",
"
\n",
"\n",
" POSTVILLE | -1.3631 | 30.053 | -0.045 | 0.964 | -60.266 | 57.540 | \n",
"
\n",
"\n",
" PRAIRIE CITY | -15.9101 | 35.473 | -0.449 | 0.654 | -85.436 | 53.616 | \n",
"
\n",
"\n",
" PRIMGHAR | 5.7114 | 19.640 | 0.291 | 0.771 | -32.782 | 44.205 | \n",
"
\n",
"\n",
" PRINCETON | 9.8593 | 32.635 | 0.302 | 0.763 | -54.104 | 73.822 | \n",
"
\n",
"\n",
" RAYMOND | 0.4309 | 17.022 | 0.025 | 0.980 | -32.931 | 33.793 | \n",
"
\n",
"\n",
" RED OAK | 3.0798 | 7.348 | 0.419 | 0.675 | -11.322 | 17.482 | \n",
"
\n",
"\n",
" REINBECK | 5.7782 | 35.472 | 0.163 | 0.871 | -63.747 | 75.303 | \n",
"
\n",
"\n",
" REMSEN | 15.5809 | 17.841 | 0.873 | 0.382 | -19.387 | 50.549 | \n",
"
\n",
"\n",
" RICEVILLE | 13.8661 | 35.472 | 0.391 | 0.696 | -55.659 | 83.391 | \n",
"
\n",
"\n",
" RIVERSIDE | 17.8288 | 13.731 | 1.298 | 0.194 | -9.084 | 44.741 | \n",
"
\n",
"\n",
" ROBINS | 25.6121 | 143.994 | 0.178 | 0.859 | -256.613 | 307.837 | \n",
"
\n",
"\n",
" ROCK RAPIDS | 3.8871 | 9.370 | 0.415 | 0.678 | -14.478 | 22.252 | \n",
"
\n",
"\n",
" ROCK VALLEY | 5.8394 | 21.045 | 0.277 | 0.781 | -35.408 | 47.087 | \n",
"
\n",
"\n",
" ROCKWELL | 21.8659 | 16.104 | 1.358 | 0.175 | -9.698 | 53.429 | \n",
"
\n",
"\n",
" ROCKWELL CITY | 17.4914 | 19.288 | 0.907 | 0.364 | -20.313 | 55.296 | \n",
"
\n",
"\n",
" ROLFE | 15.4339 | 34.948 | 0.442 | 0.659 | -53.063 | 83.930 | \n",
"
\n",
"\n",
" RUTHVEN | 9.3762 | 30.053 | 0.312 | 0.755 | -49.527 | 68.280 | \n",
"
\n",
"\n",
" SAC CITY | 3.5511 | 8.159 | 0.435 | 0.663 | -12.441 | 19.543 | \n",
"
\n",
"\n",
" SANBORN | 22.6244 | 15.408 | 1.468 | 0.142 | -7.574 | 52.823 | \n",
"
\n",
"\n",
" SCHALLER | 8.8142 | 52.594 | 0.168 | 0.867 | -94.268 | 111.897 | \n",
"
\n",
"\n",
" SCHLESWIG | 3.5061 | 14.183 | 0.247 | 0.805 | -24.292 | 31.304 | \n",
"
\n",
"\n",
" SCRANTON | 4.1263 | 40.748 | 0.101 | 0.919 | -75.739 | 83.991 | \n",
"
\n",
"\n",
" SERGEANT BLUFF | 9.4991 | 10.586 | 0.897 | 0.370 | -11.249 | 30.247 | \n",
"
\n",
"\n",
" SHEFFIELD | -16.7184 | 39.211 | -0.426 | 0.670 | -93.572 | 60.135 | \n",
"
\n",
"\n",
" SHELDON | 18.7288 | 6.652 | 2.815 | 0.005 | 5.691 | 31.767 | \n",
"
\n",
"\n",
" SHELLSBURG | 11.3929 | 11.306 | 1.008 | 0.314 | -10.767 | 33.553 | \n",
"
\n",
"\n",
" SHENANDOAH | 15.2834 | 7.973 | 1.917 | 0.055 | -0.342 | 30.909 | \n",
"
\n",
"\n",
" SIBLEY | 10.3781 | 10.950 | 0.948 | 0.343 | -11.084 | 31.840 | \n",
"
\n",
"\n",
" SIGOURNEY | 14.8261 | 11.852 | 1.251 | 0.211 | -8.403 | 38.056 | \n",
"
\n",
"\n",
" SIOUX CENTER | 31.8050 | 7.810 | 4.072 | 0.000 | 16.497 | 47.113 | \n",
"
\n",
"\n",
" SIOUX CITY | 7.8343 | 2.649 | 2.958 | 0.003 | 2.643 | 13.025 | \n",
"
\n",
"\n",
" SIOUX RAPIDS | 8.1861 | 14.425 | 0.567 | 0.570 | -20.086 | 36.459 | \n",
"
\n",
"\n",
" SLATER | 7.9728 | 25.292 | 0.315 | 0.753 | -41.600 | 57.545 | \n",
"
\n",
"\n",
" SLOAN | 16.8442 | 19.203 | 0.877 | 0.380 | -20.792 | 54.481 | \n",
"
\n",
"\n",
" SOLON | 9.0008 | 15.628 | 0.576 | 0.565 | -21.630 | 39.632 | \n",
"
\n",
"\n",
" SPENCER | 9.2005 | 4.846 | 1.899 | 0.058 | -0.297 | 18.698 | \n",
"
\n",
"\n",
" SPIRIT LAKE | 13.5214 | 5.189 | 2.606 | 0.009 | 3.352 | 23.691 | \n",
"
\n",
"\n",
" SPRINGVILLE | 4.1382 | 39.211 | 0.106 | 0.916 | -72.715 | 80.991 | \n",
"
\n",
"\n",
" ST ANSGAR | -12.1602 | 14.951 | -0.813 | 0.416 | -41.463 | 17.143 | \n",
"
\n",
"\n",
" ST CHARLES | 0.6915 | 38.506 | 0.018 | 0.986 | -74.778 | 76.161 | \n",
"
\n",
"\n",
" ST LUCAS | 18.4430 | 27.006 | 0.683 | 0.495 | -34.487 | 71.373 | \n",
"
\n",
"\n",
" STANWOOD | -86.2965 | 67.890 | -1.271 | 0.204 | -219.359 | 46.766 | \n",
"
\n",
"\n",
" STATE CENTER | 25.8882 | 15.676 | 1.652 | 0.099 | -4.835 | 56.612 | \n",
"
\n",
"\n",
" STORM LAKE | 5.5457 | 4.268 | 1.299 | 0.194 | -2.820 | 13.911 | \n",
"
\n",
"\n",
" STORY CITY | 13.1286 | 12.974 | 1.012 | 0.312 | -12.300 | 38.557 | \n",
"
\n",
"\n",
" STRATFORD | 1.0744 | 22.949 | 0.047 | 0.963 | -43.905 | 46.054 | \n",
"
\n",
"\n",
" STRAWBERRY POINT | 10.7485 | 26.771 | 0.402 | 0.688 | -41.722 | 63.219 | \n",
"
\n",
"\n",
" STUART | 7.9827 | 12.135 | 0.658 | 0.511 | -15.802 | 31.768 | \n",
"
\n",
"\n",
" SULLY | 1.1542 | 58.798 | 0.020 | 0.984 | -114.088 | 116.397 | \n",
"
\n",
"\n",
" SUMNER | 8.0987 | 11.222 | 0.722 | 0.471 | -13.897 | 30.094 | \n",
"
\n",
"\n",
" SUTHERLAND | -6.2071 | 34.445 | -0.180 | 0.857 | -73.719 | 61.305 | \n",
"
\n",
"\n",
" SWEA CITY | -0.6337 | 35.472 | -0.018 | 0.986 | -70.158 | 68.891 | \n",
"
\n",
"\n",
" SWISHER | 58.1100 | 13.131 | 4.425 | 0.000 | 32.373 | 83.847 | \n",
"
\n",
"\n",
" TIFFIN | 5.0404 | 17.774 | 0.284 | 0.777 | -29.796 | 39.877 | \n",
"
\n",
"\n",
" TIPTON | 16.0052 | 9.177 | 1.744 | 0.081 | -1.981 | 33.991 | \n",
"
\n",
"\n",
" TOLEDO | 11.9659 | 7.454 | 1.605 | 0.108 | -2.643 | 26.575 | \n",
"
\n",
"\n",
" TRAER | -5.3410 | 15.450 | -0.346 | 0.730 | -35.623 | 24.942 | \n",
"
\n",
"\n",
" TREYNOR | -3.9643 | 41.587 | -0.095 | 0.924 | -85.474 | 77.545 | \n",
"
\n",
"\n",
" TRIPOLI | 15.1197 | 13.266 | 1.140 | 0.254 | -10.882 | 41.122 | \n",
"
\n",
"\n",
" URBANA | -1.5880 | 37.837 | -0.042 | 0.967 | -75.747 | 72.571 | \n",
"
\n",
"\n",
" URBANDALE | 12.8917 | 4.344 | 2.968 | 0.003 | 4.378 | 21.405 | \n",
"
\n",
"\n",
" Urbandale | 3.6420 | 33.060 | 0.110 | 0.912 | -61.154 | 68.438 | \n",
"
\n",
"\n",
" VAN METER | -84.4213 | 52.594 | -1.605 | 0.108 | -187.504 | 18.662 | \n",
"
\n",
"\n",
" VICTOR | 18.0625 | 25.489 | 0.709 | 0.479 | -31.895 | 68.020 | \n",
"
\n",
"\n",
" VILLISCA | 1.0963 | 43.434 | 0.025 | 0.980 | -84.034 | 86.227 | \n",
"
\n",
"\n",
" VINTON | 17.6707 | 14.250 | 1.240 | 0.215 | -10.258 | 45.599 | \n",
"
\n",
"\n",
" WALFORD | 16.5690 | 37.201 | 0.445 | 0.656 | -56.345 | 89.483 | \n",
"
\n",
"\n",
" WALKER | -16.2928 | 45.553 | -0.358 | 0.721 | -105.574 | 72.989 | \n",
"
\n",
"\n",
" WALL LAKE | 13.4953 | 54.439 | 0.248 | 0.804 | -93.203 | 120.193 | \n",
"
\n",
"\n",
" WALNUT | 0.8355 | 28.270 | 0.030 | 0.976 | -54.572 | 56.243 | \n",
"
\n",
"\n",
" WAPELLO | 13.6309 | 14.681 | 0.928 | 0.353 | -15.143 | 42.405 | \n",
"
\n",
"\n",
" WASHBURN | -64.1395 | 52.594 | -1.220 | 0.223 | -167.222 | 38.943 | \n",
"
\n",
"\n",
" WASHINGTON | 2.8315 | 5.756 | 0.492 | 0.623 | -8.450 | 14.113 | \n",
"
\n",
"\n",
" WATERLOO | -22.7595 | 2.590 | -8.787 | 0.000 | -27.836 | -17.683 | \n",
"
\n",
"\n",
" WAUKEE | 18.2832 | 5.611 | 3.258 | 0.001 | 7.285 | 29.281 | \n",
"
\n",
"\n",
" WAUKON | 10.8178 | 7.321 | 1.478 | 0.140 | -3.532 | 25.167 | \n",
"
\n",
"\n",
" WAVERLY | 10.9909 | 5.350 | 2.054 | 0.040 | 0.505 | 21.477 | \n",
"
\n",
"\n",
" WEBSTER CITY | 10.4118 | 7.008 | 1.486 | 0.137 | -3.323 | 24.147 | \n",
"
\n",
"\n",
" WELLMAN | 7.5048 | 13.078 | 0.574 | 0.566 | -18.128 | 33.137 | \n",
"
\n",
"\n",
" WELLSBURG | -29.4621 | 44.456 | -0.663 | 0.508 | -116.594 | 57.670 | \n",
"
\n",
"\n",
" WESLEY | 9.1705 | 17.261 | 0.531 | 0.595 | -24.661 | 43.002 | \n",
"
\n",
"\n",
" WEST BEND | 11.4648 | 19.823 | 0.578 | 0.563 | -27.388 | 50.317 | \n",
"
\n",
"\n",
" WEST BRANCH | 22.8898 | 12.652 | 1.809 | 0.070 | -1.908 | 47.687 | \n",
"
\n",
"\n",
" WEST BURLINGTON | 14.0926 | 8.509 | 1.656 | 0.098 | -2.584 | 30.770 | \n",
"
\n",
"\n",
" WEST DES MOINES | 30.2788 | 2.750 | 11.011 | 0.000 | 24.889 | 35.669 | \n",
"
\n",
"\n",
" WEST LIBERTY | 8.6568 | 12.419 | 0.697 | 0.486 | -15.683 | 32.997 | \n",
"
\n",
"\n",
" WEST POINT | 17.8887 | 15.450 | 1.158 | 0.247 | -12.393 | 48.171 | \n",
"
\n",
"\n",
" WEST UNION | 10.8489 | 11.172 | 0.971 | 0.332 | -11.048 | 32.746 | \n",
"
\n",
"\n",
" WHEATLAND | 11.2630 | 39.957 | 0.282 | 0.778 | -67.053 | 89.578 | \n",
"
\n",
"\n",
" WILLIAMSBURG | 16.4116 | 12.626 | 1.300 | 0.194 | -8.336 | 41.159 | \n",
"
\n",
"\n",
" WILTON | 20.7052 | 11.570 | 1.790 | 0.074 | -1.971 | 43.382 | \n",
"
\n",
"\n",
" WINDSOR HEIGHTS | 32.6892 | 4.072 | 8.028 | 0.000 | 24.708 | 40.670 | \n",
"
\n",
"\n",
" WINTERSET | 5.3052 | 7.402 | 0.717 | 0.474 | -9.202 | 19.813 | \n",
"
\n",
"\n",
" WINTHROP | 15.6299 | 28.002 | 0.558 | 0.577 | -39.254 | 70.514 | \n",
"
\n",
"\n",
" WOODBINE | 19.7643 | 16.411 | 1.204 | 0.228 | -12.400 | 51.929 | \n",
"
\n",
"\n",
" WOODWARD | 11.6388 | 34.446 | 0.338 | 0.735 | -55.874 | 79.151 | \n",
"
\n",
"\n",
" ZWINGLE | -15.5083 | 28.270 | -0.549 | 0.583 | -70.916 | 39.900 | \n",
"
\n",
"
\n",
"\n",
"\n",
" Omnibus: | 364812.861 | Durbin-Watson: | 2.001 | \n",
"
\n",
"\n",
" Prob(Omnibus): | 0.000 | Jarque-Bera (JB): | 67102567296.771 | \n",
"
\n",
"\n",
" Skew: | 5.870 | Prob(JB): | 0.00 | \n",
"
\n",
"\n",
" Kurtosis: | 2448.603 | Cond. No. | 1.39e+04 | \n",
"
\n",
"
"
],
"text/plain": [
"\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: sale_dollars R-squared: 0.718\n",
"Model: OLS Adj. R-squared: 0.718\n",
"Method: Least Squares F-statistic: 1791.\n",
"Date: Sun, 22 Oct 2017 Prob (F-statistic): 0.00\n",
"Time: 22:32:18 Log-Likelihood: -1.8133e+06\n",
"No. Observations: 269258 AIC: 3.627e+06\n",
"Df Residuals: 268874 BIC: 3.631e+06\n",
"Df Model: 383 \n",
"Covariance Type: nonrobust \n",
"=======================================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"---------------------------------------------------------------------------------------\n",
"const -111.2714 1.465 -75.970 0.000 -114.142 -108.401\n",
"state_bottle_retail 6.8100 0.037 181.877 0.000 6.737 6.883\n",
"bottles_sold 13.3641 0.016 812.038 0.000 13.332 13.396\n",
"ACKLEY 12.6694 21.388 0.592 0.554 -29.250 54.589\n",
"ADAIR -16.2146 31.082 -0.522 0.602 -77.134 44.705\n",
"ADEL 21.9562 11.496 1.910 0.056 -0.576 44.488\n",
"AFTON 9.7649 58.798 0.166 0.868 -105.478 125.007\n",
"AKRON 7.8456 24.550 0.320 0.749 -40.273 55.964\n",
"ALBIA 13.9177 11.107 1.253 0.210 -7.852 35.688\n",
"ALDEN 4.5391 18.484 0.246 0.806 -31.689 40.768\n",
"ALGONA 10.4988 6.497 1.616 0.106 -2.236 23.233\n",
"ALLISON 16.4433 22.391 0.734 0.463 -27.443 60.329\n",
"ALTA -16.2747 54.439 -0.299 0.765 -122.973 90.424\n",
"ALTOONA 9.2230 4.634 1.990 0.047 0.140 18.306\n",
"AMES 12.1338 2.696 4.501 0.000 6.850 17.418\n",
"ANAMOSA 16.3067 8.538 1.910 0.056 -0.427 33.040\n",
"ANITA 10.1520 16.791 0.605 0.545 -22.759 43.063\n",
"ANKENY 17.5795 3.219 5.461 0.000 11.270 23.889\n",
"ANTHON 13.1279 15.957 0.823 0.411 -18.147 44.403\n",
"ARLINGTON 28.7329 23.397 1.228 0.219 -17.125 74.591\n",
"ARMSTRONG -28.7348 49.406 -0.582 0.561 -125.568 68.099\n",
"ARNOLD'S PARK 1.3154 13.158 0.100 0.920 -24.475 27.106\n",
"ARNOLDS PARK 11.8707 9.231 1.286 0.198 -6.221 29.963\n",
"ATLANTIC 10.5251 6.462 1.629 0.103 -2.139 23.190\n",
"AUDUBON 12.3132 14.284 0.862 0.389 -15.684 40.310\n",
"AURELIA -6.9803 61.411 -0.114 0.910 -127.344 113.384\n",
"AVOCA 11.3464 9.618 1.180 0.238 -7.505 30.197\n",
"BALDWIN -19.7364 39.957 -0.494 0.621 -98.052 58.579\n",
"BANCROFT 8.9044 10.655 0.836 0.403 -11.980 29.789\n",
"BAXTER 20.9895 26.772 0.784 0.433 -31.482 73.461\n",
"BEDFORD 17.5580 16.306 1.077 0.282 -14.402 49.518\n",
"BELLE PLAINE 4.5213 10.096 0.448 0.654 -15.267 24.309\n",
"BELLEVUE 7.7428 13.266 0.584 0.559 -18.258 33.744\n",
"BELMOND 0.3506 15.194 0.023 0.982 -29.429 30.130\n",
"BETTENDORF 16.6460 3.602 4.621 0.000 9.586 23.706\n",
"BEVINGTON 14.0133 23.709 0.591 0.554 -32.456 60.483\n",
"BLOOMFIELD 12.4578 14.354 0.868 0.385 -15.676 40.592\n",
"BLUE GRASS 23.7859 9.969 2.386 0.017 4.247 43.325\n",
"BONDURANT 18.6969 10.772 1.736 0.083 -2.415 39.809\n",
"BOONE 14.2969 5.528 2.586 0.010 3.462 25.132\n",
"BRITT 6.4683 16.306 0.397 0.692 -25.491 38.427\n",
"BROOKLYN 8.1753 14.681 0.557 0.578 -20.598 36.949\n",
"BUFFALO -17.0115 33.965 -0.501 0.616 -83.581 49.558\n",
"BUFFALO CENTER 10.2996 16.625 0.620 0.536 -22.285 42.884\n",
"BURLINGTON -0.6583 3.870 -0.170 0.865 -8.244 6.928\n",
"BUSSEY -140.3028 50.926 -2.755 0.006 -240.116 -40.489\n",
"CAMANCHE -9.6778 17.323 -0.559 0.576 -43.630 24.274\n",
"CAMBRIDGE 21.5206 28.546 0.754 0.451 -34.428 77.469\n",
"CARLISLE 7.7813 15.194 0.512 0.609 -21.998 37.561\n",
"CARROLL 30.8795 5.463 5.652 0.000 20.171 41.588\n",
"CARTER LAKE -45.0795 15.909 -2.834 0.005 -76.260 -13.899\n",
"CASCADE 14.8590 16.679 0.891 0.373 -17.832 47.550\n",
"CASEY -17.8852 33.503 -0.534 0.593 -83.550 47.780\n",
"CEDAR FALLS 8.2666 3.003 2.753 0.006 2.381 14.152\n",
"CEDAR RAPIDS 8.5375 1.994 4.282 0.000 4.630 12.445\n",
"CENTER POINT 18.8264 15.451 1.218 0.223 -11.457 49.110\n",
"CENTERVILLE 3.2231 6.662 0.484 0.629 -9.835 16.281\n",
"CENTRAL CITY -7.2243 17.774 -0.406 0.684 -42.061 27.612\n",
"CHARITON 13.2592 9.437 1.405 0.160 -5.238 31.756\n",
"CHARLES CITY 6.2152 7.217 0.861 0.389 -7.929 20.360\n",
"CHEROKEE -6.3219 7.370 -0.858 0.391 -20.767 8.123\n",
"CLARINDA 9.7266 9.115 1.067 0.286 -8.138 27.591\n",
"CLARION 8.2250 15.861 0.519 0.604 -22.862 39.312\n",
"CLARKSVILLE 9.4002 25.101 0.375 0.708 -39.796 58.597\n",
"CLEAR LAKE 8.4477 4.659 1.813 0.070 -0.684 17.579\n",
"CLINTON 6.7116 3.904 1.719 0.086 -0.940 14.363\n",
"CLIVE -1.0321 6.798 -0.152 0.879 -14.356 12.291\n",
"COLFAX 12.9306 14.756 0.876 0.381 -15.991 41.853\n",
"COLO -7.7096 39.211 -0.197 0.844 -84.562 69.143\n",
"COLUMBUS JUNCTION -56.5840 12.031 -4.703 0.000 -80.165 -33.003\n",
"CONRAD 10.9844 20.509 0.536 0.592 -29.213 51.182\n",
"COON RAPIDS 2.6825 18.793 0.143 0.886 -34.151 39.516\n",
"CORALVILLE 31.6748 3.714 8.528 0.000 24.395 38.954\n",
"CORNING 9.1684 9.756 0.940 0.347 -9.953 28.289\n",
"CORWITH 23.9953 37.201 0.645 0.519 -48.918 96.909\n",
"CORYDON 1.7810 17.909 0.099 0.921 -33.320 36.883\n",
"COUNCIL BLUFFS 17.3579 2.631 6.599 0.000 12.202 22.514\n",
"CRESCENT 8.6032 39.957 0.215 0.830 -69.712 86.918\n",
"CRESCO 19.7904 8.722 2.269 0.023 2.696 36.885\n",
"CRESTON 13.6301 6.391 2.133 0.033 1.103 26.157\n",
"Carroll 244.1771 203.635 1.199 0.230 -154.941 643.295\n",
"Cumming -127.8339 83.144 -1.538 0.124 -290.793 35.125\n",
"DAKOTA CITY -89.7085 40.748 -2.202 0.028 -169.574 -9.843\n",
"DANVILLE -15.6578 52.594 -0.298 0.766 -118.741 87.425\n",
"DAVENPORT -12.5572 2.318 -5.417 0.000 -17.101 -8.014\n",
"DAYTON 18.7383 21.158 0.886 0.376 -22.730 60.206\n",
"DE SOTO 7.0988 17.774 0.399 0.690 -27.737 41.935\n",
"DECORAH 4.9740 6.163 0.807 0.420 -7.105 17.053\n",
"DELAWARE 0.4450 46.735 0.010 0.992 -91.154 92.044\n",
"DELHI 21.3423 61.412 0.348 0.728 -99.023 141.707\n",
"DELMAR -6.1927 37.837 -0.164 0.870 -80.351 67.966\n",
"DENISON 13.6400 6.879 1.983 0.047 0.157 27.123\n",
"DENVER 13.7529 18.335 0.750 0.453 -22.183 49.689\n",
"DEWITT 23.1845 14.424 1.607 0.108 -5.086 51.455\n",
"DONNELLSON 10.5607 49.405 0.214 0.831 -86.272 107.394\n",
"DOWS -9.0705 56.493 -0.161 0.872 -119.794 101.653\n",
"DUBUQUE 9.8759 2.795 3.534 0.000 4.398 15.353\n",
"DUMONT 11.5202 25.896 0.445 0.656 -39.234 62.275\n",
"DUNLAP 8.8208 15.030 0.587 0.557 -20.638 38.280\n",
"DURANT 17.2065 13.026 1.321 0.187 -8.324 42.737\n",
"DYERSVILLE 17.7488 8.953 1.982 0.047 0.202 35.296\n",
"DYSART 6.3149 33.503 0.188 0.850 -59.350 71.980\n",
"Des Moines -7.9060 20.719 -0.382 0.703 -48.514 32.702\n",
"Dubuque -11.0442 26.106 -0.423 0.672 -62.211 40.123\n",
"EAGLE GROVE 14.9995 11.910 1.259 0.208 -8.344 38.343\n",
"EARLHAM 11.3089 33.503 0.338 0.736 -54.356 76.974\n",
"EARLY 5.7175 36.597 0.156 0.876 -66.012 77.447\n",
"EDDYVILLE 1.9253 36.022 0.053 0.957 -68.676 72.526\n",
"EDGEWOOD 18.5049 21.999 0.841 0.400 -24.612 61.621\n",
"ELDON -7.9854 32.634 -0.245 0.807 -71.947 55.976\n",
"ELDORA 14.5795 11.532 1.264 0.206 -8.023 37.182\n",
"ELDRIDGE 0.8908 7.855 0.113 0.910 -14.504 16.285\n",
"ELKADER 13.2149 18.560 0.712 0.476 -23.162 49.591\n",
"ELLSWORTH 21.6375 21.272 1.017 0.309 -20.055 63.330\n",
"ELMA 30.0380 29.422 1.021 0.307 -27.627 87.703\n",
"ELY 0.4269 31.082 0.014 0.989 -60.492 61.346\n",
