{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5b7cfe86",
   "metadata": {},
   "source": [
    "## Linear Regression Exercise"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55e146d6",
   "metadata": {},
   "source": [
    "We will use a couple of the variables we measured, soil moisture content and aggregate stability to see if there is any relationship between them. The data is located here: https://github.com/gabbymyers/516X-Project/blob/master/_data/Class%20Exercise%20Data.xlsx.    \n",
    "\n",
    "Soil moisture content refers to the moisture in the soil. We determine this by collecting a sample from each of our 36 plots at the Northeast Iowa Research Farm. The sample is taken back to the lab and the moisture content is determined by weighing the soil before and after drying.    \n",
    "\n",
    "Aggregate stability is a measure of how the soil aggregates (groups of soil particles) fall apart when wet. We use the SLAKES app to get the aggregate stability numbers for our samples. The SLAKES app take continuous images of soil aggregates as they are submerged in water and returns aggregate stability values on a range of 0-14. The lower the number, the more stable the aggregates are. Stable aggregates indicate the soil likely has better water infiltration, reducing the risk of runoff. Cover crops are known to increase aggreagate stability, so I would expect