"EMMETSBURG 7.5235 6.775 1.111 0.267 -5.754 20.801\n",
"ESTHERVILLE 8.5041 7.449 1.142 0.254 -6.095 23.104\n",
"EVANSDALE -10.0774 9.370 -1.075 0.282 -28.443 8.288\n",
"EVERLY 4.7498 76.976 0.062 0.951 -146.122 155.621\n",
"EXIRA -23.9271 43.434 -0.551 0.582 -109.057 61.203\n",
"FAIRBANK 5.2703 21.998 0.240 0.811 -37.846 48.387\n",
"FAIRFAX -6.9754 33.965 -0.205 0.837 -73.545 59.595\n",
"FAIRFIELD 2.6637 7.290 0.365 0.715 -11.625 16.952\n",
"FARLEY -6.3658 37.837 -0.168 0.866 -80.525 67.794\n",
"FARMINGTON 12.0277 36.022 0.334 0.738 -58.574 82.629\n",
"FAYETTE -4.8607 24.550 -0.198 0.843 -52.978 43.257\n",
"FLOYD 26.8015 17.773 1.508 0.132 -8.034 61.637\n",
"FONDA -16.0151 83.142 -0.193 0.847 -178.972 146.942\n",
"FONTANELLE 5.4821 25.896 0.212 0.832 -45.273 56.237\n",
"FOREST CITY 2.5327 8.460 0.299 0.765 -14.048 19.114\n",
"FORT ATKINSON 18.6753 16.154 1.156 0.248 -12.985 50.336\n",
"FORT DODGE 6.7896 3.964 1.713 0.087 -0.979 14.558\n",
"FORT MADISON 4.3457 5.635 0.771 0.441 -6.698 15.390\n",
"FREDERICKSBURG 20.6748 23.095 0.895 0.371 -24.591 65.941\n",
"GARNER -11.0150 15.407 -0.715 0.475 -41.211 19.181\n",
"GEORGE 14.2376 18.637 0.764 0.445 -22.290 50.765\n",
"GILBERTVILLE 25.6181 83.142 0.308 0.758 -137.339 188.575\n",
"GILMORE CITY -33.7885 50.925 -0.663 0.507 -133.601 66.024\n",
"GLADBROOK 22.4710 23.709 0.948 0.343 -23.999 68.941\n",
"GLENWOOD -2.3770 10.889 -0.218 0.827 -23.719 18.965\n",
"GLIDDEN 16.4634 28.003 0.588 0.557 -38.421 71.348\n",
"GOLDFIELD -73.1030 52.594 -1.390 0.165 -176.186 29.980\n",
"GOWRIE 2.4441 14.533 0.168 0.866 -26.040 30.928\n",
"GRAETTINGER 11.3875 20.719 0.550 0.583 -29.220 51.995\n",
"GRAND JUNCTION -23.8708 38.505 -0.620 0.535 -99.340 51.599\n",
"GRAND MOUND 11.7441 25.489 0.461 0.645 -38.213 61.701\n",
"GRANGER 2.4954 25.896 0.096 0.923 -48.259 53.250\n",
"GREENE 10.9579 19.823 0.553 0.580 -27.896 49.811\n",
"GREENFIELD 4.3470 14.680 0.296 0.767 -24.426 33.120\n",
"GRIMES 10.5076 5.365 1.958 0.050 -0.008 21.023\n",
"GRINNELL 12.1846 5.451 2.235 0.025 1.500 22.869\n",
"GRISWOLD 2.1828 91.076 0.024 0.981 -176.323 180.689\n",
"GRUNDY CENTER 14.6975 10.109 1.454 0.146 -5.116 34.510\n",
"GUTHRIE CENTER 17.6120 17.386 1.013 0.311 -16.463 51.687\n",
"GUTTENBERG 4.9218 12.897 0.382 0.703 -20.355 30.199\n",
"GUTTENBURG 20.4662 20.406 1.003 0.316 -19.530 60.462\n",
"HAMBURG -22.5931 39.211 -0.576 0.564 -99.446 54.260\n",
"HAMPTON 5.1508 7.985 0.645 0.519 -10.499 20.801\n",
"HARLAN 19.5010 8.031 2.428 0.015 3.761 35.241\n",
"HARPERS FERRY -21.1814 29.421 -0.720 0.472 -78.847 36.484\n",
"HARTLEY 21.5626 11.891 1.813 0.070 -1.744 44.869\n",
"HAWARDEN 6.4730 15.539 0.417 0.677 -23.982 36.929\n",
"HAZLETON -138.5507 36.598 -3.786 0.000 -210.281 -66.820\n",
"HIAWATHA -24.0826 10.358 -2.325 0.020 -44.384 -3.781\n",
"HOLSTEIN 12.6384 11.461 1.103 0.270 -9.825 35.102\n",
"HOLY CROSS 17.5875 26.322 0.668 0.504 -34.003 69.178\n",
"HOSPERS 23.5566 39.211 0.601 0.548 -53.297 100.410\n",
"HUBBARD 21.4838 23.244 0.924 0.355 -24.075 67.042\n",
"HUDSON 5.3643 32.224 0.166 0.868 -57.794 68.523\n",
"HUMBOLDT 11.7142 8.808 1.330 0.184 -5.548 28.977\n",
"HUMESTON -12.5408 37.202 -0.337 0.736 -85.455 60.374\n",
"HUXLEY 13.0762 18.714 0.699 0.485 -23.603 49.755\n",
"IDA GROVE 14.2222 11.568 1.229 0.219 -8.450 36.895\n",
"INDEPENDENCE 16.3160 6.928 2.355 0.019 2.738 29.894\n",
"INDIANOLA 3.8883 5.030 0.773 0.439 -5.970 13.747\n",
"INWOOD 15.1291 13.436 1.126 0.260 -11.204 41.463\n",
"IOWA CITY 8.3816 2.642 3.172 0.002 3.203 13.560\n",
"IOWA FALLS 18.5881 7.904 2.352 0.019 3.097 34.080\n",
"IRETON 15.8366 33.964 0.466 0.641 -50.733 82.406\n",
"Inwood 21.0536 36.597 0.575 0.565 -50.676 92.783\n",
"JEFFERSON 12.2832 8.272 1.485 0.138 -3.930 28.497\n",
"JESUP 9.8436 15.195 0.648 0.517 -19.937 39.625\n",
"JEWELL 22.1007 21.999 1.005 0.315 -21.017 65.218\n",
"JOHNSTON 16.1114 4.602 3.501 0.000 7.091 25.132\n",
"KELLOG 10.5823 45.553 0.232 0.816 -78.700 99.865\n",
"KELLOGG 5.9998 22.665 0.265 0.791 -38.423 50.423\n",
"KEOKUK 20.4061 5.105 3.997 0.000 10.400 30.412\n",
"KEOSAUQUA 18.9282 14.016 1.350 0.177 -8.543 46.399\n",
"KEOTA 4.9876 30.728 0.162 0.871 -55.238 65.213\n",
"KINGSLEY 1.6400 28.828 0.057 0.955 -54.863 58.143\n",
"KNOXVILLE 12.4646 5.619 2.218 0.027 1.452 23.477\n",
"LA PORTE CITY 16.4268 12.999 1.264 0.206 -9.052 41.905\n",
"LAKE CITY 10.7820 17.448 0.618 0.537 -23.416 44.980\n",
"LAKE MILLS 3.5394 14.250 0.248 0.804 -24.390 31.469\n",
"LAKE PARK 16.7145 35.472 0.471 0.637 -52.810 86.239\n",
"LAKE VIEW 0.8195 14.680 0.056 0.955 -27.953 29.592\n",
"LAMONI 12.2978 19.824 0.620 0.535 -26.556 51.151\n",
"LANSING 10.4263 16.680 0.625 0.532 -22.266 43.118\n",
"LARCHWOOD 8.2965 11.755 0.706 0.480 -14.742 31.336\n",
"LATIMER 17.8635 72.006 0.248 0.804 -123.267 158.994\n",
"LAURENS 10.1463 14.911 0.680 0.496 -19.079 39.372\n",
"LAWLER 44.4770 31.829 1.397 0.162 -17.908 106.862\n",
"LE CLAIRE 3.4458 12.397 0.278 0.781 -20.851 27.743\n",
"LE GRAND 15.9082 12.243 1.299 0.194 -8.088 39.904\n",
"LE MARS 4.6337 9.990 0.464 0.643 -14.945 24.213\n",
"LECLAIRE 34.4375 11.496 2.996 0.003 11.906 56.969\n",
"LEMARS 14.0501 6.988 2.011 0.044 0.353 27.747\n",
"LENOX 5.1152 17.201 0.297 0.766 -28.598 38.828\n",
"LEON -0.6040 18.872 -0.032 0.974 -37.593 36.386\n",
"LISBON 12.1819 17.323 0.703 0.482 -21.771 46.134\n",
"LOGAN 17.3734 20.109 0.864 0.388 -22.039 56.786\n",
"LOHRVILLE 26.8624 117.573 0.228 0.819 -203.578 257.303\n",
"LOST NATION 5.2824 35.472 0.149 0.882 -64.243 74.807\n",
"LOVILIA -1.3982 61.411 -0.023 0.982 -121.763 118.966\n",
"MADRID 15.4158 15.237 1.012 0.312 -14.447 45.279\n",
"MALVERN -47.8482 37.202 -1.286 0.198 -120.763 25.067\n",
"MANCHESTER 11.0398 7.984 1.383 0.167 -4.608 26.688\n",
"MANLY -18.0850 32.225 -0.561 0.575 -81.244 45.074\n",
"MANNING 9.6254 12.627 0.762 0.446 -15.124 34.375\n",
"MANSON 18.7842 15.584 1.205 0.228 -11.760 49.328\n",
"MAPLETON 9.3938 9.854 0.953 0.340 -9.920 28.708\n",
"MAQUOKETA 15.4257 5.869 2.628 0.009 3.922 26.929\n",
"MARCUS 16.8299 23.244 0.724 0.469 -28.728 62.388\n",
"MARENGO 27.6627 9.956 2.779 0.005 8.150 47.175\n",
"MARION 12.5049 4.296 2.911 0.004 4.086 20.924\n",
"MARSHALLTOWN 8.9378 4.150 2.154 0.031 0.804 17.072\n",
"MARTENSDALE -6.4502 45.552 -0.142 0.887 -95.732 82.831\n",
"MASON CITY -0.5532 3.439 -0.161 0.872 -7.294 6.188\n",
"MAXWELL 16.8655 39.957 0.422 0.673 -61.450 95.181\n",
"MECHANICSVILLE -42.9215 36.022 -1.192 0.233 -113.523 27.680\n",
"MEDIAPOLIS 5.8980 11.076 0.533 0.594 -15.811 27.607\n",
"MELBOURNE 18.2901 83.142 0.220 0.826 -144.666 181.246\n",
"MELCHER-DALLAS 6.9854 15.629 0.447 0.655 -23.646 37.617\n",
"MERRILL 1.0184 64.407 0.016 0.987 -125.218 127.255\n",
"MILFORD 15.0328 6.742 2.230 0.026 1.819 28.247\n",
"MINDEN 14.2626 67.890 0.210 0.834 -118.799 147.325\n",
"MISSOURI VALLEY 10.5286 8.062 1.306 0.192 -5.273 26.330\n",
"MONONA 3.1677 9.877 0.321 0.748 -16.191 22.526\n",
"MONROE 9.5374 18.560 0.514 0.607 -26.840 45.914\n",
"MONTEZUMA 11.2643 11.044 1.020 0.308 -10.381 32.910\n",
"MONTICELLO 14.6337 5.830 2.510 0.012 3.208 26.060\n",
"MONTROSE -14.5274 33.061 -0.439 0.660 -79.325 50.271\n",
"MORAVIA 7.9187 24.913 0.318 0.751 -40.911 56.748\n",
"MOUNT AYR 20.2394 14.425 1.403 0.161 -8.032 48.511\n",
"MOUNT PLEASANT 15.2371 6.815 2.236 0.025 1.879 28.595\n",
"MOUNT VERNON 8.7258 6.191 1.409 0.159 -3.409 20.860\n",
"MT PLEASANT 21.9054 15.583 1.406 0.160 -8.637 52.448\n",
"MT VERNON -35.2418 24.036 -1.466 0.143 -82.351 11.867\n",
"MUSCATINE 10.5240 3.742 2.812 0.005 3.189 17.859\n",
"NASHUA 11.7494 21.998 0.534 0.593 -31.367 54.866\n",
"NEOLA 23.4626 25.690 0.913 0.361 -26.889 73.814\n",
"NEVADA 9.0361 6.831 1.323 0.186 -4.353 22.425\n",
"NEW HAMPTON 10.6192 12.723 0.835 0.404 -14.317 35.556\n",
"NEW SHARON 11.7852 38.505 0.306 0.760 -63.684 87.255\n",
"NEW VIRGINIA 17.2334 34.446 0.500 0.617 -50.279 84.746\n",
"NEWTON 5.1702 4.256 1.215 0.224 -3.171 13.512\n",
"NORA SPRINGS 6.5939 30.727 0.215 0.830 -53.631 66.819\n",
"NORTH ENGLISH 20.1190 36.022 0.559 0.576 -50.484 90.721\n",
"NORTH LIBERTY -0.5936 5.789 -0.103 0.918 -11.940 10.753\n",
"NORTHWOOD 16.5553 13.919 1.189 0.234 -10.726 43.837\n",
"NORWALK 23.7449 9.628 2.466 0.014 4.875 42.615\n",
"Northwood 27.3407 17.841 1.532 0.125 -7.626 62.308\n",
"OAKLAND 3.9275 12.821 0.306 0.759 -21.202 29.057\n",
"OELWEIN 8.9083 8.398 1.061 0.289 -7.551 25.368\n",
"OGDEN 9.9022 18.409 0.538 0.591 -26.179 45.983\n",
"OKOBOJI 13.8150 32.224 0.429 0.668 -49.344 76.974\n",
"ONAWA 6.4833 7.236 0.896 0.370 -7.698 20.665\n",
"ORANGE CITY 31.1245 11.870 2.622 0.009 7.859 54.390\n",
"OSAGE 13.0410 7.368 1.770 0.077 -1.399 27.481\n",
"OSCEOLA 20.6072 7.843 2.627 0.009 5.235 35.980\n",
"OSKALOOSA 3.0222 5.400 0.560 0.576 -7.561 13.605\n",
"OTHO -21.9789 45.553 -0.482 0.629 -111.261 67.303\n",
"OTTUMWA 17.3559 4.459 3.893 0.000 8.617 26.095\n",
"OTTUWMA 19.6167 6.859 2.860 0.004 6.173 33.060\n",
"PACIFIC JUNCTION -33.2145 18.484 -1.797 0.072 -69.442 3.013\n",
"PALO -26.9541 33.060 -0.815 0.415 -91.750 37.842\n",
"PANORA 6.4980 12.651 0.514 0.607 -18.297 31.293\n",
"PARKERSBURG 11.0946 22.127 0.501 0.616 -32.273 54.462\n",
"PAULLINA -4.4437 18.048 -0.246 0.806 -39.817 30.929\n",
"PELLA 21.8905 7.074 3.095 0.002 8.027 35.754\n",
"PEOSTA 13.3592 27.244 0.490 0.624 -40.038 66.756\n",
"PERRY 11.9243 7.265 1.641 0.101 -2.315 26.164\n",
"PLEASANT HILL 0.2158 6.905 0.031 0.975 -13.319 13.750\n",
"PLEASANTVILLE -10.8642 16.005 -0.679 0.497 -42.234 20.506\n",
"POCAHONTAS 13.8810 11.891 1.167 0.243 -9.424 37.186\n",
"POLK CITY 16.2014 12.396 1.307 0.191 -8.094 40.497\n",
"POSTVILLE -1.3631 30.053 -0.045 0.964 -60.266 57.540\n",
"PRAIRIE CITY -15.9101 35.473 -0.449 0.654 -85.436 53.616\n",
"PRIMGHAR 5.7114 19.640 0.291 0.771 -32.782 44.205\n",
"PRINCETON 9.8593 32.635 0.302 0.763 -54.104 73.822\n",
"RAYMOND 0.4309 17.022 0.025 0.980 -32.931 33.793\n",
"RED OAK 3.0798 7.348 0.419 0.675 -11.322 17.482\n",
"REINBECK 5.7782 35.472 0.163 0.871 -63.747 75.303\n",
"REMSEN 15.5809 17.841 0.873 0.382 -19.387 50.549\n",
"RICEVILLE 13.8661 35.472 0.391 0.696 -55.659 83.391\n",
"RIVERSIDE 17.8288 13.731 1.298 0.194 -9.084 44.741\n",
"ROBINS 25.6121 143.994 0.178 0.859 -256.613 307.837\n",
"ROCK RAPIDS 3.8871 9.370 0.415 0.678 -14.478 22.252\n",
"ROCK VALLEY 5.8394 21.045 0.277 0.781 -35.408 47.087\n",
"ROCKWELL 21.8659 16.104 1.358 0.175 -9.698 53.429\n",
"ROCKWELL CITY 17.4914 19.288 0.907 0.364 -20.313 55.296\n",
"ROLFE 15.4339 34.948 0.442 0.659 -53.063 83.930\n",
"RUTHVEN 9.3762 30.053 0.312 0.755 -49.527 68.280\n",
"SAC CITY 3.5511 8.159 0.435 0.663 -12.441 19.543\n",
"SANBORN 22.6244 15.408 1.468 0.142 -7.574 52.823\n",
"SCHALLER 8.8142 52.594 0.168 0.867 -94.268 111.897\n",
"SCHLESWIG 3.5061 14.183 0.247 0.805 -24.292 31.304\n",
"SCRANTON 4.1263 40.748 0.101 0.919 -75.739 83.991\n",
"SERGEANT BLUFF 9.4991 10.586 0.897 0.370 -11.249 30.247\n",
"SHEFFIELD -16.7184 39.211 -0.426 0.670 -93.572 60.135\n",
"SHELDON 18.7288 6.652 2.815 0.005 5.691 31.767\n",
"SHELLSBURG 11.3929 11.306 1.008 0.314 -10.767 33.553\n",
"SHENANDOAH 15.2834 7.973 1.917 0.055 -0.342 30.909\n",
"SIBLEY 10.3781 10.950 0.948 0.343 -11.084 31.840\n",
"SIGOURNEY 14.8261 11.852 1.251 0.211 -8.403 38.056\n",
"SIOUX CENTER 31.8050 7.810 4.072 0.000 16.497 47.113\n",
"SIOUX CITY 7.8343 2.649 2.958 0.003 2.643 13.025\n",
"SIOUX RAPIDS 8.1861 14.425 0.567 0.570 -20.086 36.459\n",
"SLATER 7.9728 25.292 0.315 0.753 -41.600 57.545\n",
"SLOAN 16.8442 19.203 0.877 0.380 -20.792 54.481\n",
"SOLON 9.0008 15.628 0.576 0.565 -21.630 39.632\n",
"SPENCER 9.2005 4.846 1.899 0.058 -0.297 18.698\n",
"SPIRIT LAKE 13.5214 5.189 2.606 0.009 3.352 23.691\n",
"SPRINGVILLE 4.1382 39.211 0.106 0.916 -72.715 80.991\n",
"ST ANSGAR -12.1602 14.951 -0.813 0.416 -41.463 17.143\n",
"ST CHARLES 0.6915 38.506 0.018 0.986 -74.778 76.161\n",
"ST LUCAS 18.4430 27.006 0.683 0.495 -34.487 71.373\n",
"STANWOOD -86.2965 67.890 -1.271 0.204 -219.359 46.766\n",
"STATE CENTER 25.8882 15.676 1.652 0.099 -4.835 56.612\n",
"STORM LAKE 5.5457 4.268 1.299 0.194 -2.820 13.911\n",
"STORY CITY 13.1286 12.974 1.012 0.312 -12.300 38.557\n",
"STRATFORD 1.0744 22.949 0.047 0.963 -43.905 46.054\n",
"STRAWBERRY POINT 10.7485 26.771 0.402 0.688 -41.722 63.219\n",
"STUART 7.9827 12.135 0.658 0.511 -15.802 31.768\n",
"SULLY 1.1542 58.798 0.020 0.984 -114.088 116.397\n",
"SUMNER 8.0987 11.222 0.722 0.471 -13.897 30.094\n",
"SUTHERLAND -6.2071 34.445 -0.180 0.857 -73.719 61.305\n",
"SWEA CITY -0.6337 35.472 -0.018 0.986 -70.158 68.891\n",
"SWISHER 58.1100 13.131 4.425 0.000 32.373 83.847\n",
"TIFFIN 5.0404 17.774 0.284 0.777 -29.796 39.877\n",
"TIPTON 16.0052 9.177 1.744 0.081 -1.981 33.991\n",
"TOLEDO 11.9659 7.454 1.605 0.108 -2.643 26.575\n",
"TRAER -5.3410 15.450 -0.346 0.730 -35.623 24.942\n",
"TREYNOR -3.9643 41.587 -0.095 0.924 -85.474 77.545\n",
"TRIPOLI 15.1197 13.266 1.140 0.254 -10.882 41.122\n",
"URBANA -1.5880 37.837 -0.042 0.967 -75.747 72.571\n",
"URBANDALE 12.8917 4.344 2.968 0.003 4.378 21.405\n",
"Urbandale 3.6420 33.060 0.110 0.912 -61.154 68.438\n",
"VAN METER -84.4213 52.594 -1.605 0.108 -187.504 18.662\n",
"VICTOR 18.0625 25.489 0.709 0.479 -31.895 68.020\n",
"VILLISCA 1.0963 43.434 0.025 0.980 -84.034 86.227\n",
"VINTON 17.6707 14.250 1.240 0.215 -10.258 45.599\n",
"WALFORD 16.5690 37.201 0.445 0.656 -56.345 89.483\n",
"WALKER -16.2928 45.553 -0.358 0.721 -105.574 72.989\n",
"WALL LAKE 13.4953 54.439 0.248 0.804 -93.203 120.193\n",
"WALNUT 0.8355 28.270 0.030 0.976 -54.572 56.243\n",
"WAPELLO 13.6309 14.681 0.928 0.353 -15.143 42.405\n",
"WASHBURN -64.1395 52.594 -1.220 0.223 -167.222 38.943\n",
"WASHINGTON 2.8315 5.756 0.492 0.623 -8.450 14.113\n",
"WATERLOO -22.7595 2.590 -8.787 0.000 -27.836 -17.683\n",
"WAUKEE 18.2832 5.611 3.258 0.001 7.285 29.281\n",
"WAUKON 10.8178 7.321 1.478 0.140 -3.532 25.167\n",
"WAVERLY 10.9909 5.350 2.054 0.040 0.505 21.477\n",
"WEBSTER CITY 10.4118 7.008 1.486 0.137 -3.323 24.147\n",
"WELLMAN 7.5048 13.078 0.574 0.566 -18.128 33.137\n",
"WELLSBURG -29.4621 44.456 -0.663 0.508 -116.594 57.670\n",
"WESLEY 9.1705 17.261 0.531 0.595 -24.661 43.002\n",
"WEST BEND 11.4648 19.823 0.578 0.563 -27.388 50.317\n",
"WEST BRANCH 22.8898 12.652 1.809 0.070 -1.908 47.687\n",
"WEST BURLINGTON 14.0926 8.509 1.656 0.098 -2.584 30.770\n",
"WEST DES MOINES 30.2788 2.750 11.011 0.000 24.889 35.669\n",
"WEST LIBERTY 8.6568 12.419 0.697 0.486 -15.683 32.997\n",
"WEST POINT 17.8887 15.450 1.158 0.247 -12.393 48.171\n",
"WEST UNION 10.8489 11.172 0.971 0.332 -11.048 32.746\n",
"WHEATLAND 11.2630 39.957 0.282 0.778 -67.053 89.578\n",
"WILLIAMSBURG 16.4116 12.626 1.300 0.194 -8.336 41.159\n",
"WILTON 20.7052 11.570 1.790 0.074 -1.971 43.382\n",
"WINDSOR HEIGHTS 32.6892 4.072 8.028 0.000 24.708 40.670\n",
"WINTERSET 5.3052 7.402 0.717 0.474 -9.202 19.813\n",
"WINTHROP 15.6299 28.002 0.558 0.577 -39.254 70.514\n",
"WOODBINE 19.7643 16.411 1.204 0.228 -12.400 51.929\n",
"WOODWARD 11.6388 34.446 0.338 0.735 -55.874 79.151\n",
"ZWINGLE -15.5083 28.270 -0.549 0.583 -70.916 39.900\n",
"==============================================================================\n",
"Omnibus: 364812.861 Durbin-Watson: 2.001\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 67102567296.771\n",
"Skew: 5.870 Prob(JB): 0.00\n",
"Kurtosis: 2448.603 Cond. No. 1.39e+04\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"[2] The condition number is large, 1.39e+04. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n",
"\"\"\""
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# City Statistics\n",
"model_city.summary()"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {
"collapsed": true
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"OLS Regression Results\n",
"\n",
" Dep. Variable: | sale_dollars | R-squared: | 0.719 | \n",
"
\n",
"\n",
" Model: | OLS | Adj. R-squared: | 0.719 | \n",
"
\n",
"\n",
" Method: | Least Squares | F-statistic: | 1670. | \n",
"
\n",
"\n",
" Date: | Sun, 22 Oct 2017 | Prob (F-statistic): | 0.00 | \n",
"
\n",
"\n",
" Time: | 22:32:28 | Log-Likelihood: | -1.8128e+06 | \n",
"
\n",
"\n",
" No. Observations: | 269258 | AIC: | 3.626e+06 | \n",
"
\n",
"\n",
" Df Residuals: | 268844 | BIC: | 3.631e+06 | \n",
"
\n",
"\n",
" Df Model: | 413 | | | \n",
"
\n",
"\n",
" Covariance Type: | nonrobust | | | \n",
"
\n",
"
\n",
"\n",
"\n",
" | coef | std err | t | P>|t| | [0.025 | 0.975] | \n",
"
\n",
"\n",
" const | -96.5788 | 2.494 | -38.719 | 0.000 | -101.468 | -91.690 | \n",
"
\n",
"\n",
" state_bottle_retail | 6.7432 | 0.038 | 179.585 | 0.000 | 6.670 | 6.817 | \n",
"
\n",
"\n",
" bottles_sold | 13.3491 | 0.017 | 808.482 | 0.000 | 13.317 | 13.381 | \n",
"
\n",
"\n",
" 50002 | -29.9087 | 31.092 | -0.962 | 0.336 | -90.848 | 31.030 | \n",
"
\n",
"\n",
" 50003 | 8.2845 | 11.652 | 0.711 | 0.477 | -14.554 | 31.123 | \n",
"
\n",
"\n",
" 50006 | -8.8378 | 18.561 | -0.476 | 0.634 | -45.216 | 27.541 | \n",
"
\n",
"\n",
" 50009 | -4.3451 | 5.048 | -0.861 | 0.389 | -14.240 | 5.550 | \n",
"
\n",
"\n",
" 50014 | -22.4837 | 9.811 | -2.292 | 0.022 | -41.712 | -3.255 | \n",
"
\n",
"\n",
" 50020 | -3.5390 | 16.882 | -0.210 | 0.834 | -36.628 | 29.550 | \n",
"
\n",
"\n",
" 50021 | 19.1865 | 4.715 | 4.069 | 0.000 | 9.945 | 28.428 | \n",
"
\n",
"\n",
" 50022 | -3.0223 | 6.759 | -0.447 | 0.655 | -16.269 | 10.225 | \n",
"
\n",
"\n",
" 50023 | -12.3539 | 4.878 | -2.533 | 0.011 | -21.914 | -2.794 | \n",
"
\n",
"\n",
" 50025 | -1.3936 | 14.401 | -0.097 | 0.923 | -29.619 | 26.832 | \n",
"
\n",
"\n",
" 50028 | 7.1533 | 26.799 | 0.267 | 0.790 | -45.373 | 59.679 | \n",
"
\n",
"\n",
" 50033 | 0.1676 | 23.753 | 0.007 | 0.994 | -46.387 | 46.722 | \n",
"
\n",
"\n",
" 50035 | 4.8937 | 10.940 | 0.447 | 0.655 | -16.548 | 26.336 | \n",
"
\n",
"\n",
" 50036 | 0.6791 | 5.877 | 0.116 | 0.908 | -10.839 | 12.197 | \n",
"
\n",
"\n",
" 50044 | -154.1943 | 50.875 | -3.031 | 0.002 | -253.907 | -54.481 | \n",
"
\n",
"\n",
" 50046 | 7.6630 | 28.565 | 0.268 | 0.788 | -48.324 | 63.650 | \n",
"
\n",
"\n",
" 50047 | -5.8497 | 15.301 | -0.382 | 0.702 | -35.839 | 24.140 | \n",
"
\n",
"\n",
" 50048 | -31.6252 | 33.504 | -0.944 | 0.345 | -97.292 | 34.042 | \n",
"
\n",
"\n",
" 50049 | -0.3689 | 9.634 | -0.038 | 0.969 | -19.252 | 18.514 | \n",
"
\n",
"\n",
" 50054 | -0.8693 | 14.868 | -0.058 | 0.953 | -30.010 | 28.271 | \n",
"
\n",
"\n",
" 50056 | -21.3511 | 39.193 | -0.545 | 0.586 | -98.168 | 55.465 | \n",
"
\n",
"\n",
" 50058 | -10.8854 | 18.867 | -0.577 | 0.564 | -47.865 | 26.094 | \n",
"
\n",
"\n",
" 50060 | -11.7605 | 17.990 | -0.654 | 0.513 | -47.021 | 23.500 | \n",
"
\n",
"\n",
" 50061 | -141.0714 | 83.019 | -1.699 | 0.089 | -303.786 | 21.643 | \n",
"
\n",
"\n",
" 50069 | -6.6335 | 17.856 | -0.371 | 0.710 | -41.631 | 28.364 | \n",
"
\n",
"\n",
" 50071 | -22.8323 | 56.427 | -0.405 | 0.686 | -133.428 | 87.763 | \n",
"
\n",
"\n",
" 50072 | -2.4535 | 33.504 | -0.073 | 0.942 | -68.120 | 63.213 | \n",
"
\n",
"\n",
" 50075 | 7.8137 | 21.330 | 0.366 | 0.714 | -33.992 | 49.619 | \n",
"
\n",
"\n",
" 50076 | -37.6038 | 43.403 | -0.866 | 0.386 | -122.673 | 47.466 | \n",
"
\n",
"\n",
" 50107 | -37.4722 | 38.489 | -0.974 | 0.330 | -112.910 | 37.965 | \n",
"
\n",
"\n",
" 50109 | -11.3427 | 25.928 | -0.437 | 0.662 | -62.161 | 39.475 | \n",
"
\n",
"\n",
" 50111 | -3.0709 | 5.723 | -0.537 | 0.592 | -14.288 | 8.147 | \n",
"
\n",
"\n",
" 50112 | -1.4448 | 5.804 | -0.249 | 0.803 | -12.820 | 9.930 | \n",
"
\n",
"\n",
" 50115 | 3.8363 | 17.471 | 0.220 | 0.826 | -30.407 | 38.080 | \n",
"
\n",
"\n",
" 50122 | 7.6937 | 23.290 | 0.330 | 0.741 | -37.954 | 53.342 | \n",
"
\n",
"\n",
" 50123 | -26.4626 | 37.190 | -0.712 | 0.477 | -99.354 | 46.429 | \n",
"
\n",
"\n",
" 50124 | -0.5241 | 18.789 | -0.028 | 0.978 | -37.350 | 36.302 | \n",
"
\n",
"\n",
" 50125 | -9.7245 | 5.412 | -1.797 | 0.072 | -20.332 | 0.883 | \n",
"
\n",
"\n",
" 50126 | 5.1077 | 8.144 | 0.627 | 0.531 | -10.855 | 21.070 | \n",
"
\n",
"\n",
" 50129 | 0.4383 | 7.387 | 0.059 | 0.953 | -14.039 | 14.916 | \n",
"
\n",
"\n",
" 50130 | 8.1887 | 22.052 | 0.371 | 0.710 | -35.033 | 51.410 | \n",
"
\n",
"\n",
" 50131 | 2.4712 | 5.018 | 0.493 | 0.622 | -7.363 | 12.306 | \n",
"
\n",
"\n",
" 50135 | -7.7712 | 22.714 | -0.342 | 0.732 | -52.290 | 36.748 | \n",
"
\n",
"\n",
" 50136 | -3.3111 | 45.516 | -0.073 | 0.942 | -92.520 | 85.898 | \n",
"
\n",
"\n",
" 50138 | -1.2468 | 5.961 | -0.209 | 0.834 | -12.930 | 10.436 | \n",
"
\n",
"\n",
" 50140 | -1.5484 | 19.891 | -0.078 | 0.938 | -40.534 | 37.437 | \n",
"
\n",
"\n",
" 50142 | 2.1466 | 12.386 | 0.173 | 0.862 | -22.130 | 26.423 | \n",
"
\n",
"\n",
" 50144 | -14.2001 | 18.946 | -0.749 | 0.454 | -51.335 | 22.934 | \n",
"
\n",
"\n",
" 50150 | -15.2315 | 61.334 | -0.248 | 0.804 | -135.444 | 104.981 | \n",
"
\n",
"\n",
" 50156 | 1.6367 | 15.342 | 0.107 | 0.915 | -28.434 | 31.707 | \n",
"
\n",
"\n",
" 50158 | -4.6139 | 4.609 | -1.001 | 0.317 | -13.648 | 4.420 | \n",
"
\n",
"\n",
" 50160 | -20.0637 | 45.515 | -0.441 | 0.659 | -109.272 | 69.145 | \n",
"
\n",
"\n",
" 50161 | 3.1519 | 39.936 | 0.079 | 0.937 | -75.122 | 81.426 | \n",
"
\n",
"\n",
" 50162 | 4.6876 | 83.017 | 0.056 | 0.955 | -158.023 | 167.398 | \n",
"
\n",
"\n",
" 50163 | -6.5949 | 15.731 | -0.419 | 0.675 | -37.426 | 24.237 | \n",
"
\n",
"\n",
" 50170 | -4.2230 | 18.636 | -0.227 | 0.821 | -40.749 | 32.303 | \n",
"
\n",
"\n",
" 50171 | -2.3062 | 11.207 | -0.206 | 0.837 | -24.272 | 19.659 | \n",
"
\n",
"\n",
" 50201 | -4.6950 | 7.111 | -0.660 | 0.509 | -18.632 | 9.242 | \n",
"
\n",
"\n",
" 50207 | -1.8600 | 38.489 | -0.048 | 0.961 | -77.297 | 73.577 | \n",
"
\n",
"\n",
" 50208 | -4.7194 | 4.871 | -0.969 | 0.333 | -14.266 | 4.827 | \n",
"
\n",
"\n",
" 50210 | 3.4820 | 34.443 | 0.101 | 0.919 | -64.025 | 70.989 | \n",
"
\n",
"\n",
" 50211 | 10.1313 | 9.821 | 1.032 | 0.302 | -9.118 | 29.380 | \n",
"
\n",
"\n",
" 50212 | -3.7959 | 18.486 | -0.205 | 0.837 | -40.029 | 32.437 | \n",
"
\n",
"\n",
" 50213 | 7.0022 | 8.085 | 0.866 | 0.386 | -8.845 | 22.849 | \n",
"
\n",
"\n",
" 50216 | -7.1645 | 12.788 | -0.560 | 0.575 | -32.229 | 17.900 | \n",
"
\n",
"\n",
" 50219 | 8.3708 | 7.344 | 1.140 | 0.254 | -6.023 | 22.765 | \n",
"
\n",
"\n",
" 50220 | -1.7786 | 7.528 | -0.236 | 0.813 | -16.533 | 12.976 | \n",
"
\n",
"\n",
" 50225 | -24.5009 | 16.103 | -1.521 | 0.128 | -56.063 | 7.061 | \n",
"
\n",
"\n",
" 50226 | 2.5729 | 12.537 | 0.205 | 0.837 | -22.000 | 27.145 | \n",
"
\n",
"\n",
" 50228 | -29.7824 | 35.467 | -0.840 | 0.401 | -99.296 | 39.731 | \n",
"
\n",
"\n",
" 50240 | -13.0806 | 38.489 | -0.340 | 0.734 | -88.519 | 62.357 | \n",
"
\n",
"\n",
" 50244 | -5.7635 | 25.327 | -0.228 | 0.820 | -55.405 | 43.877 | \n",
"
\n",
"\n",
" 50247 | 12.0218 | 15.777 | 0.762 | 0.446 | -18.900 | 42.944 | \n",
"
\n",
"\n",
" 50248 | 1.2860 | 11.399 | 0.113 | 0.910 | -21.056 | 23.628 | \n",
"
\n",
"\n",
" 50249 | -12.3985 | 22.996 | -0.539 | 0.590 | -57.471 | 32.674 | \n",
"
\n",
"\n",
" 50250 | -5.7329 | 12.281 | -0.467 | 0.641 | -29.803 | 18.337 | \n",
"
\n",
"\n",
" 50251 | -12.4684 | 58.727 | -0.212 | 0.832 | -127.572 | 102.635 | \n",
"
\n",
"\n",
" 50261 | -98.1211 | 52.538 | -1.868 | 0.062 | -201.095 | 4.853 | \n",
"
\n",
"\n",
" 50263 | 4.7477 | 5.954 | 0.797 | 0.425 | -6.923 | 16.418 | \n",
"
\n",
"\n",
" 50265 | -1.8658 | 3.914 | -0.477 | 0.634 | -9.538 | 5.806 | \n",
"
\n",
"\n",
" 50266 | 45.0208 | 4.514 | 9.973 | 0.000 | 36.173 | 53.868 | \n",
"
\n",
"\n",
" 50273 | -8.2800 | 7.660 | -1.081 | 0.280 | -23.293 | 6.733 | \n",
"
\n",
"\n",
" 50276 | -2.1760 | 34.443 | -0.063 | 0.950 | -69.683 | 65.331 | \n",
"
\n",
"\n",
" 50300 | -12.2810 | 16.663 | -0.737 | 0.461 | -44.940 | 20.378 | \n",
"
\n",
"\n",
" 50309 | -102.6366 | 10.485 | -9.789 | 0.000 | -123.187 | -82.086 | \n",
"
\n",
"\n",
" 50310 | -31.7304 | 4.916 | -6.454 | 0.000 | -41.366 | -22.095 | \n",
"
\n",
"\n",
" 50311 | 7.3928 | 4.249 | 1.740 | 0.082 | -0.935 | 15.721 | \n",
"
\n",
"\n",
" 50312 | -15.1329 | 6.365 | -2.377 | 0.017 | -27.609 | -2.657 | \n",
"
\n",
"\n",
" 50313 | -26.0639 | 7.411 | -3.517 | 0.000 | -40.590 | -11.538 | \n",
"
\n",
"\n",
" 50314 | 5.2666 | 3.875 | 1.359 | 0.174 | -2.329 | 12.862 | \n",
"
\n",
"\n",
" 50315 | -23.8340 | 4.384 | -5.437 | 0.000 | -32.426 | -15.242 | \n",
"
\n",
"\n",
" 50316 | -60.5910 | 6.187 | -9.793 | 0.000 | -72.717 | -48.465 | \n",
"
\n",
"\n",
" 50317 | -25.3308 | 3.896 | -6.501 | 0.000 | -32.968 | -17.694 | \n",
"
\n",
"\n",
" 50320 | 32.8486 | 4.323 | 7.599 | 0.000 | 24.376 | 41.321 | \n",
"
\n",
"\n",
" 50321 | -1.1673 | 5.480 | -0.213 | 0.831 | -11.908 | 9.573 | \n",
"
\n",
"\n",
" 50322 | -3.8985 | 4.493 | -0.868 | 0.386 | -12.704 | 4.907 | \n",
"
\n",
"\n",
" 50323 | -2.7250 | 15.301 | -0.178 | 0.859 | -32.714 | 27.264 | \n",
"
\n",
"\n",
" 50324 | -25.0191 | 15.262 | -1.639 | 0.101 | -54.932 | 4.894 | \n",
"
\n",
"\n",
" 50325 | -14.1999 | 7.134 | -1.990 | 0.047 | -28.183 | -0.217 | \n",
"
\n",
"\n",
" 50327 | -4.3109 | 18.486 | -0.233 | 0.816 | -40.544 | 31.922 | \n",
"
\n",
"\n",
" 50401 | -14.1112 | 3.984 | -3.542 | 0.000 | -21.919 | -6.303 | \n",
"
\n",
"\n",
" 50421 | -13.2058 | 15.300 | -0.863 | 0.388 | -43.194 | 16.783 | \n",
"
\n",
"\n",
" 50423 | -7.2069 | 16.401 | -0.439 | 0.660 | -39.353 | 24.939 | \n",
"
\n",
"\n",
" 50424 | -3.3914 | 16.717 | -0.203 | 0.839 | -36.157 | 29.374 | \n",
"
\n",
"\n",
" 50428 | -5.5920 | 5.152 | -1.085 | 0.278 | -15.689 | 4.505 | \n",
"
\n",
"\n",
" 50430 | 10.3952 | 37.189 | 0.280 | 0.780 | -62.494 | 83.285 | \n",
"
\n",
"\n",
" 50435 | 13.3039 | 17.856 | 0.745 | 0.456 | -21.693 | 48.301 | \n",
"
\n",
"\n",
" 50436 | -10.9910 | 8.683 | -1.266 | 0.206 | -28.009 | 6.027 | \n",
"
\n",
"\n",
" 50438 | -24.6533 | 15.511 | -1.589 | 0.112 | -55.055 | 5.748 | \n",
"
\n",
"\n",
" 50441 | -8.4437 | 8.222 | -1.027 | 0.304 | -24.558 | 7.671 | \n",
"
\n",
"\n",
" 50450 | -10.1527 | 14.367 | -0.707 | 0.480 | -38.311 | 18.006 | \n",
"
\n",
"\n",
" 50452 | 4.2868 | 71.905 | 0.060 | 0.952 | -136.645 | 145.218 | \n",
"
\n",
"\n",
" 50456 | -31.9335 | 32.230 | -0.991 | 0.322 | -95.103 | 31.236 | \n",
"
\n",
"\n",
" 50458 | -7.1546 | 30.738 | -0.233 | 0.816 | -67.401 | 53.092 | \n",
"
\n",
"\n",
" 50459 | 6.9788 | 11.176 | 0.624 | 0.532 | -14.926 | 28.884 | \n",
"
\n",
"\n",
" 50461 | -0.6935 | 7.626 | -0.091 | 0.928 | -15.640 | 14.253 | \n",
"
\n",
"\n",
" 50466 | 0.2075 | 35.466 | 0.006 | 0.995 | -69.305 | 69.720 | \n",
"
\n",
"\n",
" 50469 | 8.0932 | 16.201 | 0.500 | 0.617 | -23.661 | 39.847 | \n",
"
\n",
"\n",
" 50472 | -25.5296 | 15.059 | -1.695 | 0.090 | -55.046 | 3.987 | \n",
"
\n",
"\n",
" 50475 | -30.4922 | 39.193 | -0.778 | 0.437 | -107.309 | 46.325 | \n",
"
\n",
"\n",
" 50483 | -4.1310 | 17.348 | -0.238 | 0.812 | -38.133 | 29.871 | \n",
"
\n",
"\n",
" 50501 | -6.7320 | 4.443 | -1.515 | 0.130 | -15.441 | 1.977 | \n",
"
\n",
"\n",
" 50511 | -3.0320 | 6.793 | -0.446 | 0.655 | -16.346 | 10.282 | \n",
"
\n",
"\n",
" 50514 | -42.4734 | 49.358 | -0.861 | 0.390 | -139.214 | 54.267 | \n",
"
\n",
"\n",
" 50517 | -4.5785 | 10.826 | -0.423 | 0.672 | -25.797 | 16.640 | \n",
"
\n",
"\n",
" 50525 | -5.4497 | 15.960 | -0.341 | 0.733 | -36.732 | 25.832 | \n",
"
\n",
"\n",
" 50529 | -103.5983 | 40.726 | -2.544 | 0.011 | -183.419 | -23.777 | \n",
"
\n",
"\n",
" 50530 | 5.1168 | 21.216 | 0.241 | 0.809 | -36.465 | 46.699 | \n",
"
\n",
"\n",
" 50533 | 1.4513 | 12.059 | 0.120 | 0.904 | -22.184 | 25.087 | \n",
"
\n",
"\n",
" 50535 | -8.0749 | 36.587 | -0.221 | 0.825 | -79.785 | 63.635 | \n",
"
\n",
"\n",
" 50536 | -6.0549 | 7.057 | -0.858 | 0.391 | -19.886 | 7.776 | \n",
"
\n",
"\n",
" 50540 | -29.3117 | 83.017 | -0.353 | 0.724 | -192.023 | 133.400 | \n",
"
\n",
"\n",
" 50541 | -47.6681 | 50.874 | -0.937 | 0.349 | -147.379 | 52.043 | \n",
"
\n",
"\n",
" 50542 | -86.8969 | 52.539 | -1.654 | 0.098 | -189.871 | 16.077 | \n",
"
\n",
"\n",
" 50543 | -11.1202 | 14.646 | -0.759 | 0.448 | -39.826 | 17.586 | \n",
"
\n",
"\n",
" 50548 | -1.8959 | 9.021 | -0.210 | 0.834 | -19.576 | 15.784 | \n",
"
\n",
"\n",
" 50554 | -3.6233 | 15.021 | -0.241 | 0.809 | -33.063 | 25.817 | \n",
"
\n",
"\n",
" 50563 | 5.0879 | 15.686 | 0.324 | 0.746 | -25.657 | 35.832 | \n",
"
\n",
"\n",
" 50569 | -35.8643 | 45.516 | -0.788 | 0.431 | -125.074 | 53.345 | \n",
"
\n",
"\n",
" 50574 | 0.1913 | 12.040 | 0.016 | 0.987 | -23.406 | 23.789 | \n",
"
\n",
"\n",
" 50579 | 3.5673 | 19.359 | 0.184 | 0.854 | -34.376 | 41.511 | \n",
"
\n",
"\n",
" 50581 | 1.8230 | 34.943 | 0.052 | 0.958 | -66.664 | 70.310 | \n",
"
\n",
"\n",
" 50583 | -10.0252 | 8.390 | -1.195 | 0.232 | -26.470 | 6.420 | \n",
"
\n",
"\n",
" 50585 | -5.4941 | 14.540 | -0.378 | 0.706 | -33.991 | 23.003 | \n",
"
\n",
"\n",
" 50588 | -8.0050 | 4.714 | -1.698 | 0.089 | -17.245 | 1.235 | \n",
"
\n",
"\n",
" 50590 | -14.1602 | 35.466 | -0.399 | 0.690 | -83.672 | 55.352 | \n",
"
\n",
"\n",
" 50595 | -3.2744 | 7.281 | -0.450 | 0.653 | -17.545 | 10.996 | \n",
"
\n",
"\n",
" 50597 | -1.9411 | 19.890 | -0.098 | 0.922 | -40.925 | 37.043 | \n",
"
\n",
"\n",
" 50601 | -1.0390 | 21.445 | -0.048 | 0.961 | -43.070 | 40.992 | \n",
"
\n",
"\n",
" 50602 | 0.6020 | 17.052 | 0.035 | 0.972 | -32.820 | 34.024 | \n",
"
\n",
"\n",
" 50606 | 14.7401 | 23.442 | 0.629 | 0.529 | -31.206 | 60.686 | \n",
"
\n",
"\n",
" 50613 | -5.4575 | 3.699 | -1.475 | 0.140 | -12.707 | 1.792 | \n",
"
\n",
"\n",
" 50616 | -7.3308 | 7.482 | -0.980 | 0.327 | -21.996 | 7.334 | \n",
"
\n",
"\n",
" 50619 | -4.3782 | 25.137 | -0.174 | 0.862 | -53.646 | 44.889 | \n",
"
\n",
"\n",
" 50621 | -2.6679 | 20.571 | -0.130 | 0.897 | -42.987 | 37.651 | \n",
"
\n",
"\n",
" 50622 | 0.0443 | 18.413 | 0.002 | 0.998 | -36.045 | 36.133 | \n",
"
\n",
"\n",
" 50627 | 0.9066 | 11.687 | 0.078 | 0.938 | -22.000 | 23.813 | \n",
"
\n",
"\n",
" 50628 | 16.6751 | 29.438 | 0.566 | 0.571 | -41.022 | 74.372 | \n",
"
\n",
"\n",
" 50629 | -8.3797 | 22.051 | -0.380 | 0.704 | -51.600 | 34.840 | \n",
"
\n",
"\n",
" 50630 | 6.9327 | 23.142 | 0.300 | 0.765 | -38.425 | 52.290 | \n",
"
\n",
"\n",
" 50634 | 11.7941 | 83.017 | 0.142 | 0.887 | -150.917 | 174.505 | \n",
"
\n",
"\n",
" 50635 | 8.6252 | 23.753 | 0.363 | 0.717 | -37.929 | 55.180 | \n",
"
\n",
"\n",
" 50636 | -2.7311 | 19.890 | -0.137 | 0.891 | -41.715 | 36.253 | \n",
"
\n",
"\n",
" 50638 | 0.9179 | 10.291 | 0.089 | 0.929 | -19.251 | 21.087 | \n",
"
\n",
"\n",
" 50641 | -152.2637 | 36.588 | -4.162 | 0.000 | -223.976 | -80.552 | \n",
"
\n",
"\n",
" 50643 | -8.0216 | 32.229 | -0.249 | 0.803 | -71.190 | 55.147 | \n",
"
\n",
"\n",
" 50644 | 2.7410 | 7.204 | 0.380 | 0.704 | -11.379 | 16.861 | \n",
"
\n",
"\n",
" 50647 | -3.3971 | 9.862 | -0.344 | 0.730 | -22.726 | 15.932 | \n",
"
\n",
"\n",
" 50648 | -3.7996 | 15.301 | -0.248 | 0.804 | -33.788 | 26.189 | \n",
"
\n",
"\n",
" 50651 | 2.7037 | 13.132 | 0.206 | 0.837 | -23.034 | 28.442 | \n",
"
\n",
"\n",
" 50658 | -1.9036 | 22.051 | -0.086 | 0.931 | -45.124 | 41.316 | \n",
"
\n",
"\n",
" 50659 | -3.0345 | 12.859 | -0.236 | 0.813 | -28.239 | 22.170 | \n",
"
\n",
"\n",
" 50662 | -4.6819 | 8.624 | -0.543 | 0.587 | -21.584 | 12.220 | \n",
"
\n",
"\n",
" 50665 | -2.5488 | 22.179 | -0.115 | 0.909 | -46.019 | 40.922 | \n",
"
\n",
"\n",
" 50667 | -13.2569 | 17.110 | -0.775 | 0.438 | -46.793 | 20.279 | \n",
"
\n",
"\n",
" 50669 | -7.9526 | 35.466 | -0.224 | 0.823 | -77.465 | 61.560 | \n",
"
\n",
"\n",
" 50674 | -5.5210 | 11.382 | -0.485 | 0.628 | -27.829 | 16.787 | \n",
"
\n",
"\n",
" 50675 | -19.0237 | 15.554 | -1.223 | 0.221 | -49.510 | 11.462 | \n",
"
\n",
"\n",
" 50676 | 1.4611 | 13.396 | 0.109 | 0.913 | -24.794 | 27.716 | \n",
"
\n",
"\n",
" 50677 | -2.5882 | 5.710 | -0.453 | 0.650 | -13.779 | 8.602 | \n",
"
\n",
"\n",
" 50680 | -43.2726 | 44.422 | -0.974 | 0.330 | -130.338 | 43.793 | \n",
"
\n",
"\n",
" 50682 | 2.0910 | 28.025 | 0.075 | 0.941 | -52.836 | 57.018 | \n",
"
\n",
"\n",
" 50701 | -16.7178 | 4.837 | -3.457 | 0.001 | -26.197 | -7.238 | \n",
"
\n",
"\n",
" 50702 | -16.4732 | 4.342 | -3.794 | 0.000 | -24.984 | -7.962 | \n",
"
\n",
"\n",
" 50703 | -72.2323 | 4.494 | -16.072 | 0.000 | -81.041 | -63.424 | \n",
"
\n",
"\n",
" 50707 | -40.2384 | 8.129 | -4.950 | 0.000 | -56.171 | -24.305 | \n",
"
\n",
"\n",
" 50801 | 0.0630 | 6.692 | 0.009 | 0.992 | -13.053 | 13.179 | \n",
"
\n",
"\n",
" 50830 | -3.8187 | 58.727 | -0.065 | 0.948 | -118.922 | 111.285 | \n",
"
\n",
"\n",
" 50833 | 3.8418 | 16.401 | 0.234 | 0.815 | -28.305 | 35.988 | \n",
"
\n",
"\n",
" 50841 | -7.5872 | 13.506 | -0.562 | 0.574 | -34.058 | 18.884 | \n",
"
\n",
"\n",
" 50846 | -7.7487 | 25.928 | -0.299 | 0.765 | -58.566 | 43.069 | \n",
"
\n",
"\n",
" 50849 | -9.2956 | 14.792 | -0.628 | 0.530 | -38.288 | 19.697 | \n",
"
\n",
"\n",
" 50851 | -8.6087 | 17.288 | -0.498 | 0.619 | -42.492 | 25.275 | \n",
"
\n",
"\n",
" 50854 | 6.5800 | 14.539 | 0.453 | 0.651 | -21.917 | 35.077 | \n",
"
\n",
"\n",
" 50864 | -12.5812 | 43.403 | -0.290 | 0.772 | -97.650 | 72.488 | \n",
"
\n",
"\n",
" 51001 | -5.9024 | 24.589 | -0.240 | 0.810 | -54.097 | 42.292 | \n",
"
\n",
"\n",
" 51002 | -29.9874 | 54.378 | -0.551 | 0.581 | -136.567 | 76.592 | \n",
"
\n",
"\n",
" 51004 | -0.5980 | 16.055 | -0.037 | 0.970 | -32.066 | 30.870 | \n",
"
\n",
"\n",
" 51005 | -20.6372 | 61.334 | -0.336 | 0.737 | -140.850 | 99.575 | \n",
"
\n",
"\n",
" 51012 | -19.9132 | 7.629 | -2.610 | 0.009 | -34.866 | -4.960 | \n",
"
\n",
"\n",
" 51023 | -7.1054 | 15.642 | -0.454 | 0.650 | -37.762 | 23.552 | \n",
"
\n",
"\n",
" 51025 | -0.8733 | 11.617 | -0.075 | 0.940 | -23.642 | 21.896 | \n",
"
\n",
"\n",
" 51027 | 2.2879 | 33.963 | 0.067 | 0.946 | -64.279 | 68.855 | \n",
"
\n",
"\n",
" 51028 | -12.0640 | 28.847 | -0.418 | 0.676 | -68.604 | 44.476 | \n",
"
\n",
"\n",
" 51031 | -2.5559 | 6.126 | -0.417 | 0.676 | -14.562 | 9.450 | \n",
"
\n",
"\n",
" 51034 | -4.1896 | 10.041 | -0.417 | 0.676 | -23.870 | 15.491 | \n",
"
\n",
"\n",
" 51035 | 3.1191 | 23.290 | 0.134 | 0.893 | -42.528 | 48.767 | \n",
"
\n",
"\n",
" 51038 | -12.7361 | 64.323 | -0.198 | 0.843 | -138.807 | 113.335 | \n",
"
\n",
"\n",
" 51040 | -7.2080 | 7.499 | -0.961 | 0.336 | -21.906 | 7.490 | \n",
"
\n",
"\n",
" 51041 | 17.6005 | 12.021 | 1.464 | 0.143 | -5.959 | 41.160 | \n",
"
\n",
"\n",
" 51046 | -18.0042 | 18.128 | -0.993 | 0.321 | -53.534 | 17.526 | \n",
"
\n",
"\n",
" 51050 | 1.9184 | 17.923 | 0.107 | 0.915 | -33.210 | 37.047 | \n",
"
\n",
"\n",
" 51053 | -4.7779 | 52.538 | -0.091 | 0.928 | -107.751 | 98.195 | \n",
"
\n",
"\n",
" 51054 | -4.1676 | 10.758 | -0.387 | 0.698 | -25.252 | 16.917 | \n",
"
\n",
"\n",
" 51055 | 3.0083 | 19.274 | 0.156 | 0.876 | -34.769 | 40.785 | \n",
"
\n",
"\n",
" 51058 | -19.7716 | 34.443 | -0.574 | 0.566 | -87.278 | 47.735 | \n",
"
\n",
"\n",
" 51101 | -19.7871 | 8.101 | -2.442 | 0.015 | -35.665 | -3.909 | \n",
"
\n",
"\n",
" 51103 | -9.8981 | 6.168 | -1.605 | 0.109 | -21.988 | 2.192 | \n",
"
\n",
"\n",
" 51104 | -8.7683 | 5.678 | -1.544 | 0.123 | -19.897 | 2.360 | \n",
"
\n",
"\n",
" 51105 | -21.7183 | 6.601 | -3.290 | 0.001 | -34.657 | -8.780 | \n",
"
\n",
"\n",
" 51106 | 10.4248 | 4.836 | 2.156 | 0.031 | 0.947 | 19.902 | \n",
"
\n",
"\n",
" 51108 | -6.1111 | 8.038 | -0.760 | 0.447 | -21.866 | 9.643 | \n",
"
\n",
"\n",
" 51109 | -6.3463 | 14.755 | -0.430 | 0.667 | -35.267 | 22.574 | \n",
"
\n",
"\n",
" 51201 | 5.1171 | 6.941 | 0.737 | 0.461 | -8.486 | 18.721 | \n",
"
\n",
"\n",
" 51237 | 0.5687 | 18.712 | 0.030 | 0.976 | -36.107 | 37.244 | \n",
"
\n",
"\n",
" 51238 | 9.6976 | 39.193 | 0.247 | 0.805 | -67.119 | 86.515 | \n",
"
\n",
"\n",
" 51240 | 2.2007 | 12.765 | 0.172 | 0.863 | -22.818 | 27.219 | \n",
"
\n",
"\n",
" 51241 | -5.2087 | 11.906 | -0.437 | 0.662 | -28.544 | 18.126 | \n",
"
\n",
"\n",
" 51245 | -7.7163 | 19.708 | -0.392 | 0.695 | -46.343 | 30.910 | \n",
"
\n",
"\n",
" 51246 | -9.6877 | 9.569 | -1.012 | 0.311 | -28.442 | 9.066 | \n",
"
\n",
"\n",
" 51247 | -7.8392 | 21.104 | -0.371 | 0.710 | -49.202 | 33.524 | \n",
"
\n",
"\n",
" 51248 | 8.8231 | 15.512 | 0.569 | 0.569 | -21.579 | 39.225 | \n",
"
\n",
"\n",
" 51249 | -3.1822 | 11.115 | -0.286 | 0.775 | -24.968 | 18.603 | \n",
"
\n",
"\n",
" 51250 | 18.2911 | 8.054 | 2.271 | 0.023 | 2.506 | 34.076 | \n",
"
\n",
"\n",
" 51301 | -4.4180 | 5.241 | -0.843 | 0.399 | -14.691 | 5.855 | \n",
"
\n",
"\n",
" 51331 | -5.1645 | 7.858 | -0.657 | 0.511 | -20.566 | 10.237 | \n",
"
\n",
"\n",
" 51334 | -5.0916 | 7.704 | -0.661 | 0.509 | -20.192 | 10.009 | \n",
"
\n",
"\n",
" 51338 | -8.8283 | 76.864 | -0.115 | 0.909 | -159.480 | 141.823 | \n",
"
\n",
"\n",
" 51342 | -2.3078 | 20.779 | -0.111 | 0.912 | -43.035 | 38.419 | \n",
"
\n",
"\n",
" 51346 | 7.8156 | 12.040 | 0.649 | 0.516 | -15.783 | 31.414 | \n",
"
\n",
"\n",
" 51347 | 3.0485 | 35.466 | 0.086 | 0.932 | -66.464 | 72.561 | \n",
"
\n",
"\n",
" 51351 | 1.5295 | 7.027 | 0.218 | 0.828 | -12.243 | 15.302 | \n",
"
\n",
"\n",
" 51355 | 0.1004 | 32.230 | 0.003 | 0.998 | -63.069 | 63.269 | \n",
"
\n",
"\n",
" 51358 | -4.2979 | 30.067 | -0.143 | 0.886 | -63.228 | 54.632 | \n",
"
\n",
"\n",
" 51360 | 0.0190 | 5.560 | 0.003 | 0.997 | -10.878 | 10.916 | \n",
"
\n",
"\n",
" 51401 | 17.5053 | 5.815 | 3.010 | 0.003 | 6.108 | 28.903 | \n",
"
\n",
"\n",
" 51442 | 0.0771 | 7.159 | 0.011 | 0.991 | -13.953 | 14.108 | \n",
"
\n",
"\n",
" 51443 | 2.7054 | 28.025 | 0.097 | 0.923 | -52.223 | 57.633 | \n",
"
\n",
"\n",
" 51445 | 0.6973 | 11.723 | 0.059 | 0.953 | -22.279 | 23.673 | \n",
"
\n",
"\n",
" 51449 | -2.8651 | 17.533 | -0.163 | 0.870 | -37.230 | 31.500 | \n",
"
\n",
"\n",
" 51450 | -12.7259 | 14.792 | -0.860 | 0.390 | -41.718 | 16.266 | \n",
"
\n",
"\n",
" 51453 | 12.9900 | 117.379 | 0.111 | 0.912 | -217.069 | 243.049 | \n",
"
\n",
"\n",
" 51455 | -4.0115 | 12.765 | -0.314 | 0.753 | -29.030 | 21.007 | \n",
"
\n",
"\n",
" 51461 | -9.9745 | 14.300 | -0.698 | 0.485 | -38.002 | 18.053 | \n",
"
\n",
"\n",
" 51462 | -9.7198 | 40.725 | -0.239 | 0.811 | -89.539 | 70.099 | \n",
"
\n",
"\n",
" 51466 | -0.1239 | 54.378 | -0.002 | 0.998 | -106.703 | 106.455 | \n",
"
\n",
"\n",
" 51501 | 2.3809 | 3.837 | 0.620 | 0.535 | -5.140 | 9.902 | \n",
"
\n",
"\n",
" 51503 | 5.6901 | 4.249 | 1.339 | 0.181 | -2.638 | 14.018 | \n",
"
\n",
"\n",
" 51510 | -58.8503 | 16.008 | -3.676 | 0.000 | -90.226 | -27.475 | \n",
"
\n",
"\n",
" 51521 | -2.2436 | 9.811 | -0.229 | 0.819 | -21.472 | 16.985 | \n",
"
\n",
"\n",
" 51526 | -5.0958 | 39.936 | -0.128 | 0.898 | -83.370 | 73.178 | \n",
"
\n",
"\n",
" 51530 | 14.2455 | 117.379 | 0.121 | 0.903 | -215.814 | 244.305 | \n",
"
\n",
"\n",
" 51534 | -15.9805 | 11.055 | -1.445 | 0.148 | -37.649 | 5.688 | \n",
"
\n",
"\n",
" 51535 | -11.4494 | 90.934 | -0.126 | 0.900 | -189.677 | 166.779 | \n",
"
\n",
"\n",
" 51537 | 5.9062 | 8.267 | 0.714 | 0.475 | -10.298 | 22.110 | \n",
"
\n",
"\n",
" 51546 | 3.5996 | 20.174 | 0.178 | 0.858 | -35.940 | 43.140 | \n",
"
\n",
"\n",
" 51551 | -61.7359 | 37.190 | -1.660 | 0.097 | -134.628 | 11.156 | \n",
"
\n",
"\n",
" 51553 | 0.5729 | 67.797 | 0.008 | 0.993 | -132.308 | 133.454 | \n",
"
\n",
"\n",
" 51555 | -3.2086 | 8.297 | -0.387 | 0.699 | -19.470 | 13.053 | \n",
"
\n",
"\n",
" 51559 | 9.6207 | 25.723 | 0.374 | 0.708 | -40.796 | 60.037 | \n",
"
\n",
"\n",
" 51560 | -9.7561 | 12.956 | -0.753 | 0.451 | -35.150 | 15.638 | \n",
"
\n",
"\n",
" 51561 | -46.6442 | 18.561 | -2.513 | 0.012 | -83.022 | -10.266 | \n",
"
\n",
"\n",
" 51566 | -10.4752 | 7.607 | -1.377 | 0.169 | -25.386 | 4.435 | \n",
"
\n",
"\n",
" 51575 | -17.5749 | 41.561 | -0.423 | 0.672 | -99.034 | 63.884 | \n",
"
\n",
"\n",
" 51577 | -12.7977 | 28.291 | -0.452 | 0.651 | -68.247 | 42.652 | \n",
"
\n",
"\n",
" 51579 | 5.9828 | 16.505 | 0.362 | 0.717 | -26.366 | 38.332 | \n",
"
\n",
"\n",
" 51601 | 1.6640 | 8.211 | 0.203 | 0.839 | -14.429 | 17.757 | \n",
"
\n",
"\n",
" 51632 | -3.0163 | 7.951 | -0.379 | 0.704 | -18.601 | 12.568 | \n",
"
\n",
"\n",
" 51640 | -36.3935 | 39.193 | -0.929 | 0.353 | -113.211 | 40.424 | \n",
"
\n",
"\n",
" 52001 | -4.2894 | 3.812 | -1.125 | 0.260 | -11.760 | 3.181 | \n",
"
\n",
"\n",
" 52002 | 4.0070 | 6.282 | 0.638 | 0.524 | -8.305 | 16.319 | \n",
"
\n",
"\n",
" 52003 | -11.8010 | 7.086 | -1.665 | 0.096 | -25.690 | 2.088 | \n",
"
\n",
"\n",
" 52031 | -5.8589 | 13.395 | -0.437 | 0.662 | -32.113 | 20.396 | \n",
"
\n",
"\n",
" 52033 | 1.2119 | 16.771 | 0.072 | 0.942 | -31.660 | 34.084 | \n",
"
\n",
"\n",
" 52036 | -13.0730 | 46.694 | -0.280 | 0.780 | -104.593 | 78.447 | \n",
"
\n",
"\n",
" 52037 | -19.9528 | 37.822 | -0.528 | 0.598 | -94.084 | 54.178 | \n",
"
\n",
"\n",
" 52040 | 4.1247 | 9.162 | 0.450 | 0.653 | -13.833 | 22.082 | \n",
"
\n",
"\n",
" 52042 | 4.7953 | 22.051 | 0.217 | 0.828 | -38.425 | 48.015 | \n",
"
\n",
"\n",
" 52043 | -0.4586 | 18.636 | -0.025 | 0.980 | -36.984 | 36.067 | \n",
"
\n",
"\n",
" 52046 | -20.2794 | 37.823 | -0.536 | 0.592 | -94.411 | 53.853 | \n",
"
\n",
"\n",
" 52052 | -4.1976 | 11.100 | -0.378 | 0.705 | -25.954 | 17.558 | \n",
"
\n",
"\n",
" 52053 | 3.9616 | 26.352 | 0.150 | 0.881 | -47.688 | 55.611 | \n",
"
\n",
"\n",
" 52057 | 4.0086 | 12.493 | 0.321 | 0.748 | -20.478 | 28.495 | \n",
"
\n",
"\n",
" 52060 | 1.7497 | 6.197 | 0.282 | 0.778 | -10.396 | 13.896 | \n",
"
\n",
"\n",
" 52068 | -0.4140 | 27.270 | -0.015 | 0.988 | -53.862 | 53.034 | \n",
"
\n",
"\n",
" 52076 | -2.8712 | 26.799 | -0.107 | 0.915 | -55.396 | 49.654 | \n",
"
\n",
"\n",
" 52079 | -29.2503 | 28.291 | -1.034 | 0.301 | -84.700 | 26.200 | \n",
"
\n",
"\n",
" 52084 | -75.9708 | 10.855 | -6.999 | 0.000 | -97.246 | -54.696 | \n",
"
\n",
"\n",
" 52087 | -7.0022 | 10.521 | -0.666 | 0.506 | -27.624 | 13.619 | \n",
"
\n",
"\n",
" 52101 | -8.5698 | 6.475 | -1.324 | 0.186 | -21.260 | 4.120 | \n",
"
\n",
"\n",
" 52136 | 6.2559 | 8.938 | 0.700 | 0.484 | -11.262 | 23.773 | \n",
"
\n",
"\n",
" 52142 | -18.3814 | 24.589 | -0.748 | 0.455 | -66.576 | 29.813 | \n",
"
\n",
"\n",
" 52144 | 5.0966 | 16.250 | 0.314 | 0.754 | -26.753 | 36.946 | \n",
"
\n",
"\n",
" 52146 | -34.7268 | 29.438 | -1.180 | 0.238 | -92.424 | 22.970 | \n",
"
\n",
"\n",
" 52151 | -3.3330 | 16.772 | -0.199 | 0.842 | -36.205 | 29.539 | \n",
"
\n",
"\n",
" 52154 | 31.1452 | 31.836 | 0.978 | 0.328 | -31.253 | 93.543 | \n",
"
\n",
"\n",
" 52159 | -10.3523 | 10.063 | -1.029 | 0.304 | -30.076 | 9.371 | \n",
"
\n",
"\n",
" 52162 | -14.9808 | 30.067 | -0.498 | 0.618 | -73.911 | 43.949 | \n",
"
\n",
"\n",
" 52166 | 5.3714 | 27.032 | 0.199 | 0.842 | -47.611 | 58.354 | \n",
"
\n",
"\n",
" 52172 | -2.7345 | 7.582 | -0.361 | 0.718 | -17.595 | 12.126 | \n",
"
\n",
"\n",
" 52175 | -2.7737 | 11.333 | -0.245 | 0.807 | -24.986 | 19.439 | \n",
"
\n",
"\n",
" 52205 | 2.7027 | 8.758 | 0.309 | 0.758 | -14.463 | 19.869 | \n",
"
\n",
"\n",
" 52207 | -33.4341 | 39.936 | -0.837 | 0.402 | -111.708 | 44.840 | \n",
"
\n",
"\n",
" 52208 | -9.0768 | 10.278 | -0.883 | 0.377 | -29.221 | 11.068 | \n",
"
\n",
"\n",
" 52211 | -5.4183 | 14.792 | -0.366 | 0.714 | -34.411 | 23.574 | \n",
"
\n",
"\n",
" 52213 | 5.0373 | 15.555 | 0.324 | 0.746 | -25.450 | 35.524 | \n",
"
\n",
"\n",
" 52214 | -21.0266 | 17.857 | -1.178 | 0.239 | -56.025 | 13.972 | \n",
"
\n",
"\n",
" 52223 | 7.3775 | 61.334 | 0.120 | 0.904 | -112.835 | 127.591 | \n",
"
\n",
"\n",
" 52224 | -7.3855 | 33.504 | -0.220 | 0.826 | -73.052 | 58.281 | \n",
"
\n",
"\n",
" 52227 | -13.2876 | 31.092 | -0.427 | 0.669 | -74.226 | 47.651 | \n",
"
\n",
"\n",
" 52228 | -20.8334 | 33.964 | -0.613 | 0.540 | -87.402 | 45.735 | \n",
"
\n",
"\n",
" 52233 | -37.8710 | 10.536 | -3.595 | 0.000 | -58.520 | -17.221 | \n",
"
\n",
"\n",
" 52240 | -6.9281 | 3.548 | -1.953 | 0.051 | -13.881 | 0.025 | \n",
"
\n",
"\n",
" 52241 | 18.2449 | 4.222 | 4.321 | 0.000 | 9.969 | 26.521 | \n",
"
\n",
"\n",
" 52245 | -2.4160 | 7.422 | -0.326 | 0.745 | -16.963 | 12.131 | \n",
"
\n",
"\n",
" 52246 | 3.8799 | 6.956 | 0.558 | 0.577 | -9.755 | 17.514 | \n",
"
\n",
"\n",
" 52248 | -8.8408 | 30.739 | -0.288 | 0.774 | -69.088 | 51.406 | \n",
"
\n",
"\n",
" 52253 | -1.5028 | 17.409 | -0.086 | 0.931 | -35.624 | 32.619 | \n",
"
\n",
"\n",
" 52254 | -8.4216 | 35.466 | -0.237 | 0.812 | -77.934 | 61.091 | \n",
"
\n",
"\n",
" 52301 | 13.9906 | 10.141 | 1.380 | 0.168 | -5.885 | 33.866 | \n",
"
\n",
"\n",
" 52302 | -1.7461 | 4.912 | -0.355 | 0.722 | -11.374 | 7.882 | \n",
"
\n",
"\n",
" 52303 | 6.3331 | 10.913 | 0.580 | 0.562 | -15.055 | 27.721 | \n",
"
\n",
"\n",
" 52306 | -56.6558 | 36.014 | -1.573 | 0.116 | -127.242 | 13.930 | \n",
"
\n",
"\n",
" 52310 | 0.9092 | 6.159 | 0.148 | 0.883 | -11.162 | 12.980 | \n",
"
\n",
"\n",
" 52314 | -7.1847 | 6.334 | -1.134 | 0.257 | -19.598 | 5.229 | \n",
"
\n",
"\n",
" 52316 | 6.2083 | 36.014 | 0.172 | 0.863 | -64.378 | 76.794 | \n",
"
\n",
"\n",
" 52317 | -14.1641 | 6.122 | -2.314 | 0.021 | -26.162 | -2.166 | \n",
"
\n",
"\n",
" 52324 | -40.5398 | 33.062 | -1.226 | 0.220 | -105.341 | 24.261 | \n",
"
\n",
"\n",
" 52327 | 4.2951 | 13.854 | 0.310 | 0.757 | -22.859 | 31.449 | \n",
"
\n",
"\n",
" 52328 | 11.7279 | 143.749 | 0.082 | 0.935 | -270.016 | 293.472 | \n",
"
\n",
"\n",
" 52332 | -2.3405 | 11.465 | -0.204 | 0.838 | -24.811 | 20.130 | \n",
"
\n",
"\n",
" 52333 | -4.5720 | 15.731 | -0.291 | 0.771 | -35.404 | 26.260 | \n",
"
\n",
"\n",
" 52336 | -9.5144 | 39.193 | -0.243 | 0.808 | -86.331 | 67.302 | \n",
"
\n",
"\n",
" 52337 | -99.9479 | 67.798 | -1.474 | 0.140 | -232.831 | 32.935 | \n",
"
\n",
"\n",
" 52338 | 44.7882 | 13.262 | 3.377 | 0.001 | 18.795 | 70.781 | \n",
"
\n",
"\n",
" 52340 | -8.7741 | 17.857 | -0.491 | 0.623 | -43.773 | 26.225 | \n",
"
\n",
"\n",
" 52342 | -1.7379 | 7.709 | -0.225 | 0.822 | -16.848 | 13.372 | \n",
"
\n",
"\n",
" 52345 | -15.4194 | 37.823 | -0.408 | 0.684 | -89.551 | 58.712 | \n",
"
\n",
"\n",
" 52347 | 4.3374 | 25.523 | 0.170 | 0.865 | -45.686 | 54.361 | \n",
"
\n",
"\n",
" 52349 | 4.0480 | 14.367 | 0.282 | 0.778 | -24.111 | 32.207 | \n",
"
\n",
"\n",
" 52351 | 2.9520 | 37.189 | 0.079 | 0.937 | -69.938 | 75.842 | \n",
"
\n",
"\n",
" 52352 | -30.0249 | 45.515 | -0.660 | 0.509 | -119.234 | 59.184 | \n",
"
\n",
"\n",
" 52353 | -10.7119 | 6.090 | -1.759 | 0.079 | -22.649 | 1.225 | \n",
"
\n",
"\n",
" 52356 | -6.1172 | 13.209 | -0.463 | 0.643 | -32.007 | 19.772 | \n",
"
\n",
"\n",
" 52358 | 9.0290 | 12.789 | 0.706 | 0.480 | -16.038 | 34.096 | \n",
"
\n",
"\n",
" 52361 | 2.7874 | 12.765 | 0.218 | 0.827 | -22.231 | 27.806 | \n",
"
\n",
"\n",
" 52401 | 10.6881 | 6.225 | 1.717 | 0.086 | -1.513 | 22.889 | \n",
"
\n",
"\n",
" 52402 | -4.6156 | 3.435 | -1.344 | 0.179 | -11.348 | 2.117 | \n",
"
\n",
"\n",
" 52403 | -9.0701 | 6.396 | -1.418 | 0.156 | -21.607 | 3.466 | \n",
"
\n",
"\n",
" 52404 | -14.8756 | 3.948 | -3.768 | 0.000 | -22.613 | -7.138 | \n",
"
\n",
"\n",
" 52405 | -3.3481 | 4.202 | -0.797 | 0.426 | -11.584 | 4.888 | \n",
"
\n",
"\n",
" 52411 | 4.8312 | 6.146 | 0.786 | 0.432 | -7.214 | 16.877 | \n",
"
\n",
"\n",
" 52501 | 4.3080 | 4.328 | 0.995 | 0.320 | -4.175 | 12.791 | \n",
"
\n",
"\n",
" 52531 | 0.2523 | 11.270 | 0.022 | 0.982 | -21.836 | 22.341 | \n",
"
\n",
"\n",
" 52537 | -1.2295 | 14.470 | -0.085 | 0.932 | -29.590 | 27.131 | \n",
"
\n",
"\n",
" 52544 | -10.4153 | 6.950 | -1.499 | 0.134 | -24.037 | 3.207 | \n",
"
\n",
"\n",
" 52553 | -11.5573 | 36.013 | -0.321 | 0.748 | -82.142 | 59.028 | \n",
"
\n",
"\n",
" 52554 | -21.6532 | 32.638 | -0.663 | 0.507 | -85.622 | 42.316 | \n",
"
\n",
"\n",
" 52556 | -10.7956 | 7.552 | -1.429 | 0.153 | -25.598 | 4.006 | \n",
"
\n",
"\n",
" 52565 | 5.2998 | 14.136 | 0.375 | 0.708 | -22.405 | 33.005 | \n",
"
\n",
"\n",
" 52571 | -5.4152 | 24.950 | -0.217 | 0.828 | -54.317 | 43.486 | \n",
"
\n",
"\n",
" 52577 | -10.5555 | 5.756 | -1.834 | 0.067 | -21.837 | 0.726 | \n",
"
\n",
"\n",
" 52591 | 1.0685 | 12.001 | 0.089 | 0.929 | -22.454 | 24.591 | \n",
"
\n",
"\n",
" 52601 | -11.7506 | 4.454 | -2.638 | 0.008 | -20.480 | -3.021 | \n",
"
\n",
"\n",
" 52623 | -29.5609 | 52.538 | -0.563 | 0.574 | -132.535 | 73.413 | \n",
"
\n",
"\n",
" 52625 | -2.9976 | 49.358 | -0.061 | 0.952 | -99.737 | 93.742 | \n",
"
\n",
"\n",
" 52626 | -1.7682 | 36.014 | -0.049 | 0.961 | -72.354 | 68.817 | \n",
"
\n",
"\n",
" 52627 | -9.2686 | 5.976 | -1.551 | 0.121 | -20.982 | 2.445 | \n",
"
\n",
"\n",
" 52632 | 6.9464 | 5.483 | 1.267 | 0.205 | -3.799 | 17.692 | \n",
"
\n",
"\n",
" 52637 | -5.7046 | 11.925 | -0.478 | 0.632 | -29.077 | 17.668 | \n",
"
\n",
"\n",
" 52639 | -28.4896 | 33.063 | -0.862 | 0.389 | -93.292 | 36.313 | \n",
"
\n",
"\n",
" 52641 | 2.6482 | 6.588 | 0.402 | 0.688 | -10.264 | 15.561 | \n",
"
\n",
"\n",
" 52653 | -0.1926 | 14.793 | -0.013 | 0.990 | -29.186 | 28.801 | \n",
"
\n",
"\n",
" 52655 | -12.7123 | 7.811 | -1.628 | 0.104 | -28.021 | 2.596 | \n",
"
\n",
"\n",
" 52656 | 4.4909 | 15.554 | 0.289 | 0.773 | -25.995 | 34.977 | \n",
"
\n",
"\n",
" 52722 | 3.2800 | 4.125 | 0.795 | 0.426 | -4.804 | 11.364 | \n",
"
\n",
"\n",
" 52726 | 9.9088 | 10.153 | 0.976 | 0.329 | -9.991 | 29.809 | \n",
"
\n",
"\n",
" 52728 | -30.7783 | 33.964 | -0.906 | 0.365 | -97.346 | 35.790 | \n",
"
\n",
"\n",
" 52730 | -23.3074 | 17.409 | -1.339 | 0.181 | -57.429 | 10.814 | \n",
"
\n",
"\n",
" 52732 | -5.5050 | 4.526 | -1.216 | 0.224 | -14.376 | 3.366 | \n",
"
\n",
"\n",
" 52733 | -22.7024 | 12.957 | -1.752 | 0.080 | -48.097 | 2.693 | \n",
"
\n",
"\n",
" 52738 | -70.2835 | 12.179 | -5.771 | 0.000 | -94.154 | -46.413 | \n",
"
\n",
"\n",
" 52742 | 9.6636 | 14.540 | 0.665 | 0.506 | -18.833 | 38.161 | \n",
"
\n",
"\n",
" 52747 | 3.3977 | 13.158 | 0.258 | 0.796 | -22.392 | 29.187 | \n",
"
\n",
"\n",
" 52748 | -12.7320 | 8.096 | -1.573 | 0.116 | -28.600 | 3.136 | \n",
"
\n",
"\n",
" 52751 | -2.0188 | 25.523 | -0.079 | 0.937 | -52.043 | 48.005 | \n",
"
\n",
"\n",
" 52753 | 6.5331 | 8.703 | 0.751 | 0.453 | -10.525 | 23.591 | \n",
"
\n",
"\n",
" 52761 | -3.0989 | 4.247 | -0.730 | 0.466 | -11.422 | 5.224 | \n",
"
\n",
"\n",
" 52768 | -4.0831 | 32.638 | -0.125 | 0.900 | -68.054 | 59.887 | \n",
"
\n",
"\n",
" 52772 | 2.2827 | 9.380 | 0.243 | 0.808 | -16.102 | 20.668 | \n",
"
\n",
"\n",
" 52776 | -4.9321 | 12.559 | -0.393 | 0.695 | -29.548 | 19.684 | \n",
"
\n",
"\n",
" 52777 | -2.4986 | 39.936 | -0.063 | 0.950 | -80.773 | 75.776 | \n",
"
\n",
"\n",
" 52778 | 6.8460 | 11.724 | 0.584 | 0.559 | -16.133 | 29.825 | \n",
"
\n",
"\n",
" 52801 | -23.7006 | 143.749 | -0.165 | 0.869 | -305.444 | 258.043 | \n",
"
\n",
"\n",
" 52802 | -60.1258 | 5.848 | -10.282 | 0.000 | -71.587 | -48.665 | \n",
"
\n",
"\n",
" 52803 | -111.9066 | 7.277 | -15.378 | 0.000 | -126.169 | -97.644 | \n",
"
\n",
"\n",
" 52804 | -27.7476 | 4.444 | -6.244 | 0.000 | -36.457 | -19.038 | \n",
"
\n",
"\n",
" 52806 | -13.1925 | 4.913 | -2.685 | 0.007 | -22.823 | -3.562 | \n",
"
\n",
"\n",
" 52807 | 7.5408 | 4.189 | 1.800 | 0.072 | -0.670 | 15.751 | \n",
"
\n",
"\n",
" 56201 | -7.0070 | 42.452 | -0.165 | 0.869 | -90.212 | 76.198 | \n",
"
\n",
"\n",
" 712-2 | -4.7017 | 15.138 | -0.311 | 0.756 | -34.372 | 24.969 | \n",
"
\n",
"
\n",
"\n",
"\n",
" Omnibus: | 367270.389 | Durbin-Watson: | 2.000 | \n",
"
\n",
"\n",
" Prob(Omnibus): | 0.000 | Jarque-Bera (JB): | 67275238939.284 | \n",
"
\n",
"\n",
" Skew: | 5.961 | Prob(JB): | 0.00 | \n",
"
\n",
"\n",
" Kurtosis: | 2451.746 | Cond. No. | 9.85e+03 | \n",
"
\n",
"
"
],
"text/plain": [
"\n",
"\"\"\"\n",
" OLS Regression Results \n",
"==============================================================================\n",
"Dep. Variable: sale_dollars R-squared: 0.719\n",
"Model: OLS Adj. R-squared: 0.719\n",
"Method: Least Squares F-statistic: 1670.\n",
"Date: Sun, 22 Oct 2017 Prob (F-statistic): 0.00\n",
"Time: 22:32:28 Log-Likelihood: -1.8128e+06\n",
"No. Observations: 269258 AIC: 3.626e+06\n",
"Df Residuals: 268844 BIC: 3.631e+06\n",
"Df Model: 413 \n",
"Covariance Type: nonrobust \n",
"=======================================================================================\n",
" coef std err t P>|t| [0.025 0.975]\n",
"---------------------------------------------------------------------------------------\n",
"const -96.5788 2.494 -38.719 0.000 -101.468 -91.690\n",
"state_bottle_retail 6.7432 0.038 179.585 0.000 6.670 6.817\n",
"bottles_sold 13.3491 0.017 808.482 0.000 13.317 13.381\n",
"50002 -29.9087 31.092 -0.962 0.336 -90.848 31.030\n",
"50003 8.2845 11.652 0.711 0.477 -14.554 31.123\n",
"50006 -8.8378 18.561 -0.476 0.634 -45.216 27.541\n",
"50009 -4.3451 5.048 -0.861 0.389 -14.240 5.550\n",
"50014 -22.4837 9.811 -2.292 0.022 -41.712 -3.255\n",
"50020 -3.5390 16.882 -0.210 0.834 -36.628 29.550\n",
"50021 19.1865 4.715 4.069 0.000 9.945 28.428\n",
"50022 -3.0223 6.759 -0.447 0.655 -16.269 10.225\n",
"50023 -12.3539 4.878 -2.533 0.011 -21.914 -2.794\n",
"50025 -1.3936 14.401 -0.097 0.923 -29.619 26.832\n",
"50028 7.1533 26.799 0.267 0.790 -45.373 59.679\n",
"50033 0.1676 23.753 0.007 0.994 -46.387 46.722\n",
"50035 4.8937 10.940 0.447 0.655 -16.548 26.336\n",
"50036 0.6791 5.877 0.116 0.908 -10.839 12.197\n",
"50044 -154.1943 50.875 -3.031 0.002 -253.907 -54.481\n",
"50046 7.6630 28.565 0.268 0.788 -48.324 63.650\n",
"50047 -5.8497 15.301 -0.382 0.702 -35.839 24.140\n",
"50048 -31.6252 33.504 -0.944 0.345 -97.292 34.042\n",
"50049 -0.3689 9.634 -0.038 0.969 -19.252 18.514\n",
"50054 -0.8693 14.868 -0.058 0.953 -30.010 28.271\n",
"50056 -21.3511 39.193 -0.545 0.586 -98.168 55.465\n",
"50058 -10.8854 18.867 -0.577 0.564 -47.865 26.094\n",
"50060 -11.7605 17.990 -0.654 0.513 -47.021 23.500\n",
"50061 -141.0714 83.019 -1.699 0.089 -303.786 21.643\n",
"50069 -6.6335 17.856 -0.371 0.710 -41.631 28.364\n",
"50071 -22.8323 56.427 -0.405 0.686 -133.428 87.763\n",
"50072 -2.4535 33.504 -0.073 0.942 -68.120 63.213\n",
"50075 7.8137 21.330 0.366 0.714 -33.992 49.619\n",
"50076 -37.6038 43.403 -0.866 0.386 -122.673 47.466\n",
"50107 -37.4722 38.489 -0.974 0.330 -112.910 37.965\n",
"50109 -11.3427 25.928 -0.437 0.662 -62.161 39.475\n",
"50111 -3.0709 5.723 -0.537 0.592 -14.288 8.147\n",
"50112 -1.4448 5.804 -0.249 0.803 -12.820 9.930\n",
"50115 3.8363 17.471 0.220 0.826 -30.407 38.080\n",
"50122 7.6937 23.290 0.330 0.741 -37.954 53.342\n",
"50123 -26.4626 37.190 -0.712 0.477 -99.354 46.429\n",
"50124 -0.5241 18.789 -0.028 0.978 -37.350 36.302\n",
"50125 -9.7245 5.412 -1.797 0.072 -20.332 0.883\n",
"50126 5.1077 8.144 0.627 0.531 -10.855 21.070\n",
"50129 0.4383 7.387 0.059 0.953 -14.039 14.916\n",
"50130 8.1887 22.052 0.371 0.710 -35.033 51.410\n",
"50131 2.4712 5.018 0.493 0.622 -7.363 12.306\n",
"50135 -7.7712 22.714 -0.342 0.732 -52.290 36.748\n",
"50136 -3.3111 45.516 -0.073 0.942 -92.520 85.898\n",
"50138 -1.2468 5.961 -0.209 0.834 -12.930 10.436\n",
"50140 -1.5484 19.891 -0.078 0.938 -40.534 37.437\n",
"50142 2.1466 12.386 0.173 0.862 -22.130 26.423\n",
"50144 -14.2001 18.946 -0.749 0.454 -51.335 22.934\n",
"50150 -15.2315 61.334 -0.248 0.804 -135.444 104.981\n",
"50156 1.6367 15.342 0.107 0.915 -28.434 31.707\n",
"50158 -4.6139 4.609 -1.001 0.317 -13.648 4.420\n",
"50160 -20.0637 45.515 -0.441 0.659 -109.272 69.145\n",
"50161 3.1519 39.936 0.079 0.937 -75.122 81.426\n",
"50162 4.6876 83.017 0.056 0.955 -158.023 167.398\n",
"50163 -6.5949 15.731 -0.419 0.675 -37.426 24.237\n",
"50170 -4.2230 18.636 -0.227 0.821 -40.749 32.303\n",
"50171 -2.3062 11.207 -0.206 0.837 -24.272 19.659\n",
"50201 -4.6950 7.111 -0.660 0.509 -18.632 9.242\n",
"50207 -1.8600 38.489 -0.048 0.961 -77.297 73.577\n",
"50208 -4.7194 4.871 -0.969 0.333 -14.266 4.827\n",
"50210 3.4820 34.443 0.101 0.919 -64.025 70.989\n",
"50211 10.1313 9.821 1.032 0.302 -9.118 29.380\n",
"50212 -3.7959 18.486 -0.205 0.837 -40.029 32.437\n",
"50213 7.0022 8.085 0.866 0.386 -8.845 22.849\n",
"50216 -7.1645 12.788 -0.560 0.575 -32.229 17.900\n",
"50219 8.3708 7.344 1.140 0.254 -6.023 22.765\n",
"50220 -1.7786 7.528 -0.236 0.813 -16.533 12.976\n",
"50225 -24.5009 16.103 -1.521 0.128 -56.063 7.061\n",
"50226 2.5729 12.537 0.205 0.837 -22.000 27.145\n",
"50228 -29.7824 35.467 -0.840 0.401 -99.296 39.731\n",
"50240 -13.0806 38.489 -0.340 0.734 -88.519 62.357\n",
"50244 -5.7635 25.327 -0.228 0.820 -55.405 43.877\n",
"50247 12.0218 15.777 0.762 0.446 -18.900 42.944\n",
"50248 1.2860 11.399 0.113 0.910 -21.056 23.628\n",
"50249 -12.3985 22.996 -0.539 0.590 -57.471 32.674\n",
"50250 -5.7329 12.281 -0.467 0.641 -29.803 18.337\n",
"50251 -12.4684 58.727 -0.212 0.832 -127.572 102.635\n",
"50261 -98.1211 52.538 -1.868 0.062 -201.095 4.853\n",
"50263 4.7477 5.954 0.797 0.425 -6.923 16.418\n",
"50265 -1.8658 3.914 -0.477 0.634 -9.538 5.806\n",
"50266 45.0208 4.514 9.973 0.000 36.173 53.868\n",
"50273 -8.2800 7.660 -1.081 0.280 -23.293 6.733\n",
"50276 -2.1760 34.443 -0.063 0.950 -69.683 65.331\n",
"50300 -12.2810 16.663 -0.737 0.461 -44.940 20.378\n",
"50309 -102.6366 10.485 -9.789 0.000 -123.187 -82.086\n",
"50310 -31.7304 4.916 -6.454 0.000 -41.366 -22.095\n",
"50311 7.3928 4.249 1.740 0.082 -0.935 15.721\n",
"50312 -15.1329 6.365 -2.377 0.017 -27.609 -2.657\n",
"50313 -26.0639 7.411 -3.517 0.000 -40.590 -11.538\n",
"50314 5.2666 3.875 1.359 0.174 -2.329 12.862\n",
"50315 -23.8340 4.384 -5.437 0.000 -32.426 -15.242\n",
"50316 -60.5910 6.187 -9.793 0.000 -72.717 -48.465\n",
"50317 -25.3308 3.896 -6.501 0.000 -32.968 -17.694\n",
"50320 32.8486 4.323 7.599 0.000 24.376 41.321\n",
"50321 -1.1673 5.480 -0.213 0.831 -11.908 9.573\n",
"50322 -3.8985 4.493 -0.868 0.386 -12.704 4.907\n",
"50323 -2.7250 15.301 -0.178 0.859 -32.714 27.264\n",
"50324 -25.0191 15.262 -1.639 0.101 -54.932 4.894\n",
"50325 -14.1999 7.134 -1.990 0.047 -28.183 -0.217\n",
"50327 -4.3109 18.486 -0.233 0.816 -40.544 31.922\n",
"50401 -14.1112 3.984 -3.542 0.000 -21.919 -6.303\n",
"50421 -13.2058 15.300 -0.863 0.388 -43.194 16.783\n",
"50423 -7.2069 16.401 -0.439 0.660 -39.353 24.939\n",
"50424 -3.3914 16.717 -0.203 0.839 -36.157 29.374\n",
"50428 -5.5920 5.152 -1.085 0.278 -15.689 4.505\n",
"50430 10.3952 37.189 0.280 0.780 -62.494 83.285\n",
"50435 13.3039 17.856 0.745 0.456 -21.693 48.301\n",
"50436 -10.9910 8.683 -1.266 0.206 -28.009 6.027\n",
"50438 -24.6533 15.511 -1.589 0.112 -55.055 5.748\n",
"50441 -8.4437 8.222 -1.027 0.304 -24.558 7.671\n",
"50450 -10.1527 14.367 -0.707 0.480 -38.311 18.006\n",
"50452 4.2868 71.905 0.060 0.952 -136.645 145.218\n",
"50456 -31.9335 32.230 -0.991 0.322 -95.103 31.236\n",
"50458 -7.1546 30.738 -0.233 0.816 -67.401 53.092\n",
"50459 6.9788 11.176 0.624 0.532 -14.926 28.884\n",
"50461 -0.6935 7.626 -0.091 0.928 -15.640 14.253\n",
"50466 0.2075 35.466 0.006 0.995 -69.305 69.720\n",
"50469 8.0932 16.201 0.500 0.617 -23.661 39.847\n",
"50472 -25.5296 15.059 -1.695 0.090 -55.046 3.987\n",
"50475 -30.4922 39.193 -0.778 0.437 -107.309 46.325\n",
"50483 -4.1310 17.348 -0.238 0.812 -38.133 29.871\n",
"50501 -6.7320 4.443 -1.515 0.130 -15.441 1.977\n",
"50511 -3.0320 6.793 -0.446 0.655 -16.346 10.282\n",
"50514 -42.4734 49.358 -0.861 0.390 -139.214 54.267\n",
"50517 -4.5785 10.826 -0.423 0.672 -25.797 16.640\n",
"50525 -5.4497 15.960 -0.341 0.733 -36.732 25.832\n",
"50529 -103.5983 40.726 -2.544 0.011 -183.419 -23.777\n",
"50530 5.1168 21.216 0.241 0.809 -36.465 46.699\n",
"50533 1.4513 12.059 0.120 0.904 -22.184 25.087\n",
"50535 -8.0749 36.587 -0.221 0.825 -79.785 63.635\n",
"50536 -6.0549 7.057 -0.858 0.391 -19.886 7.776\n",
"50540 -29.3117 83.017 -0.353 0.724 -192.023 133.400\n",
"50541 -47.6681 50.874 -0.937 0.349 -147.379 52.043\n",
"50542 -86.8969 52.539 -1.654 0.098 -189.871 16.077\n",
"50543 -11.1202 14.646 -0.759 0.448 -39.826 17.586\n",
"50548 -1.8959 9.021 -0.210 0.834 -19.576 15.784\n",
"50554 -3.6233 15.021 -0.241 0.809 -33.063 25.817\n",
"50563 5.0879 15.686 0.324 0.746 -25.657 35.832\n",
"50569 -35.8643 45.516 -0.788 0.431 -125.074 53.345\n",
"50574 0.1913 12.040 0.016 0.987 -23.406 23.789\n",
"50579 3.5673 19.359 0.184 0.854 -34.376 41.511\n",
"50581 1.8230 34.943 0.052 0.958 -66.664 70.310\n",
"50583 -10.0252 8.390 -1.195 0.232 -26.470 6.420\n",
"50585 -5.4941 14.540 -0.378 0.706 -33.991 23.003\n",
"50588 -8.0050 4.714 -1.698 0.089 -17.245 1.235\n",
"50590 -14.1602 35.466 -0.399 0.690 -83.672 55.352\n",
"50595 -3.2744 7.281 -0.450 0.653 -17.545 10.996\n",
"50597 -1.9411 19.890 -0.098 0.922 -40.925 37.043\n",
"50601 -1.0390 21.445 -0.048 0.961 -43.070 40.992\n",
"50602 0.6020 17.052 0.035 0.972 -32.820 34.024\n",
"50606 14.7401 23.442 0.629 0.529 -31.206 60.686\n",
"50613 -5.4575 3.699 -1.475 0.140 -12.707 1.792\n",
"50616 -7.3308 7.482 -0.980 0.327 -21.996 7.334\n",
"50619 -4.3782 25.137 -0.174 0.862 -53.646 44.889\n",
"50621 -2.6679 20.571 -0.130 0.897 -42.987 37.651\n",
"50622 0.0443 18.413 0.002 0.998 -36.045 36.133\n",
"50627 0.9066 11.687 0.078 0.938 -22.000 23.813\n",
"50628 16.6751 29.438 0.566 0.571 -41.022 74.372\n",
"50629 -8.3797 22.051 -0.380 0.704 -51.600 34.840\n",
"50630 6.9327 23.142 0.300 0.765 -38.425 52.290\n",
"50634 11.7941 83.017 0.142 0.887 -150.917 174.505\n",
"50635 8.6252 23.753 0.363 0.717 -37.929 55.180\n",
"50636 -2.7311 19.890 -0.137 0.891 -41.715 36.253\n",
"50638 0.9179 10.291 0.089 0.929 -19.251 21.087\n",
"50641 -152.2637 36.588 -4.162 0.000 -223.976 -80.552\n",
"50643 -8.0216 32.229 -0.249 0.803 -71.190 55.147\n",
"50644 2.7410 7.204 0.380 0.704 -11.379 16.861\n",
"50647 -3.3971 9.862 -0.344 0.730 -22.726 15.932\n",
"50648 -3.7996 15.301 -0.248 0.804 -33.788 26.189\n",
"50651 2.7037 13.132 0.206 0.837 -23.034 28.442\n",
"50658 -1.9036 22.051 -0.086 0.931 -45.124 41.316\n",
"50659 -3.0345 12.859 -0.236 0.813 -28.239 22.170\n",
"50662 -4.6819 8.624 -0.543 0.587 -21.584 12.220\n",
"50665 -2.5488 22.179 -0.115 0.909 -46.019 40.922\n",
"50667 -13.2569 17.110 -0.775 0.438 -46.793 20.279\n",
"50669 -7.9526 35.466 -0.224 0.823 -77.465 61.560\n",
"50674 -5.5210 11.382 -0.485 0.628 -27.829 16.787\n",
"50675 -19.0237 15.554 -1.223 0.221 -49.510 11.462\n",
"50676 1.4611 13.396 0.109 0.913 -24.794 27.716\n",
"50677 -2.5882 5.710 -0.453 0.650 -13.779 8.602\n",
"50680 -43.2726 44.422 -0.974 0.330 -130.338 43.793\n",
"50682 2.0910 28.025 0.075 0.941 -52.836 57.018\n",
"50701 -16.7178 4.837 -3.457 0.001 -26.197 -7.238\n",
"50702 -16.4732 4.342 -3.794 0.000 -24.984 -7.962\n",
"50703 -72.2323 4.494 -16.072 0.000 -81.041 -63.424\n",
"50707 -40.2384 8.129 -4.950 0.000 -56.171 -24.305\n",
"50801 0.0630 6.692 0.009 0.992 -13.053 13.179\n",
"50830 -3.8187 58.727 -0.065 0.948 -118.922 111.285\n",
"50833 3.8418 16.401 0.234 0.815 -28.305 35.988\n",
"50841 -7.5872 13.506 -0.562 0.574 -34.058 18.884\n",
"50846 -7.7487 25.928 -0.299 0.765 -58.566 43.069\n",
"50849 -9.2956 14.792 -0.628 0.530 -38.288 19.697\n",
"50851 -8.6087 17.288 -0.498 0.619 -42.492 25.275\n",
"50854 6.5800 14.539 0.453 0.651 -21.917 35.077\n",
"50864 -12.5812 43.403 -0.290 0.772 -97.650 72.488\n",
"51001 -5.9024 24.589 -0.240 0.810 -54.097 42.292\n",
"51002 -29.9874 54.378 -0.551 0.581 -136.567 76.592\n",
"51004 -0.5980 16.055 -0.037 0.970 -32.066 30.870\n",
"51005 -20.6372 61.334 -0.336 0.737 -140.850 99.575\n",
"51012 -19.9132 7.629 -2.610 0.009 -34.866 -4.960\n",
"51023 -7.1054 15.642 -0.454 0.650 -37.762 23.552\n",
"51025 -0.8733 11.617 -0.075 0.940 -23.642 21.896\n",
"51027 2.2879 33.963 0.067 0.946 -64.279 68.855\n",
"51028 -12.0640 28.847 -0.418 0.676 -68.604 44.476\n",
"51031 -2.5559 6.126 -0.417 0.676 -14.562 9.450\n",
"51034 -4.1896 10.041 -0.417 0.676 -23.870 15.491\n",
"51035 3.1191 23.290 0.134 0.893 -42.528 48.767\n",
"51038 -12.7361 64.323 -0.198 0.843 -138.807 113.335\n",
"51040 -7.2080 7.499 -0.961 0.336 -21.906 7.490\n",
"51041 17.6005 12.021 1.464 0.143 -5.959 41.160\n",
"51046 -18.0042 18.128 -0.993 0.321 -53.534 17.526\n",
"51050 1.9184 17.923 0.107 0.915 -33.210 37.047\n",
"51053 -4.7779 52.538 -0.091 0.928 -107.751 98.195\n",
"51054 -4.1676 10.758 -0.387 0.698 -25.252 16.917\n",
"51055 3.0083 19.274 0.156 0.876 -34.769 40.785\n",
"51058 -19.7716 34.443 -0.574 0.566 -87.278 47.735\n",
"51101 -19.7871 8.101 -2.442 0.015 -35.665 -3.909\n",
"51103 -9.8981 6.168 -1.605 0.109 -21.988 2.192\n",
"51104 -8.7683 5.678 -1.544 0.123 -19.897 2.360\n",
"51105 -21.7183 6.601 -3.290 0.001 -34.657 -8.780\n",
"51106 10.4248 4.836 2.156 0.031 0.947 19.902\n",
"51108 -6.1111 8.038 -0.760 0.447 -21.866 9.643\n",
"51109 -6.3463 14.755 -0.430 0.667 -35.267 22.574\n",
"51201 5.1171 6.941 0.737 0.461 -8.486 18.721\n",
"51237 0.5687 18.712 0.030 0.976 -36.107 37.244\n",
"51238 9.6976 39.193 0.247 0.805 -67.119 86.515\n",
"51240 2.2007 12.765 0.172 0.863 -22.818 27.219\n",
"51241 -5.2087 11.906 -0.437 0.662 -28.544 18.126\n",
"51245 -7.7163 19.708 -0.392 0.695 -46.343 30.910\n",
"51246 -9.6877 9.569 -1.012 0.311 -28.442 9.066\n",
"51247 -7.8392 21.104 -0.371 0.710 -49.202 33.524\n",
"51248 8.8231 15.512 0.569 0.569 -21.579 39.225\n",
"51249 -3.1822 11.115 -0.286 0.775 -24.968 18.603\n",
"51250 18.2911 8.054 2.271 0.023 2.506 34.076\n",
"51301 -4.4180 5.241 -0.843 0.399 -14.691 5.855\n",
"51331 -5.1645 7.858 -0.657 0.511 -20.566 10.237\n",
"51334 -5.0916 7.704 -0.661 0.509 -20.192 10.009\n",
"51338 -8.8283 76.864 -0.115 0.909 -159.480 141.823\n",
"51342 -2.3078 20.779 -0.111 0.912 -43.035 38.419\n",
"51346 7.8156 12.040 0.649 0.516 -15.783 31.414\n",
"51347 3.0485 35.466 0.086 0.932 -66.464 72.561\n",
"51351 1.5295 7.027 0.218 0.828 -12.243 15.302\n",
"51355 0.1004 32.230 0.003 0.998 -63.069 63.269\n",
"51358 -4.2979 30.067 -0.143 0.886 -63.228 54.632\n",
"51360 0.0190 5.560 0.003 0.997 -10.878 10.916\n",
"51401 17.5053 5.815 3.010 0.003 6.108 28.903\n",
"51442 0.0771 7.159 0.011 0.991 -13.953 14.108\n",
"51443 2.7054 28.025 0.097 0.923 -52.223 57.633\n",
"51445 0.6973 11.723 0.059 0.953 -22.279 23.673\n",
"51449 -2.8651 17.533 -0.163 0.870 -37.230 31.500\n",
"51450 -12.7259 14.792 -0.860 0.390 -41.718 16.266\n",
"51453 12.9900 117.379 0.111 0.912 -217.069 243.049\n",
"51455 -4.0115 12.765 -0.314 0.753 -29.030 21.007\n",
"51461 -9.9745 14.300 -0.698 0.485 -38.002 18.053\n",
"51462 -9.7198 40.725 -0.239 0.811 -89.539 70.099\n",
"51466 -0.1239 54.378 -0.002 0.998 -106.703 106.455\n",
"51501 2.3809 3.837 0.620 0.535 -5.140 9.902\n",
"51503 5.6901 4.249 1.339 0.181 -2.638 14.018\n",
"51510 -58.8503 16.008 -3.676 0.000 -90.226 -27.475\n",
"51521 -2.2436 9.811 -0.229 0.819 -21.472 16.985\n",
"51526 -5.0958 39.936 -0.128 0.898 -83.370 73.178\n",
"51530 14.2455 117.379 0.121 0.903 -215.814 244.305\n",
"51534 -15.9805 11.055 -1.445 0.148 -37.649 5.688\n",
"51535 -11.4494 90.934 -0.126 0.900 -189.677 166.779\n",
"51537 5.9062 8.267 0.714 0.475 -10.298 22.110\n",
"51546 3.5996 20.174 0.178 0.858 -35.940 43.140\n",
"51551 -61.7359 37.190 -1.660 0.097 -134.628 11.156\n",
"51553 0.5729 67.797 0.008 0.993 -132.308 133.454\n",
"51555 -3.2086 8.297 -0.387 0.699 -19.470 13.053\n",
"51559 9.6207 25.723 0.374 0.708 -40.796 60.037\n",
"51560 -9.7561 12.956 -0.753 0.451 -35.150 15.638\n",
"51561 -46.6442 18.561 -2.513 0.012 -83.022 -10.266\n",
"51566 -10.4752 7.607 -1.377 0.169 -25.386 4.435\n",
"51575 -17.5749 41.561 -0.423 0.672 -99.034 63.884\n",
"51577 -12.7977 28.291 -0.452 0.651 -68.247 42.652\n",
"51579 5.9828 16.505 0.362 0.717 -26.366 38.332\n",
"51601 1.6640 8.211 0.203 0.839 -14.429 17.757\n",
"51632 -3.0163 7.951 -0.379 0.704 -18.601 12.568\n",
"51640 -36.3935 39.193 -0.929 0.353 -113.211 40.424\n",
"52001 -4.2894 3.812 -1.125 0.260 -11.760 3.181\n",
"52002 4.0070 6.282 0.638 0.524 -8.305 16.319\n",
"52003 -11.8010 7.086 -1.665 0.096 -25.690 2.088\n",
"52031 -5.8589 13.395 -0.437 0.662 -32.113 20.396\n",
"52033 1.2119 16.771 0.072 0.942 -31.660 34.084\n",
"52036 -13.0730 46.694 -0.280 0.780 -104.593 78.447\n",
"52037 -19.9528 37.822 -0.528 0.598 -94.084 54.178\n",
"52040 4.1247 9.162 0.450 0.653 -13.833 22.082\n",
"52042 4.7953 22.051 0.217 0.828 -38.425 48.015\n",
"52043 -0.4586 18.636 -0.025 0.980 -36.984 36.067\n",
"52046 -20.2794 37.823 -0.536 0.592 -94.411 53.853\n",
"52052 -4.1976 11.100 -0.378 0.705 -25.954 17.558\n",
"52053 3.9616 26.352 0.150 0.881 -47.688 55.611\n",
"52057 4.0086 12.493 0.321 0.748 -20.478 28.495\n",
"52060 1.7497 6.197 0.282 0.778 -10.396 13.896\n",
"52068 -0.4140 27.270 -0.015 0.988 -53.862 53.034\n",
"52076 -2.8712 26.799 -0.107 0.915 -55.396 49.654\n",
"52079 -29.2503 28.291 -1.034 0.301 -84.700 26.200\n",
"52084 -75.9708 10.855 -6.999 0.000 -97.246 -54.696\n",
"52087 -7.0022 10.521 -0.666 0.506 -27.624 13.619\n",
"52101 -8.5698 6.475 -1.324 0.186 -21.260 4.120\n",
"52136 6.2559 8.938 0.700 0.484 -11.262 23.773\n",
"52142 -18.3814 24.589 -0.748 0.455 -66.576 29.813\n",
"52144 5.0966 16.250 0.314 0.754 -26.753 36.946\n",
"52146 -34.7268 29.438 -1.180 0.238 -92.424 22.970\n",
"52151 -3.3330 16.772 -0.199 0.842 -36.205 29.539\n",
"52154 31.1452 31.836 0.978 0.328 -31.253 93.543\n",
"52159 -10.3523 10.063 -1.029 0.304 -30.076 9.371\n",
"52162 -14.9808 30.067 -0.498 0.618 -73.911 43.949\n",
"52166 5.3714 27.032 0.199 0.842 -47.611 58.354\n",
"52172 -2.7345 7.582 -0.361 0.718 -17.595 12.126\n",
"52175 -2.7737 11.333 -0.245 0.807 -24.986 19.439\n",
"52205 2.7027 8.758 0.309 0.758 -14.463 19.869\n",
"52207 -33.4341 39.936 -0.837 0.402 -111.708 44.840\n",
"52208 -9.0768 10.278 -0.883 0.377 -29.221 11.068\n",
"52211 -5.4183 14.792 -0.366 0.714 -34.411 23.574\n",
"52213 5.0373 15.555 0.324 0.746 -25.450 35.524\n",
"52214 -21.0266 17.857 -1.178 0.239 -56.025 13.972\n",
"52223 7.3775 61.334 0.120 0.904 -112.835 127.591\n",
"52224 -7.3855 33.504 -0.220 0.826 -73.052 58.281\n",
"52227 -13.2876 31.092 -0.427 0.669 -74.226 47.651\n",
"52228 -20.8334 33.964 -0.613 0.540 -87.402 45.735\n",
"52233 -37.8710 10.536 -3.595 0.000 -58.520 -17.221\n",
"52240 -6.9281 3.548 -1.953 0.051 -13.881 0.025\n",
"52241 18.2449 4.222 4.321 0.000 9.969 26.521\n",
"52245 -2.4160 7.422 -0.326 0.745 -16.963 12.131\n",
"52246 3.8799 6.956 0.558 0.577 -9.755 17.514\n",
"52248 -8.8408 30.739 -0.288 0.774 -69.088 51.406\n",
"52253 -1.5028 17.409 -0.086 0.931 -35.624 32.619\n",
"52254 -8.4216 35.466 -0.237 0.812 -77.934 61.091\n",
"52301 13.9906 10.141 1.380 0.168 -5.885 33.866\n",
"52302 -1.7461 4.912 -0.355 0.722 -11.374 7.882\n",
"52303 6.3331 10.913 0.580 0.562 -15.055 27.721\n",
"52306 -56.6558 36.014 -1.573 0.116 -127.242 13.930\n",
"52310 0.9092 6.159 0.148 0.883 -11.162 12.980\n",
"52314 -7.1847 6.334 -1.134 0.257 -19.598 5.229\n",
"52316 6.2083 36.014 0.172 0.863 -64.378 76.794\n",
"52317 -14.1641 6.122 -2.314 0.021 -26.162 -2.166\n",
"52324 -40.5398 33.062 -1.226 0.220 -105.341 24.261\n",
"52327 4.2951 13.854 0.310 0.757 -22.859 31.449\n",
"52328 11.7279 143.749 0.082 0.935 -270.016 293.472\n",
"52332 -2.3405 11.465 -0.204 0.838 -24.811 20.130\n",
"52333 -4.5720 15.731 -0.291 0.771 -35.404 26.260\n",
"52336 -9.5144 39.193 -0.243 0.808 -86.331 67.302\n",
"52337 -99.9479 67.798 -1.474 0.140 -232.831 32.935\n",
"52338 44.7882 13.262 3.377 0.001 18.795 70.781\n",
"52340 -8.7741 17.857 -0.491 0.623 -43.773 26.225\n",
"52342 -1.7379 7.709 -0.225 0.822 -16.848 13.372\n",
"52345 -15.4194 37.823 -0.408 0.684 -89.551 58.712\n",
"52347 4.3374 25.523 0.170 0.865 -45.686 54.361\n",
"52349 4.0480 14.367 0.282 0.778 -24.111 32.207\n",
"52351 2.9520 37.189 0.079 0.937 -69.938 75.842\n",
"52352 -30.0249 45.515 -0.660 0.509 -119.234 59.184\n",
"52353 -10.7119 6.090 -1.759 0.079 -22.649 1.225\n",
"52356 -6.1172 13.209 -0.463 0.643 -32.007 19.772\n",
"52358 9.0290 12.789 0.706 0.480 -16.038 34.096\n",
"52361 2.7874 12.765 0.218 0.827 -22.231 27.806\n",
"52401 10.6881 6.225 1.717 0.086 -1.513 22.889\n",
"52402 -4.6156 3.435 -1.344 0.179 -11.348 2.117\n",
"52403 -9.0701 6.396 -1.418 0.156 -21.607 3.466\n",
"52404 -14.8756 3.948 -3.768 0.000 -22.613 -7.138\n",
"52405 -3.3481 4.202 -0.797 0.426 -11.584 4.888\n",
"52411 4.8312 6.146 0.786 0.432 -7.214 16.877\n",
"52501 4.3080 4.328 0.995 0.320 -4.175 12.791\n",
"52531 0.2523 11.270 0.022 0.982 -21.836 22.341\n",
"52537 -1.2295 14.470 -0.085 0.932 -29.590 27.131\n",
"52544 -10.4153 6.950 -1.499 0.134 -24.037 3.207\n",
"52553 -11.5573 36.013 -0.321 0.748 -82.142 59.028\n",
"52554 -21.6532 32.638 -0.663 0.507 -85.622 42.316\n",
"52556 -10.7956 7.552 -1.429 0.153 -25.598 4.006\n",
"52565 5.2998 14.136 0.375 0.708 -22.405 33.005\n",
"52571 -5.4152 24.950 -0.217 0.828 -54.317 43.486\n",
"52577 -10.5555 5.756 -1.834 0.067 -21.837 0.726\n",
"52591 1.0685 12.001 0.089 0.929 -22.454 24.591\n",
"52601 -11.7506 4.454 -2.638 0.008 -20.480 -3.021\n",
"52623 -29.5609 52.538 -0.563 0.574 -132.535 73.413\n",
"52625 -2.9976 49.358 -0.061 0.952 -99.737 93.742\n",
"52626 -1.7682 36.014 -0.049 0.961 -72.354 68.817\n",
"52627 -9.2686 5.976 -1.551 0.121 -20.982 2.445\n",
"52632 6.9464 5.483 1.267 0.205 -3.799 17.692\n",
"52637 -5.7046 11.925 -0.478 0.632 -29.077 17.668\n",
"52639 -28.4896 33.063 -0.862 0.389 -93.292 36.313\n",
"52641 2.6482 6.588 0.402 0.688 -10.264 15.561\n",
"52653 -0.1926 14.793 -0.013 0.990 -29.186 28.801\n",
"52655 -12.7123 7.811 -1.628 0.104 -28.021 2.596\n",
"52656 4.4909 15.554 0.289 0.773 -25.995 34.977\n",
"52722 3.2800 4.125 0.795 0.426 -4.804 11.364\n",
"52726 9.9088 10.153 0.976 0.329 -9.991 29.809\n",
"52728 -30.7783 33.964 -0.906 0.365 -97.346 35.790\n",
"52730 -23.3074 17.409 -1.339 0.181 -57.429 10.814\n",
"52732 -5.5050 4.526 -1.216 0.224 -14.376 3.366\n",
"52733 -22.7024 12.957 -1.752 0.080 -48.097 2.693\n",
"52738 -70.2835 12.179 -5.771 0.000 -94.154 -46.413\n",
"52742 9.6636 14.540 0.665 0.506 -18.833 38.161\n",
"52747 3.3977 13.158 0.258 0.796 -22.392 29.187\n",
"52748 -12.7320 8.096 -1.573 0.116 -28.600 3.136\n",
"52751 -2.0188 25.523 -0.079 0.937 -52.043 48.005\n",
"52753 6.5331 8.703 0.751 0.453 -10.525 23.591\n",
"52761 -3.0989 4.247 -0.730 0.466 -11.422 5.224\n",
"52768 -4.0831 32.638 -0.125 0.900 -68.054 59.887\n",
"52772 2.2827 9.380 0.243 0.808 -16.102 20.668\n",
"52776 -4.9321 12.559 -0.393 0.695 -29.548 19.684\n",
"52777 -2.4986 39.936 -0.063 0.950 -80.773 75.776\n",
"52778 6.8460 11.724 0.584 0.559 -16.133 29.825\n",
"52801 -23.7006 143.749 -0.165 0.869 -305.444 258.043\n",
"52802 -60.1258 5.848 -10.282 0.000 -71.587 -48.665\n",
"52803 -111.9066 7.277 -15.378 0.000 -126.169 -97.644\n",
"52804 -27.7476 4.444 -6.244 0.000 -36.457 -19.038\n",
"52806 -13.1925 4.913 -2.685 0.007 -22.823 -3.562\n",
"52807 7.5408 4.189 1.800 0.072 -0.670 15.751\n",
"56201 -7.0070 42.452 -0.165 0.869 -90.212 76.198\n",
"712-2 -4.7017 15.138 -0.311 0.756 -34.372 24.969\n",
"==============================================================================\n",
"Omnibus: 367270.389 Durbin-Watson: 2.000\n",
"Prob(Omnibus): 0.000 Jarque-Bera (JB): 67275238939.284\n",
"Skew: 5.961 Prob(JB): 0.00\n",
"Kurtosis: 2451.746 Cond. No. 9.85e+03\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
"[2] The condition number is large, 9.85e+03. This might indicate that there are\n",
"strong multicollinearity or other numerical problems.\n",
"\"\"\""
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Zip Code Statistics\n",
"model_zip.summary()"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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ct9HX397e3vDNnD9/fqPdkiRJakFvvmU8d64qc83mt/utZmyrX/4ZIrvuumvD/RMnTux1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\nn833r3XSe/irMt82IrbtYP3HyMzrM/Mjmfn7VI8i2Be4FHg8cHJEbDO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"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 260,
"width": 441
}
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"MSE: 325336187.622\n"
]
}
],
"source": [
"#County Scatterplot\n",
"plt.scatter(predictions_county, y, s=30, c='r', marker='+', zorder=10)\n",
"plt.xlabel(\"Variables\")\n",
"plt.ylabel(\"Sale Dollars\")\n",
"plt.plot(predictions_county, np.poly1d(np.polyfit(predictions_county, y, 1))(predictions_county))\n",
"plt.show()\n",
"print(\"MSE:\", model_county.mse_model) ## mean squared error"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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1/vmF2/GBfSZufqCkYkqGub+stz3D0En19kuZ+U6AiPgQcBbwN0BTYS4zz6Tq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\nl3pcGBG3lPqdERGrFz4SzdTL+YmI3ee5njIiTp5nPwM/1hF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"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 260,
"width": 441
}
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"MSE: 85316215.6492\n"
]
}
],
"source": [
"#City Scatterplot\n",
"plt.scatter(predictions_city, y, s=30, c='r', marker='+', zorder=10)\n",
"plt.xlabel(\"Variables\")\n",
"plt.ylabel(\"Sale Dollars\")\n",
"plt.plot(predictions_city, np.poly1d(np.polyfit(predictions_city, y, 1))(predictions_city))\n",
"plt.show()\n",
"print(\"MSE:\", model_city.mse_model) ## mean squared error"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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nrIqIA4GPA0dTXSlcQtVG4VOZee/6vw2SJEk9M+m8uXRs8vrYjbXNmlGmkKRN\nVjLMvbTedg9Dx9fbr2Xm+wAi4hTgDODdQJ/CXGaeTtUjrqfjzwXO7eWxzgfO34Txa6me4/viJsxZ\nAXyq/pAkSSrm4KsXcPO81UVqPXnsdIYPe8bdUJL6Uckwty2wssHzaq+l6o32n132fYUqzP1dweNL\nkiSpgf/36zb+5/fLitR69J3T2GpEy6yhJzW1kmFua6rnuv6iXiBkKjA3M//QuT8z2yOijSoASpIk\naTM4775lfOimtiK17jliCs/aqqW6WklNr+R/kYuAbSNiUpdG3a+ptzc2GD8CWFrw+JIkSQJ+8dhK\nDv3xk0VqXXvItrxs22csBC5pECgZ5u4AXke1aMdpETGaatn+BH7WdWBETKVqzP2ngseXJEka0ua0\nr2Hm93rTaveZznv1JN68w+gitSRtHiXD3P9QNdU+JSIOA8YD04HFVCtFdrVfvb274PElSZKGpEUr\n17Hj//a9TxzAKS/Zmn/afVyRWpI2r2JhLjOviIh/A/4f8IJ69yLgXZn5VLfhx9bbnyFJkqReWb0u\n2e6CMr3iDt1+S87fb5sitST1j6JPsWbmJyLia1SrVC4BfpOZf/XUbUSMoOpF9yPgByWPL0mSNBRk\nJhMLNfx+1tjh3PO2qUVqSepfxcJcROxW//GhzPzu+sZl5hrgS6WOK0mSNJS86DtP8OiydUVqLT5u\nOhH2ipOaVckrc3cBHVStCFylUpIkqaBjf/4kVzy8skit+cdMZ+RwQ5zU7EqGuXago0HTcEmSJPXS\nmXct4bN3dl9+oHceevtUJm05vEgtSQOvZJi7H3hJRGyZmWV+bSRJkjREXf7HFRx3/aKND+yBW96y\nHTtPGFGklqTBo2SYuxCYCRwDfK1gXUmSpCHjjgWr2f+qBUVqXfG6bdh3+pZFakkafEqGua8ABwD/\nGRHrgNmZ2VGwviRJUst6dOlaXvTdeUVqffEVE5j1t2OL1JI0eJUMc+cCbcBaqitz/xYRtwELgPUt\nuZSZ+e6C5yBJktRUlq7p4FnferxIrfe+YCz//vIJRWpJGvxKhrnjgAQ6l0aaDBy0kTkJGOYkSdKQ\ns64j2eb8Mr3iXr7dSK55w7ZFaklqHiXD3KcL1pIkSWpZE2bPLVIngMWzZhSpJan5FAtzmWmYkyRJ\n2oDXXDWfWxesKVJr0XHTGWbDb2lIK3llTpIkSQ187OY2vv6HZUVqzX3nNMaOGFaklqTmZpiTJEna\nTM79w1Iv0jF2AAAgAElEQVROvrm9SK3fvW0qM8ba8FvS0zZbmIuIqcB0YCxPL4ryDJl5w+Y6B0mS\npIHw87krectPnixS6/o3bsvuk0cWqSWptRQNcxExDPgw8AFg+x5MydLnIEmSNFDua1vDnt+fX6TW\n+ftN4tDtRxepJak1FQtSdZC7Ang91ZW4NmAC0AE8RtWqYMt6+DJgYaljS5IkDaQnV67jef/7RJFa\np710HCe/eOsitSS1tpJXxWYBbwAeB96Wmb+KiA5gfmY+pw57rwTOAPYAPpWZFxQ8viRJUr9atS6Z\nckGZXnGH7TCab756UpFakoaGkmHunVS3TX4sM3/V/cXM7ABuiIj9gKuAb0TE/Zn564LnIEmStNll\nJhPPKxPinrvVcH57xNQitSQNLSXD3K719vvd9v/VskuZuS4iPgzcC3wUOLzgOUiSJG1WL7jkcR5f\n3lGk1uLjphP2ipPUSyXD3FZAe2au6LJvJfCMm74z8w8RsQTYq+DxJUmSNpt3XvskVz2yskit+cdM\nZ+RwQ5ykvikZ5uYB0yJiWH1LJcAC4FkRMT0z/3IvQv383GieXhBFkiRpUPrcnUv43F1PFan1x6On\nMXGUDb8llVEyzP0JeBZVb7lH63131PveAnyly9hDgBHAnwseX5IkqZjLHlrOu3+xuEit2w7bjr8Z\nP6JILUnqVDLM/RTYG3gNMLvedxFwKPC5iBgD3EX1bN1pVIulXFnw+JIkSX1224LVHHjVgiK1fnDQ\nZPaZNqpILUnqrmSY+x7wj1TtCWYDZOalEXE58Gbgc13GBvAA8MmCx5ckSeq1Py9dy67fnVek1pf2\nnsAxO48tUkuS1qdYmMvM31E1Bu/uCOC9VKtWPgtop7qKd1Zmlrl3QZIkqZeeWtPBs7/1eJFa73/h\nWP5tzwlFaknSxpS8MtdQZq4Dvlp/SJIkDQrrOpJtzi/TK27vqSP54cHbFqklST212cOcJEnSYDNh\n9twidUYMgwXHzihSS5I2lWFOkiQNGftfOZ87Fq4pUmvRcdMZZsNvSQOoV2EuIo4pdQKZeUGpWpIk\nSY185KY2vnnfsiK15r5zGmNH2CtO0sDr7ZW586haC5RgmJMkSZvF13+/lI/9ur1Ird8fOZVpY4YX\nqSVJJfQ2zN1AuTAnSZJU1M8eXcnhP32ySK3r37gtu08eWaSWJJXUqzCXma8ufB6SJEl99vvFa3jF\n5fOL1Lpw/0m88bmji9SSpM3BBVAkSVLTW7ym3AqVp79sHB/abesitSRpc2r6p3cjYpuIeE9EfD8i\nHoiIFRHRHhE3RsS7I6Lh1xgRe0XE1RGxKCKWR8TdEfGhiFjvzfARcUhEXF/XXxoRv4mIYzdyfsdG\nxC31+PZ6/iEbGD+8Po+7669lUX2ee/X8uyJJ0tCwal0y88YxvPY3Y/pc64gdR9M2a4ZBTlLTaIUr\nc0dQNSR/HPg58AgwBTgM+AZwcEQckZl/ecYvIg4FLgNWApcAi4A3Al8E9q5r/pWIOBH4MvAk8C1g\nNXA4cF5E7JqZH20w5yzgZOBR4OvASOAo4MqIOCkzz+42PoCL67r3AWcDk4AjgRsi4q2ZeUUvvkeS\nJLWUzGTieWUafj9v3HBuf+vUIrUkqT/1tjXBdYWOn5l5QB9r3A+8CfhhZnZ07oyIU4BbgLdSBbvL\n6v3jqILVOuDVmXlbvf804Drg8Ig4KjMv7lJre+AsqtC3R2Y+XO//F+BW4OSIuCwzb+4yZy+qIPcg\nMDMzF9f7zwRuB86KiKs6a9WOogpyNwEHZObKes45wI3A1yPiusx8qo/fM0mSmtZO//s4C1Z2bHxg\nDyw+bjphrzhJTaq3V+ZeXej4fV4RMzMbBsvMfKIOQZ+lOt/L6pcOB7YFLugMcvX4lRFxKnAt8H6q\nK2SdjgdGAZ/vGr4yc3FE/CtwLvA+4OYuc95Xbz/bGeTqOQ9HxFeA04BZwKe6zHl/vT21M8jVc26N\niEuAd9XnP3u93xBJklrUUT97kmv+vHLjA3tgwbHTGTHMECepufU2zM0qehabz5p6u7bLvv3r7TUN\nxt8ALAf2iohRmbmqB3N+1G1MT47zI6owtz91mIuIUcBe9fF/uZ4576rnGOYkSUPGGXcs4azflrkp\n5eGjpzFhVNMvGSBJAESXR8laSkRsAdwJvAg4KDN/XO+/FdiD6nbJ2xvMuwfYBXhhZv6+3rcAmAxM\nzsxnNK2JiKXAWGBsZi6PiLHAUmBpZj7jKeqImAwsAOZn5pR63y7APcA9mblrgzl7UN3SeUtm7tno\na25vb2/4Zs6ZM6fRbkmSBrVr5g/ntPtHFan1vZet4NmjW/NnHknNaaeddmq4f/z48T2+baAVFkBZ\nn89RBbmrO4NcbXy9bV/PvM79EzZxzth63PLNeIzucyRJajl3LxnGu+/eskitc3ZdycvGl3m+TpIG\nm5YMcxHxD1SLj/yB6tbETZpebzfl13e9mdNfx1hv6u9PnVcHB8O56Gm+L4OT78vg5Xuzef3pqbW8\n+NJ5RWp9ee8JvGvnsUVqqXf872Vw8n1pLcXDXESMAN4BvA14KbBN/dKTwB1UrQC+nZlrGlfo8/E/\nCPwXcC/VipCLug3pvMI1nsbGdRvX+efJ9Zxn3GbZZc6SHh6j0VW43pyXJElNb8nqDp5z0eNFap24\ny1ac8Xfr+6dUklpL0TAXEc8Dvk/1zFn3ez23Aw4CXgd8JCIOy8wHCx//Q1S94u6hCnLzGwy7j+qZ\nuZ2pWgR0nb8FsAPVgikPdZszuZ5zc7c506husXw0M5cDZOayiJgLzIiIaZnZ/V+ozl+F3N9l3wNU\n7RJ2jIgtMnNtD+ZIktS01nYkk88v0ytuj/Hr+NlhzylSS5KaRbHlnOr+bddSPae2Fvhf4ATg4Prj\nhHrfWmBX4KcR8YzFQfpw/P9HFeTuAvZbT5CDqpccVMGyu32AMcBNXVay3Nicg7uN6dWc+ng31cd/\n1SYcR5KkpjNh9twiQW708ODWVy7nq7uu2vhgSWoxJdfm/QjwHOBPwEsy8x2ZeW5m/rj+ODcz30F1\n6+UjwHPrOX1WN/z+HNWVtgMyc+EGhl8KLASOqleI7KyxJXBG/elXu82ZDawCTqwbiHfOmQicUn96\nTrc5nZ9/oh7XOWd74IN1ve4tBjqPe0Z9Pp1zZgJHUq2AeRmSJDWpfa6Yz4TZc4vUWnTcdB4/ZnqR\nWpLUjEreZvkWqsU5js/Me9c3KDN/FxHvBn4KHAZ8ui8HjYhjgX+hukXxl8A/RDxjNc+HM/O8+vhL\nIuIEqlB3fURcDCwC3gQ8v95/Sbdz/mNEfAz4EnBb3cB7NVUD72cB/5GZN3ebc1NEfIEqsN4dEZcC\nI6lC2STgpK4NyGsXU31PDgfujIgrqZ45PBIYDpyQmUuQJKnJ/MOvFnPB/cuL1HrsXdMYs4W94iSp\nZJjbEViemT/f2MDMvDYiltdz+mqHejsc+NB6xvwCOK/L8S+PiH2BTwBvBbakembtI8CXskHzvcz8\nckQ8DHwUOIbqqua9wKmZeX6jg2bmyRFxN3Ai8F6gg2oRmDMz86oG4zMi3k51u+XxwEnASqpm5mdk\n5k3r/zZIkjT4fPV3S/nnW8qs3fWHI6cydczwIrUkqRU0fWuCzDwdOL0X834FvH4T51wJXLmJc84H\nGoa99YxfS/Xs3xc35TiSJA0mP/nzSt72s0YLQG+6G960LbttM7JILUlqJSXD3IPArhGxf2ZucJGO\niDiAaqGP/yt4fEmSNMB+t2gNe1+xvjXINs23D5jE658zukgtSWpFJcPc5cBuwDcj4uDM/H2jQRHx\nYuBcqufrvlfw+JIkaYDMX7GOnS9+okitz+wxjpN2LbbgtSS1rJJh7j+A46hWtLwrIi4Hfg7MBUZR\nrV65H9US+wE8DHyh4PElSVI/W7k2mXphmV5xRz5vNP+zz6QitSRpKCgW5jLzqYg4kGrp/F2pVmQ8\nvNuwzmUm7wbemplPlTq+JEnqP5nJxPPKhLidx2/BLYdNKVJLkoaSogugZOYDde+2I6mC3EuBbeuX\nF1Ct5HgpcElmril5bEmS1D92+PZjLF71jIWfe2XxcdNp0FJIktQDxVezrEPat+oPSZLUIo74yUJ+\nOndVkVoLj53OFsMMcZLUF03fmkCSJG1e/3J7O1+4e2mRWg8fPY0Jo2z4LUklFA9zETEKeBYwiWrF\nysXAo5lZ5ld5kiSpX1z8wHLe98vFRWrd+dYp7DDO3yFLUklF/q8aESOBE4CjgZcBI7oNWRMRt1Hd\nevnNzFxd4riSJDWb8RMmbPD19ra2fjqT9fv1vFUcdPXCIrWuPngye00dVaSWJOmv9TnMRcSLgB9Q\ntR6Ap1es7GoksBfwCuDkiHjT+vrQSVJ3zfDDr9QTG/u73DlmoP5OP/zUWna/dF6RWv/9ygkcvdPY\nIrUkSY31KcxFxPbAjcDWVCHuLuBHwP9R3V4JMJGqVcHBwO7A84AbI+IlmflIX44vqfUN9h9+pVbQ\nvrqD5170eJFa//iirfj0zPFFakmSNqyvV+a+AYyjajswKzOvXs+4i4FPRMQbgG8Ck4GvAQf18fiS\nJKmXVq9LtrugTK+4/aaP4vuvm1ykliSpZ3od5urbK/cHVgEHZ+YdG5uTmT+MiNdTXc17TUS8MDPv\n7e05SJKk3pkwe26ROluPCP78zulFakmSNk1frswdUW/P70mQ65SZt0fEBcB7gLcBp/fhHCRJ0iYo\nFeLAht+SNND6Eub2oGo9cFEv5n6LavXLmX04viRJg0IzLNKz/UWP0bY6i9R6/F3TGb2FIU6SBlpf\nwtwL6u1tvZjbOecFGxwlSdIgN9gX6Tnu54u4/OEVRWrdd+RUpowZXqSWJKnv+hLmJgDLM3OT/4XI\nzBURsbSuIUmSCjv7nqc49dYlRWrdeOh2vGhS9xaykqSB1pcwtzXVKpa9tYxqVUtJkoaE9ra2zX5L\n5s/nruQtP3myTzU6XXzgJA569ugitSRJ5fUlzJW4z2JYgRqSWlh//PAr9afN9ff1j0vW8pLLyjT8\nPmPmOE580dZFakmSNp++9pmTpM3OsCat31NrOnj2t8o0/J4wMnj4HbYZkKRm0dcwt21EPNTbuX08\ntiRJQ1ZHJpPOK9PwG6Bt1oxitSRJ/aOvYW44sH0f5pdZI1mSpCGkZK84Q5wkNa++hLlPFzsLSZK0\nUSVD3PxjpjNyuL3iJKmZ9TrMZaZhTlJTcSEVbQ79sUjPvj+Yz2+fXNOnGp3mHDWVbUfbK06SWoEL\noEgaEgZ7Y2c1t8319+aff9PGV+9dVqTW9W/clt0njyxSS5I0OBjmJEkaZC55cDl/f8PiIrW+se9E\nDt9xTJFakqTBxTAnSdIgcefC1ex35YIitU560VZ8Zub4IrUkSYOTYU6SpAE2b/k6nn/JE0VqvWzy\nCK5943ZFakmSBjfDnCRJA2T1umS7C+wVJ0nqHcOcNMR0XQhkjwavt8ICID1Z7EQaSJnJRBt+S5L6\nyDAnDSFDYUXHwRbkbIeg7kr2ilt03HSGhb3iJGmoMsxJ6ldDKdwMhfDcbAby71/JEPfoO6ex1Yhh\nxepJkpqTYU5SvzHcaCAN1N+/nS9+nPkrOorUuuvwKWy/tf90S5Iqm+1fhIiYAjwbGJOZN2yu40iS\nNBiddONiLpyzvEitK163DftO37JILUlS6yge5iLiSOATwC71rux6nIiYAHwXCOAtmflU6XOQJGmg\nXDRnGR+8sczVvX/7u/G8f5etitSSJLWeomEuIj4HfIwqqK0CRtR//ovMbIuIJ4CjgTcBF5U8B0nS\n0DTQi9/86olVvOFHC4vUOnzH0Xxj30lFakmSWlexMBcRrwX+CWgHTgC+DzwKNOpcej7wDuAtGOYk\nDRI+q9e8BjLIzV+xjp0vLtPwe/KWw3jg7dOK1JIktb6SV+ZOpLql8mOZeSlArH+55JvrsS8teHxJ\nQ0hff3g3uKmv1nYkk8+3V5wkaeCUDHN71ttvb2xgZi6LiHZgasHjS9qI9ra2lmgNYJDTQCvZZsAQ\nJ0nqrZJhbgKwJDN7unTX8FIHjojDgX2B3YEXA1sDF2XmOxuM3R744wbKXZKZR63nOMcCHwReCKwD\n7gTOysyr1jN+OHAScDywE7AC+DVwRmbetJ45o4GPA0cBzwWWANcDn8rM32/gvKUe6Rpk5syZA8BO\nO+00UKezWQ10aGuV8KynzbxxTLFa846ZzqjhNvyWJPVeyTC3CNguIsZsLNBFxA5UgevhQsc+lSrE\nLaV6Tu9vezDnt8DlDfbf02hwRJwFnFzX/zowkipwXRkRJ2Xm2d3GB3AxcDhwH3A2MAk4ErghIt6a\nmVd0mzMK+CmwN3Ab8F9U7R2OAN4QEftn5m968LVJg9JQDDet9vW0uvW9XyWvxP3ubVOZMbbY7zMl\nSZug1X4OKRnmbgEOqT++s5GxJ9fbXxY69oepQtYDVFfoft6DOXdl5uk9KR4Re1Gd84PAzMxcXO8/\nE7gdOCsirsrMh7tMO4oqyN0EHJCZK+s55wA3Al+PiOu6tWb4CFWQuxQ4MjM76jmXUAXPb0bErp37\npWbUl/9JDvRqhWp+m/r3b/qFj7F8bRY59lUHT+aVU0cVqSVJ2nQ9+Tli/IQJTRXohhWs9Q2qNgT/\nGhHPbTQgIoZHxKnAB6gWQDmnxIEz8+eZOSczy/yL+0zvq7ef7Qxy9XEfBr4CjAJmdZvz/np7ameQ\nq+fcClwCbEsV9oC/XMnrPM4/dQ1s9RW8X1Ld3rlvga9HajoGOfWnD964mAmz5xYJcp+ZOY62WTMM\ncpKk4oqFucy8kmrxkx2BOyLiXGAsQEScGBH/TXVb5afrKV/NzJtLHb8XpkfE30fEKfV2tw2M3b/e\nXtPgtR91G9N5u+RewHIaX318xhzgecBzgPszs9EzfY3mSJIK+s6Dy5kwey4Xzenp49/r96yxw2mb\nNYOTXrR1gTOTJOmZijYNB44DFlAt+tF5pSqpnv2C6spdB/AF4P8VPvamek398RcRcT1wbGY+0mXf\nWGAGsDQzH29QZ0693bnLvr+hWuDlocxc28M5z6+396/nfBvN6ZHORS4Gg8F0LkPRHjNn/vXn3V6/\n7dZb+/0culvfOXQ/155o9r9vzX7+/erWW3v2d2s939OHlgdH3jG63Om8sgqDc+Y8tZGRKsn/ZgYn\n35fBaSi+Lz39WaK/vjclFqArGubq4PLhiPgKcCzwCmAa1RXAeVT95c7PzD+UPO4mWg58huoZtIfq\nfbsBpwP7AddGxO6Zuax+bXy9bV9Pvc79Xe8B6685Uo9t7AfdzjGbM9ANhnNQ6+rN35vl62Dfm8ut\nUNkZ4iRJ6g+lr8wBkJkPAKdtjtp9lZnzgU92231DRLyWamGSPYH38PTVxB6X3oSxnWtRb+45wOBY\ndr7Vl8DvTz15dqwvD+4OhveoxDm0t7Ux8F9J7/T1v5dWW6lrc8hMJp5nw+9W4b8xg5Pvy+Dk+7Jx\nzfS92Sxhrhll5tqI+AZVmNuHp8Nc5xWx8Q0nNr6itrE54wrN0RDT00VAmm0lppKG6tfdqRVX6iqt\nZJuBRcdNZ1jYK06SNDAMc39tQb0d27kjM5dFxFxgRkRMa/DcXGd07/qs2wNUTcV3jIgtGjw312jO\nffV2fc/ENZojSeqhkiHuj0dPY+KokgtCS5K06XoV5iKi+22KvZaZ/1KqVgEvr7cPddt/HfAu4CBg\ndrfXDu4yBoDMXBURNwGvqj+69717xhyqHnaPADtHxA4NVrRsNEdqabYjUAklQ9x5L17JLlt3GOQk\nqQm1t7W13KMIvb0ydzq9eHarm6hr9GuYi4g9gTszc3W3/ftTNR8H+Fa3aedQhblPRMTlXZqGbw98\nEFjFM0PeV6mC3BkR0bVp+EzgSKqrgJd1Ds7MrBuK/yvw7xHRtWn4oXWte4Ff9P6rl5qHQU599exv\nPcZTa8q0Hz3z5eM54QVbDcnV3ySplTRbWNuY3oa5C+h7mCsmIt4MvLn+dGq9fUVEnFf/eWFmfrT+\n8+eBXeo2BI/W+3bj6f5tp2XmTV3rZ+ZNEfEF4CPA3RFxKTCSKpRNAk6qG4h3dTFwGFVj8Dsj4kpg\nm3rOcOCEzFzSbc4XgEPqOb+JiGupes8dQbUK5/Fdm4lLkp7poze38Y0/LNv4wB543bNGcclrJhep\nJUlSab0Kc5l5XOHz6KvdqVohdLVj/QHwJ6AzzF0IvAWYSXXr4giqtgnfAc7OzEZNvsnMkyPibuBE\n4L1U/fLuAM7MzKsajM+IeDtwE3A8Ve+9lcANwBndA2M9Z1VEHAh8HDia6krhEqo2Cp/KzHs3/q2Q\nmp9X5dQbVz+ygqOvXVSsnitUSpIGu5ZYACUzT6e69bMnY88Fzu3lcc4Hzt+E8WuBL9YfPZ2zAvhU\n/SEVMxjuE+/JOfS1voaeR5auZbfvzitWzxAnSWoWLRHmJPVM97AzEL1mNhS4ehP0DHBD15qOZNvz\n7RW3qQb6lzqSpHIMc1IT6emVLX8YG7oGwxXY/lByhcqhEuLAPoSS1GqKh7l6tcb3AXsD0+nSs62B\nzEwDpbQJ/CFLG9PKf0dKhrgn3jWdLbew4bckqXkVDVIR8XHgDKCnDXj8V1SStFElQ9xth23H34wf\nUayeJEkDpViYi4j9qHqkrQM+CVxFtdrjAuAVwBTgQKpVHQHeDdxd6viShp5WvgKlSskQ97V9JvK2\n540pVk+SpIFW8srcSVS95z6Vmf8KEBEA6zLzIeAh4OaI+AZwPdWKki8peHxJg1jJ57gMca3voB8u\n4NfzVxepdfiOo/nGvpOK1JIkaTApGeb2rLdf67b/r265zMzHI+IDwE+BU4APFjwHSYNQTxddkM6+\n5ylOvXVJsXpDaXETSdLQUzLMTQaWZebCLvvWAo3uabkOWEHVtFuSgM27EuNQWOGxmd2+YDUHXLWg\nWD1DnCRpKCgZ5hYD4xrsmxwR4zOzvXNnZmZEdADTCh5fGhJaPZRsjvN3OfbBq21VB9t/+/Fy9Qxx\nkqQhpKerTvbEo8CoiNi2y7576+2ruw6MiBdTtSxYVvD4UsvzdkW1isxkwuy5xYJc26wZBrke6Mkv\nLPylhiQ1j5JX5n5FtaDJHsCP6n0/APYFzoqIx4C7gF2Bb1ItlvKLgseXJDWBkitULjpuOsPCLjeb\nwrAmSa2jZJj7PtWKlsfydJj7KlUD8Z2AX3cZG8By4PSCx5ckDWIlQ9yDb5/KNlsOL1ZPkqRmVDLM\n3UB11e0va0ln5sqI2Bf4L+BNwCiqK3I3Ax/OzP8reHxJ0iBUMsT96PWTecWUUcXqSZLUzIqFuczs\nAH7XYP8TwJERMYJqxcslmemzcpLU4qZcMJdV68rUOvWl4/joi7cuU0ySpBZR8srcBmXmGqDckmWS\nmsbmbDmgwecff7WY8+9fXqTWLhO34FdvnlKkliRJrabfwpyk1tWToGZYa31XPLyCY3++qFg9V6eU\nJGnDNnuYi4iTgOOBnamep7sL+M/MvGJzH1vS5tcMPdy8Mrh5PfzUWna/dF6xeoY4SZJ6ptdhLiL2\nAH5C1Rj8hZm5qsGYi4EjOj8FRlO1KtgnIk7JzM/39vjSUNTMoWQwBDqVtXpdst0FjxWrZ4iTJGnT\n9OXK3P7ABOCi9QS5o4G31Z/OA66gahL+ZmAH4DMR8YPM/H0fzkEacpo5lAx0oFM5JVeoNMRJktQ7\nfQlz+1C1Gfj+el7/x3r7CPCyzHwSICJOBW4EdgfeDXy0D+cgSepHJUPcvGOmM2q4Db8lSeqtvoS5\nHanC3G+6vxARk4GZ9ev/0hnkADJzRUScTnWlbt8+HF+S+lWz3uJaQskQd8dbp7DjONffkiSpr/ry\nr+lU1t8zbq96m8CVDV6/tt7u2IfjSy1lKAeFZtAMC71sDiVD3Df3nchhO44pVk+SpKGuL2FuLLB2\nPa/NrLcPZOaC7i9m5vKIaAfsACsxdIOCBq/9r5zPHQvXFKn19r8Zw1dfNbFILUmS9LS+hLkngSkR\nsV1mzu/22suprsrdtoH5I6laFUhqAj0JnGp+//V/T/Gp25YUq+fiJpIkbT59CXO/BV4LvBP4QufO\n+nm5V9Wf/qLRxIiYStWmYE4fji+pnxjkWt8t81fx2h8uLFbPECdJ0ubXlzB3CfA64JMR8Ufgh8AM\n4CtUV91Wsf6VLjvD3j19OL4k9ZjPJDa2eFUHO3z78WL1DHGSJPWfvoS5C4EPAi8DLu32WgJnZ+b6\nfs17VD3mxj4cX9JmVvqK3EAFJp9JfKbMZOJ5NvyWJKmZ9TrMZea6iDgYuAh4TbeXLwD+udG8iNgR\neFP9aaOVLiUNAr0JckMpDDWzkitULj5uOhH2ipMkaSD0qdFPfeXtdRHxfGDXevftmfnHDUzrAN4M\nrMnMB/pyfP3/9u48XpKqvvv458cog4wwAwKKaAR0QMUl0RmXwRWj0Qgiigl5ohA0JiSiUSExcYlg\nXCNKIpiQJ1FAiYEHiBoUl0Q2FaIzbrggDsu4IIss98LMwODA7/mjTkPT0/fe7nt7qe7+vF+vetV0\n9TlVp6tu3envPVWnJKlzvQxxV/+fXdlh8VY9W58kSepeT57ampmXA5d3WHYdsK4X25Ukza2XIe7L\nL96Jp+yyuGfrkyRJ89eTMCdpYaanpmozQEev75Ory+eaRL0Mcceu2J6/eLyPBpUkqU4Mc1JN9DvU\ntAtVK1q2P+gg11ym7qGuToF7LkdcdDOnX3l7T9b15J3uz1cO2KUn65IkSb1lmJMmQDehar4WGmRG\nYTTJhbRvEEHw7Ks28poLb1nwehocoVKSpHozzElakLoHsDro96MRrrp1M086+/p51W3HECdJ0mgw\nzEnSiNp0V/LgT/isOEmSJpVhTtK82Ss3PL0c3MQQJ0nSaDLMSeqaIW54ehnibjj0oWy9yAd+S5I0\nqgxzknqm16Nh9kId2zQfvQxx3z34wey+nb/+JUkadf5vLqknxiU01U0vQ9ypz92RA3d/QM/WJ0mS\nhlPHytIAACAASURBVGurYTegFyLi4Ig4ISK+GhG3RkRGxGlz1FkVEedGxM0RsTEiLo2IN0bEolnq\n7B8RF0TEdESsj4hvRMRhc2znsIj4Zik/XervP0v5RaUdl0bE7aV950bEqrn3hNR/XmI5GCu/tm3P\ngtxhe23L1OG7GeQkSRoz49Iz93bgicB64BfAo2crHBEHAmcDdwBnADcDBwDHA/sCr2hT50jgBOAm\n4DTgTuBg4JSIeHxmHt2mznHAUaVN/wpsDRwCnBMRr8/ME1vKB3B6We/lwInAjsDvAxdFxMsz87Md\n7A/pPhb6QPBBBDhDYuW1F97MmVdt25N1LV4E1x/q4CaSJI2rcQlzb6IKTFcAzwbOn6lgRGxPFazu\nAp6TmWvK8ncA5wEHR8QhmXl6U53dgeOoQt+KzFxXlr8LWA0cFRFnZ+YlTXVWUQW5K4GVmXlLWf5B\n4FvAcRHxuca6ikOogtzFwPMy845S5yTga8C/RsR5mXnbPPaRJlwjLA37csi6hrZ+tquTMH3a967l\nVY5QKUmSujAWl1lm5vmZuTYzs4PiBwM7A6c3glxZxx1UPXwAf9ZS59XAYuDE5vBVAtp7y8sjWuo0\nXr+nEeRKnXXAR8v6Dm+p09ju2xtBrtRZTdWDuHNpvzQwdQ1fo2Z6aqrt9ONf3kQc/0Nedd7NPdnO\n1OG7GeQkSZoQYxHmurRfmX+xzXsXARuBVRGxuMM6X2gpM686ZXuryva/2sV2pI4sXbasq165RthQ\nf9ydybKTr+ExZ1zXk/UZ4iRJmjzjcpllN/Yu85+0vpGZmyPiamAfYE/gsg7qXBsRG4CHRcS2mbkx\nIpYAuwHrM/PaNm1YW+Z7NS17FLAIuCozN3dYpyNr166du9CA1Kktk2TFypVd1+n2WK3o03oXqo7t\nWvm13twTB/DNfTcS4bk1aO7vevK41JPHpZ48LsO3fPnyBa9jEsPc0jKfnuH9xvLmLoxO6iwp5Tb2\ncRutdaQtzCe4tVqzenUPWtJbc32umdq8ZvXqedfttV6GuP956kaW3r9nq5MkSSNoEsPcXKLMO7n/\nbiF1BrWNnqT+hWr89acObRlnvRjcZHpqitajNNd6G5dkdlJuPj8BnXyuFStXznhZ6FyXi/b7p7KX\nz4o754U78cxdF89dUH3h77J68rjUk8elnjwu42USw1yjh2vpDO9v31Ku8e+dSp2bZqlza4fbaNcL\nN592SX3XSZBaumyZ99i10csQ95pHL+FDT7djXpIk3WsSw9zlVLfR7EX1iIB7RMT9gD2AzcBVLXV2\nKnUuaamzK9Ullr/IzI0AmbkhIq4BdouIXdvcN9f4U0jzPXhXUD0uYc+IuF+b++ba1ZFUQ0848zp+\ntv6unq3PgU0kSVI7kzia5Xll/sI27z0L2Ba4ODM3dVjnRS1l5lWnbO/isv1ndrEdqRaG/fy6Ojju\ne7ex7ORrehbkHKFSkiTNZhLD3FnAjcAhEXHPQHcRsQ3w7vLyn1vqnAxsAo4sDxBv1NkBeGt5eVJL\nncbrt5VyjTq7A68r6zu5pU5ju+8u7WnUWQn8PvAr4Ow5Pp80NJMa6L5z450sO/ka3v3tW+cu3IHV\nz9jI6mds7Mm6JEnS+BqLyywj4qXAS8vLh5T50yPilPLvGzPzaIDMvDUiXksV6i6IiNOBm4GXUD2C\n4CyqB3TfIzOvjoi/BD4CrImIM4A7qR7g/TDgQ5l5SUudiyPiw8CbgUsj4ixga6pQtiPw+uYHkBen\nAy8r6/1ORJwDPKjUWQS8NjN7821RI2cSg1KvPnMn65nP/X4bfn03u53W7ukj89PohavjcNGdDG4j\nSZIGayzCHPCbwGEty/YsE8BPgaMbb2TmZyLi2cDbgJcD21Dds/Zm4COZucWIkZl5QkSsK+s5lKpX\n80fA2zPz1HaNysyjIuJS4EjgT4C7gW8DH8zMz7UpnxHxB1SXW74aeD1wB9XDzN+dmRfPvSs0jgYV\n5Or0hXyQQa5RrpvP38vBTep+KWU3g+BIkqTBGYswl5nHAMd0WefrwO92Wecc4Jwu65wKtA17M5Tf\nDBxfJqkvGl+6Gz1AjeewTWLvX7d6GeKuP/ShLF4UcxeUJElqYyzCnKTOtfae9OIh493oV2BsfK6F\nrH+23qVehrhvHLQLey/zid+SJGlhDHPSLLxPqHvtAlE/e/yat9WP7fQyxH346ct49aOX9Gx9kiRp\nshnmpBl4n9D8Ne+XQVy6WfcQ99RdtuZLL965Z+uTJEkCw5ykEdfrINfLEAf1H9xEkiSNLsOcNGGa\nw8+KWcq1mp6acoCULhjiJElSvxnmJHXMQDc3Q5wkSRoUw5xUYwan0THOIa6TEO+9o5IkDZ5hTmqj\nDiGqDm2oi5mCQh320S1/9FAixv9ZcYY1SZLqxzAnjYl+Bp5hf5EfVGiL43/YcdnvHfxgHrGdv0Il\nSdLw+E1E0n0MO7gNQjehrdW7V27PkY/broetkSRJmh/DnLQAkxB8WvXr/qlBXEq5kBB3/63gV4eN\n731xkiRp9BjmJHVtFEPsQoLcOA9uIkmSRpdhTtJ9OGrhvQxxkiSpzgxzkrqydNmykQt03fbKGeIk\nSdIoMMxJ6lpr710dwt1MgS3ftA/5pn06qmuIGzx7giVJmj/DnNTGIB+S3KsBPob5zLXZeusGtS/n\nCmxz1TU0DF4nP7Oj2BMsSdKgGOakFoPsKegmgHUSihain+v3y7gkSWrwqoze2WrYDZDqpNOeAg2O\nv9AlSRofftfqLXvmJAH1/cW57ORrYI4BTBZyiaUkSdKoMsxJRV3DTEPd29dry06+ZthNkCRJqjXD\nnMTkBaU6q2uI8/p+SZJUN4Y5ST0xW9jpJOjs+alruXnT3b1sUs846qIkSaojw5zUZ/b6zR50/mvd\n7Rx6/s0DbpHU3pzn6+rVg2mIJEkdcDRLaR46DWjjEOT61dt0x+Zk2cnXGOQmWCc/W4Ps7ezkfF2x\ncuUAWiJJUmfsmZM0p9m+UM832C4Fsvw75hitcja3/NFDmT68v8/gU/94aaokSfNnz5wm2tJly8Yu\nBExPTd1nGgXzebTANw/ahanDdyMiAEOBJEmjoG5XZYw6e+Y0sfod4sYtJNbFYXttyz/uu8OwmyFJ\nkubJsNY7hjlNJINc59p9lmH9Ep46fLehbFeSJKmODHOaOL0KWq3rmaS/Mg16GH5DnCRJ0pYMc1KP\n+Jyx3qtLiJuemnuAFY+9JEkaNMOc1EN1uLxy0G3oJOh0qy4hrplhTZIk1Y1hTtK8XT71a5766Rtg\nhkcLdDtK5UJDXD+CpSZHJz8/a3xouCSpRgxzmih+0e+dZSdf09P19ao3rtGD5rHWfMzZA7t27WAa\nIklSB3zOnCaGX+4Hr9OHgXsJoyRJUvfsmZPUV+0C3ceevQMv33PbtuV7NdCIl1xKkqRxZ5iTNFCz\nXU7ZSfhy1FBJkqSKYU5S1zq9fLLZMEaotHeud3w0gyRJ9WOYk3SfL+J1HdhEw2OPqSRJ9WSY09iz\nZ6YzM4W4bh8v0Gr68OF+wR/VB36vWLly1vfr2GZJkjRYEzuaZUSsi4icYbpuhjqrIuLciLg5IjZG\nxKUR8caIWDTLdvaPiAsiYjoi1kfENyLisDnadlhEfLOUny7191/oZ55EBrnO9CvIQT2OwfTU1KxT\n3cwV5KAe+1WSJA3XpPfMTQP/0Gb5+tYFEXEgcDZwB3AGcDNwAHA8sC/wijZ1jgROAG4CTgPuBA4G\nTomIx2fm0W3qHAccBfwC+Fdga+AQ4JyIeH1mntj9x5QkSZI0biY9zE1l5jFzFYqI7amC1V3AczJz\nTVn+DuA84OCIOCQzT2+qsztwHFXoW5GZ68rydwGrgaMi4uzMvKSpziqqIHclsDIzbynLPwh8Czgu\nIj7XWJck1d0oXuIqSdKomNjLLLt0MLAzcHojyAFk5h3A28vLP2up82pgMXBic/gqAe295eURLXUa\nr9/TCHKlzjrgo2V9hy/kg4yzpcuWbTFpbvMZmVLqRKcDp0iSpPmZ9J65xRHxSuA3gA3ApcBFmXlX\nS7n9yvyLbdZxEbARWBURizNzUwd1vtBSppPtfAF4RynzzjbvTzS/EHauEd4a98PNdF9cL0PeTMen\ntVdmVAcrkSRJGobIzGG3YSgiYh3wiDZvXQ0cnpkXNpVdDaygulzyW23W9QNgH+CxmXlZWfYrYCdg\np8y8qU2d9cASYElmboyIJVT36q3PzO3alN8J+BVwQ2Y+uN1nmp6ebnsw165d227xWOlkwAhV4vgf\n9mRgk15Zs3r1sJtQO93+PPd7H3bantZ2zLeeJEmTYPny5W2XL126NDpdxyT3zJ0MfBX4IXAbsCdw\nJPAnwBci4umZ+b1SdmmZT8+wrsby5i6FTuosKeU2znMbkuYwV6AYhyCxYuXKvn6ONatXT8R+lCRp\n1ExsmMvMY1sW/QA4ovSYHQUcAxzU4eoa6bmbbs751JlP+RlT/yA1egfr0BbVSz9/Jjq5/HbFypVj\ncelmv8+tufbRQrY+Sr8X/F1WTx6XevK41JPHZbw4AMqWTirzZzUta/SKLaW97VvKdVPn1g7Lz9Vz\nJ/XcOIScUWQvlyRJ6sTE9szN4oYyX9K07HKqe+b2onpEwD0i4n7AHsBm4KqWOjuVOpe01Nm1rP8X\nmbkRIDM3RMQ1wG4RsWtmXtvSrsafT34yz88lzWtQk9ZAN4mDzSx0UJb51F+zejXLly+fyP0tSZI6\nY8/clp5e5s3B7Lwyf2Gb8s8CtgUubhrJcq46L2ops5A6UsfyTfvUavCTupjtcRYLHV7f4fklSVK/\nTGSYi4h9ImLHNssfAZxYXp7W9NZZwI3AIRGxoqn8NsC7y8t/blndycAm4MjyAPFGnR2At5aXJ7XU\nabx+WynXqLM78LqyvpNn/XCSFmQcg1W7ZzAO4nmMnVym66W8kiTN36ReZvkK4K8j4nyqRxHcBjwS\neDGwDXAucFyjcGbeGhGvpQp1F0TE6cDNwEuAvcvyM5o3kJlXR8RfAh8B1kTEGcCdVA8gfxjwocy8\npKXOxRHxYeDNwKURcRawNfD7wI7A65sfQC6pPpYuW1bLYNJpz2C/2l7HfSJJ0riY1DB3PlUI+y2q\nyyqXAFPA14BPAp/MlgfwZeZnIuLZwNuAl1OFviuogtdHWsuXOieU59kdDRxK1RP6I+DtmXlqu4Zl\n5lERcSn3PibhbuDbwAcz83ML/NxSx2b6Et7Jg70Xuo266PZz1jXQSZKk8TSRYa48EPzCOQtuWe/r\nwO92Wecc4Jwu65wKtA17Uj90G0C6HRTFgDN/nYRn968kSZNpIsOcxsM43ts0quoaJnrZizhMdd2/\nkiRpuAxzGknj8AVdg9EchPy5kSRJ48QwJ6mvvERQkiSpPyby0QSSBsNnrDk8vyRJ6h975jRyxv3L\nv/pnWIOJGNZGz0w/J40HjXpMJUl1YJjTSDHIaaEm7Uu4o2F2b9jP5pMkqVOGOUkzmvQQ0O1omHXd\nH3VtlyRJWhjDnKT76Ca8TELvxLh/PkmSNLocAEWacGtWr77n317GKkmSNDoMc9IEaw5ykiRJGi2G\nOY0Me40kSZKkexnmJPWNz1iTJEnqHwdAkdRXhjVJkqT+sGdOI8FLLCUNij3KkqRRYc+cNCaav1wa\nfuc26c/Q0+xmOv5r164FYPkgGyNJ0gzsmVPtGUzmNqzgMaqBp5OfKX/uJElS3dkzp1qb5C/U01NT\nA+89GsY2JUmSND+GOamGOglVUIXdduGq0/orVq7c4llzhjVJkqTRYJhTbU1ir5xBSpIkSZ3ynjmp\nRpYuWzaRIVaSJEnds2dOtTTpgWbSP/9CeM+fJEmaFPbMqXYMMgs3qfvQUSolSdIkMcypVvyiLUmS\nJHXGMKfaMMhpUDq51NLLMSVJUt15z5xqwSCnQTOsSZKkUWfPnIbOICdJkiR1z545acKtWLnyPq/t\nsVKvOcKoJEn9Yc+chspeufrxmKiXHGFUkqT+sWdOGnF+EZYkSZpM9sxJGhuOUilJkiaJPXMaGnuU\n1A+GNUmSNCnsmZMkSZKkEWSY01DYKydJkiQtjJdZauAMcuOvk2Ps5ZCSJEkLY8+cBsogN/46Pcb+\nLEiSJC2MYU7SFgxa6hVHGJUkqX+8zFIDsWLlymE3QdKQGNYkSeoPw5z6zl6e4Wn3JdrjIUmSNB4M\nc+obQ8Pg2PMhSZI0ebxnroYi4mER8fGI+GVEbIqIdRHxDxGxw7Db1omly5YZ5CRJkqQ+s2euZiLi\nkcDFwC7AZ4EfA08B/gJ4YUTsm5k3DbGJszLESZIkSYNhz1z9/BNVkHtDZr40M/86M/cDjgf2Bt4z\n1NbNwiAnSZIkDY5hrkYiYk/gBcA64KMtb78T2AC8KiKWDLhpqrG63S/XaXvq1m5JkqRR42WW9bJf\nmX85M+9ufiMzb4uIr1OFvacBXxl041QPCw1B01NTc/ai9mIbkiRJ6i/DXL3sXeY/meH9tVRhbi+6\nCHNr165dYLPmlgk+Sa57a1avBrp7Dl9PjmfZ7iwbWfg2tGCDOHc1Px6bevK41JPHpZ48LsO3fPny\nBa/DMFcvS8t8eob3G8trc3Na5rBbUB9rmgJSJ+Gsufya1au7riNJkqTJZpgbLVHmXUWoXqT+VtmU\n4iJilpKjrZNLEhvllre8nkvrUZlPnYVq/FWuHz8jmj+PS315bOrJ41JPHpd68riMF8NcvTR63pbO\n8P72LeWGohHkxjnEwb3hyvu/JEmSVEeGuXq5vMz3muH9xp9QZrqnrq9GuTfOQCZJkqRxY5irl/PL\n/AURsVXziJYRsR2wL3A78L+DbFSnIa7TSxLnYvCSJEmS5maYq5HMvDIivkw1YuXrgBOa3j4WWAL8\nS2ZuGFB77vl3pz1x01NT3HD7Xex1+nX3WX7ggzfz9uV3en22JEmS1COGufr5c+Bi4CMR8TzgMuCp\nwHOpLq982yAbM5/LKXd5wCKmDt/tPssc/laSJEnqra2G3QDdV2ZeCawATqEKcUcBjwQ+Ajw9M28a\nVFtG7b44SZIkaZLYM1dDmflz4PBht0OSJElSfdkzJ0mSJEkjyDAnSZIkSSPIMCdJkiRJI8gwJ0mS\nJEkjyDAnSZIkSSPIMCdJkiRJI8gwJ0mSJEkjyDAnSZIkSSPIMCdJkiRJI8gwJ0mSJEkjKDJz2G1Q\nj0xPT3swJUmSpBG2dOnS6LSsPXOSJEmSNIIMc5IkSZI0ggxzkiRJkjSCDHOSJEmSNIIMc5IkSZI0\nghzNUpIkSZJGkD1zkiRJkjSCDHOSJEmSNIIMc5IkSZI0ggxzkiRJkjSCDHPqq4h4WER8PCJ+GRGb\nImJdRPxDROww7LaNkrLfcobpuhnqrIqIcyPi5ojYGBGXRsQbI2LRLNvZPyIuiIjpiFgfEd+IiMPm\naNthEfHNUn661N9/oZ+5TiLi4Ig4ISK+GhG3lv1+2hx1arn/I2JRacelEXF7ad+5EbFq7j1RL90c\nl4jYfZZzKCPi9Fm20/d9HBEPiIhjI+LyiLgjIm6IiP8XEY/pbq8MV0Q8KCL+OCI+HRFXlM8/HRFf\ni4jXRETb7x2eL/3V7XHxfBmciPhARHwlIn7e9Pm/ExHvjIgHzVDH80X3ykwnp75MwCOB64EEPgO8\nHzivvP4x8KBht3FUJmAdMAUc02Y6uk35A4HNwHrgY8AHyz5P4MwZtnFkef9G4KPA8cDPy7LjZqhz\nXHn/56X8R4GbyrIjh73ferj/v1s+023AZeXfp81Svpb7HwjgzKZz8IOlfetLew8c9r7u13EBdi/v\nf3eG8+jgYe1jYDHwtVJnNfAB4FPAr4ENwFOHva+7OCZHlM/xS+DfgfcBH6f6/ZXAWZSRtD1f6ntc\nPF8GemzuBP63HI/3AyeUz5XANcDDPV+cZv0ZGnYDnMZ3Ar5UTurXtyz/cFl+0rDbOCoTVZhb12HZ\n7YEbgE3Aiqbl2wAXl31/SEud3YE7yi/q3ZuW7wBcUeo8vaXOqrL8CmCHlnXdVNa3ezefs64T8Fxg\nefnP6jnMHhpqu/+BPyh1vg5s07R8ZWnvDcB2w97ffTouu5f3T+li/QPZx8DflDpnAls1LT+wLP9h\n8/I6T8B+wAGt7QUeAvysfJ6XNy33fKnncfF8Gdyx2WaG5e8pn+efmpZ5vjht+bMy7AY4jecE7FlO\n6qvb/OexHdVfajYAS4bd1lGY6C7Mvbrs+1PbvLdfee/CluXvKsuP7XR9wCfK8sPb1JlxfaM+MXdo\nqO3+By4qy5/bps6M6xuFqYPjsjvdfznt+z6mCqI/Lcv3aFNnxvWN2gS8tXyWE5qWeb7U87h4vgz/\nuDyxfJb/blrm+eK0xeQ9c+qX/cr8y5l5d/MbmXkb1V9utgWeNuiGjbDFEfHKiHhrRPxFRDx3huvj\nG/v+i23euwjYCKyKiMUd1vlCS5mF1JkEtdz/ZXuryva/2sV2xs1DI+JPy3n0pxHxhFnKDmIfPxL4\nDeAnmXl1h3VG1a/LfHPTMs+X4Wt3XBo8X4bngDK/tGmZ54u2cL9hN0Bja+8y/8kM768FXgDsBXxl\nIC0afQ8BPtmy7OqIODwzL2xaNuO+z8zNEXE1sA9V7+llHdS5NiI2AA+LiG0zc2NELAF2A9Zn5rVt\n2rq2zPfq5IONmbru/0cBi4CrMrPdl7ZJOWbPL9M9IuIC4LDM/FnTskHt405+V7bWGTkRcT/g0PKy\n+Qui58sQzXJcGjxfBiQijgYeCCwFVgDPoApy728q5vmiLdgzp35ZWubTM7zfWL5sAG0ZBycDz6MK\ndEuAxwP/QnUpzBci4olNZeez7zuts7Rl7vHdUl33/6Qfs43A3wFPprpXZAfg2cD5VJdofqV8iWkY\n1D6elOPyfuBxwLmZ+aWm5Z4vwzXTcfF8GbyjgXcCb6QKcl8EXpCZv2oq4/miLRjmNCxR5jnUVoyI\nzDw2M8/LzOszc2Nm/iAzj6AaTOYBVKOLdWo++36+x8vju6W67v+xPicz84bM/NvM/HZmTpXpIqor\nBL5B9ZflP57PqrsoO8hjXxsR8QbgKKoR7l7VbfUy93zpsdmOi+fL4GXmQzIzqP5o+zKq3rXvRMST\nuliN58sEMsypX1r/0tNq+5Zymp+TyvxZTcvms+87rXNrh+Xn+ivdOKvr/vecbKNcEvRv5WU351Gv\n9vFYH5eIeB3wj8CPqAZGuLmliOfLEHRwXNryfOm/8kfbT1MF5wdRDR7S4PmiLRjm1C+Xl/lM10cv\nL/OZrntXZ24o8+bLXWbc9+X+iD2obnS/qsM6u5b1/yIzNwJk5gaq5988sLzfapKPb133/xXAXcCe\npR2d1JkUjcuY7jmPBriPx/Z3ZUS8ETgR+AFVYLiuTTHPlwHr8LjMxvNlADLzp1Rhe5+I2Kks9nzR\nFgxz6pfzy/wFEXGfn7OI2A7YF7id6kGZmr+nl3nzL+7zyvyFbco/i2oU0Yszc1OHdV7UUmYhdSZB\nLfd/2d7FZfvP7GI7k6Axqu5VLcsHsY+vpHrO114RsUeHdWovIt5C9aDh71IFhhtmKOr5MkBdHJfZ\neL4MzkPL/K4y93zRlob9bASn8Z3woeG92o/7ADu2Wf4IqhGiEnhr0/Ltqf5y2s1DRffAh4Z3ejye\nw+zPM6vt/qezh7puP+x93Kfj8lRg6zbL9yv7KoFVw9jHjN9DkN9R2r2GNr+7Wsp6vtTzuHi+DOaY\nPBp4SJvlW3HvQ8O/3rTc88Vpy5+jYTfAaXwnqufBXF9O7s8A76P6q0xSdfs/aNhtHIWJanCTO6ie\n0/JPwAeAs6h6NhP4fOt/usBLqS61WE91f8PfU93k3vgPMNps5/Xl/RuBj1L99fbnZdlxM7TtQ+X9\nn5fyHy31Ezhy2Puuh8fgpcApZfpi+XxXNi07rk352u1/qpvQzyzvX1ba9bHSzs3AgcPe1/06LsAF\nVF+Cziz76niqx6Jkmd4+rH0MLKb6ApTAaqoRBj9F9fyvDcBTh72vuzgmh5XPsbnsr2PaTH/k+VLv\n4+L5MrDj8sbS7q8A/5fqe9LHqX6PJXAt8FjPF6dZf46G3QCn8Z6Ah1MNq38tcCfwU6qbrmf9q6DT\nffbhs4H/KL+sp8ov/l8B/031fKAtfnGXevsC5wK3UAW/7wNvAhbNsq0DgAuB28p/iqupnic0W/sO\nK+U2lHoXAvsPe7/1+Bgc0/Qlpt20blT2P9XzRd9U2nN7ad+5tPyVfRSmbo4L8Brgc8C68uViE9Xl\nWmcAzxz2PqYalfZYqt72Tdz7Rfqx3eyTYU8dHJMELmhTz/OlRsfF82Vgx+VxVCHpu1RBaTPVICGr\nyzFr+13J88WpeYqy8yVJkiRJI8QBUCRJkiRpBBnmJEmSJGkEGeYkSZIkaQQZ5iRJkiRpBBnmJEmS\nJGkEGeYkSZIkaQQZ5iRJkiRpBBnmJEmSJGkEGeYkSZIkaQQZ5iRJkiRpBBnmJEmSJGkEGeYkSRqC\niPhaRGREvLKH63x3Wee/zaPuo0rdzb1qjySpvwxzkqSxEhGnllDyoy7qvK7UuSMilvWzfZIk9Yph\nTpI0bk4p88dExIoO6xxa5p/NzKneN6mtnwKXA9MD2p4kaczcb9gNkCSpxy6gCkqPoAppa2YrHBF7\nA08pL0/ta8uaZOYfDmpbkqTxZM+cJGmsZGYCnywvD4mIuf5w2eiVuw74Ut8aJklSjxnmJEnjqNHD\ntjPwopkKRUQAjQFI/j0z7yrLF0XE8yLihIj4dkRcHxGbIuKXEfGfEfGcWdZ5z8AmEbFDRHwwIi6P\niNsj4sZ25dqs4wkR8c5S5mdl2zdFxPkR8eqImPP/7/IZjoqISyNiQ6n/2S4uPW23zl0i4v0R8f2I\nWF/W+/0y8MoOM9RZHBFviohLImIqIu6MiOsi4ntl/z5tvu2RpEnnZZaSpLGTmVdExMXAKqqet3Nm\nKPoc4DfKv5svsXw88D9NrzcBvwZ2BQ4CDoqIt2Tm38/SjAcD3wZ2B+4o9Tt1EbC0/PsuYD2wnn6Z\n2QAABtxJREFUY2nvc4ADI+JljfDZRgD/CbwE2AxsKPVfAvxuRPxBZp7VRXuIiGcBnwUaA8TcWdr2\nuDK9MiKen5lrm+rcn2o/PqMsSmAK2Ilq/zwB2AH4327aIkmq2DMnSRpXjXB2wCwjVDYusfxOZn6/\nafkm4Axgf6rQ8YDMfCDwEOCdVCHmfRHx5Fm2fwxVqPodYElmbg902gt1AfAaqqC5ODOXAQ8EDgNu\noAplb5il/suBFwNvBLYv9ZcDX6H6Q+6pEbFHh20hIvakCsTLgH8B9gIeACyhCr5fprpH8eyIWNRU\n9VVUQW4D8IdU+3FHYDFVyH0D0LzfJUldMMxJksbVGVQ9YouB32t9MyK2pQo90DLwSWZelpmHZObn\nM/OGch8emXl9Zr4LeA/V/6FHzLL9rYEXZeaXM/PuUv+KThqemS/NzI9n5s8bvW+ZuSEzPwEcUor9\n+SyrWAq8LTP/MTNvb9r2AcAVwLbAWzppS/FeYHvgw5l5RGauzcy7s/IDqnD5A6pgd0BTvUZ4PTkz\nP5WZm0pb7srMn2bmCZn5gS7aIUlqYpiTJI2lzJymuiwQ7u2Ba3YQsB3VZYj/0eXqG5dt7jtLmc9l\n5mVdrrcTFwC3AY+KiF1mKLMe+EjrwhLsPlxeHtzJxiLigdwbeo9vV6aEtLPLy+c3vXVrme/aybYk\nSd0xzEmSxlmjx23fcqlgs0bA+0Jm3tBaMSK2jYg3R8SFEXFDRPy6DFiSwOpS7KGzbPuS+TY6Kr9X\nBiz5eXmYeWPbd1OF0Nm2/81Gj1wbF5b5gyLiN2Yo02wl1aWZCawpg5dsMQFvKuUf3lT33DJ/eUR8\nJiIOiogdO9imJKkDDoAiSRpnXwaupeoZehVwLEBE7Ao8r5TZ4tlyEbEbVQ/Yo5oWbwBuoQpTi6gG\n8Vgyy7Z/NZ8Gl0FDzqK6dLFhE3Aj1b16UI3SudUs279mlk00v7cz8LM5mtToVQuq+wfnsm3jH5l5\nXkQcC7wdOLBMRMRlwOeBkzLzyg7WKUlqw545SdLYKvebnVZevqrprVdSBbKbaT/S5UeogtyVVJdj\n7pCZD8zMXTLzIdw7OmPMsvmZRpqcyxFUQW4D8Hrg4Zm5TWbunJkPKdtv9CTOtv2ZdFun8V3hV5kZ\nHUy/3Vw5M4+hGjDlrVTh+jbgMcDRwGUR4cPTJWmeDHOSpHHX6Hl7ZES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"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 260,
"width": 441
}
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"MSE: 79648697.4319\n"
]
}
],
"source": [
"#Zip Code Scatterplot\n",
"plt.scatter(predictions_zip, y, s=30, c='r', marker='+', zorder=10)\n",
"plt.xlabel(\"Variables\")\n",
"plt.ylabel(\"Sale Dollars\")\n",
"plt.plot(predictions_zip, np.poly1d(np.polyfit(predictions_zip, y, 1))(predictions_zip))\n",
"plt.show()\n",
"print(\"MSE:\", model_zip.mse_model) ## mean squared error"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the graph above bottles retail and bottles sold are still in a linear configuration."
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Split my data into training and test.\n",
"x_train_county, x_test_county, y_train_county, y_test_county = train_test_split(X_county, y, test_size = 0.3, random_state = 1234)\n",
"x_train_county = sm.add_constant(x_train_county)\n",
"x_test_county = sm.add_constant(x_test_county)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Split my data into training and test.\n",
"x_train_city, x_test_city, y_train_city, y_test_city = train_test_split(X_city, y, test_size = 0.3, random_state = 1234)\n",
"x_train_city = sm.add_constant(x_train_city)\n",
"x_test_city = sm.add_constant(x_test_city)"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Split my data into training and test.\n",
"x_train_zip, x_test_zip, y_train_zip, y_test_zip = train_test_split(X_zip, y, test_size = 0.3, random_state = 1234)\n",
"x_train_zip = sm.add_constant(x_train_zip)\n",
"x_test_zip = sm.add_constant(x_test_zip)"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1884821, 101)\n",
"(807781, 101)\n",
"(2692602, 100)\n",
"(1884821,)\n",
"(807781,)\n",
"(2692602,)\n"
]
}
],
"source": [
"print(x_train.shape)\n",
"print(x_test.shape)\n",
"print(X.shape)\n",
"print(y_train.shape)\n",
"print(y_test.shape)\n",
"print(y.shape)\n"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#County \n",
"lm = linear_model.LinearRegression()\n",
"model_lin_county = lm.fit(x_train_county, y_train_county)\n",
"predictions_county = lm.predict(x_test_county)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#City \n",
"lm = linear_model.LinearRegression()\n",
"model_lin_city = lm.fit(x_train_city, y_train_city)\n",
"predictions_city = lm.predict(x_test_city)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Zip Code\n",
"lm = linear_model.LinearRegression()\n",
"model_lin_zip = lm.fit(x_train_zip, y_train_zip)\n",
"predictions_zip = lm.predict(x_test_zip)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Creating a kfolds cross validation test"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cross validated scores: [ 0.75449393 0.74423201 0.72962293 0.71827552 0.72573546]\n",
"Average: 0.734471967462\n"
]
}
],
"source": [
"#County\n",
"scores_county = cross_val_score(model_lin_county, x_train_county, y_train_county, cv=5)\n",
"print(\"Cross validated scores:\", scores_county)\n",
"print(\"Average: \", scores_county.mean())"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cross validated scores: [ 7.54656701e-01 7.44332737e-01 7.30246737e-01 7.18273801e-01\n",
" -8.12869612e+05]\n",
"Average: -162573.332904\n"
]
}
],
"source": [
"#City\n",
"scores_city = cross_val_score(model_lin_city, x_train_city, y_train_city, cv=5)\n",
"print(\"Cross validated scores:\", scores_city)\n",
"print(\"Average: \", scores_city.mean())"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cross validated scores: [ 0.75582441 0.74495646 0.73070775 0.71923674 0.72683249]\n",
"Average: 0.735511569804\n"
]
}
],
"source": [
"#Zip Codes\n",
"scores_zip = cross_val_score(model_lin_zip, x_train_zip, y_train_zip, cv=5)\n",
"print(\"Cross validated scores:\", scores_zip)\n",
"print(\"Average: \", scores_zip.mean())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plot your results\n",
"\n",
"Again make sure that you record any valuable information. For example, in the tax scenario, did you find the sales from the first three months of the year to be a good predictor of the total sales for the year? Plot the predictions versus the true values and discuss the successes and limitations of your models"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Testing Model:"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cross Predicted Accuracy: 0.715240830688\n"
]
},
{
"data": {
"image/png": 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jTgHOBx6n3PxckiRJ0uTWknvmIuL1wIcp7u96N/BVYCvwcw2KXw/8n8BvA18a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\nmhSATwwAAAAASUVORK5CYII=\n",
"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 264,
"width": 441
}
},
"output_type": "display_data"
}
],
"source": [
"#County\n",
"predictions_county = cross_val_predict(model_lin_county, X_county, y, cv=5)\n",
"plt.scatter(y, predictions_county)\n",
"plt.xlabel('Actual')\n",
"plt.ylabel('Predicted')\n",
"accuracy_county = metrics.r2_score(y, predictions_county)\n",
"print(\"Cross Predicted Accuracy:\", accuracy_county)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cross Predicted Accuracy: -2.77554534782e+13\n"
]
},
{
"data": {
"image/png": 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JhRjIJEmSJKkQA5kkSZIkFWIgkyRJkqRCDGSSJEmSVIiBTJIkSZIKMZBJkiRJUiEGMkmS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\nYJvM3Ar47FQrioilwKeAdcC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"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 271,
"width": 434
}
},
"output_type": "display_data"
}
],
"source": [
"#City\n",
"predictions_city = cross_val_predict(model_lin_city, X_city, y, cv=5)\n",
"plt.scatter(y, predictions_city)\n",
"plt.xlabel('Actual')\n",
"plt.ylabel('Predicted')\n",
"accuracy_city = metrics.r2_score(y, predictions_city)\n",
"print(\"Cross Predicted Accuracy:\", accuracy_city)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cross Predicted Accuracy: 0.716408801447\n"
]
},
{
"data": {
"image/png": 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5dFVNTOX5B6tr7ZXx7yyPd2VNTOW8l5T9qcScCJwLPEJZ/FySJElSZ2vKAigR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\nQmCC\n",
"text/plain": [
""
]
},
"metadata": {
"image/png": {
"height": 263,
"width": 441
}
},
"output_type": "display_data"
}
],
"source": [
"#Zip Code\n",
"predictions_zip = cross_val_predict(model_lin_zip, X_zip, y, cv=5)\n",
"plt.scatter(y, predictions_zip)\n",
"plt.xlabel('Actual')\n",
"plt.ylabel('Predicted')\n",
"accuracy_zip = metrics.r2_score(y, predictions_zip)\n",
"print(\"Cross Predicted Accuracy:\", accuracy_zip)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Present the Results\n",
"\n",
"Present your conclusions and results. If you have more than one interesting model feel free to include more than one along with a discussion. Use your work in this notebook to prepare your write-up."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"This report provides an analysis and evaluation of the 3 best location within Iowa to build a liquor store. The methods of analysis were putting together linear regression models and running them through a K folds cross validation test. The specific things we were interested in measuring was sale price, number of bottles sold, and bottle retail price. We then separated all variables by County, City, and Zip Code. After running the initial tests we decided to drop city and continue the test via Zip Code and County. \n",
"\n",
"The top 5 counties with the best sales/ bottle ratio for their size were Dallas County, Carroll County, Sioux County, Iowa County, Howard County. The top five zip codes were 50266, 52338, 50320, 52154, 50021. Since zip code 52388 has a winery that is responsible for a majority of the sale we will be dropping it from this list which leaves us with the top three zip codes 50266, 50320, 52154. My first suggestion for a new liquor store would be in 50266. \n",
"\n",
"Additional information that would be needed to perform a more detailed analysis and provide a more accurate location within the zip code would be demographics and population data. With the additional information we should be able to narrow down the location to within a few blocks.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 1
}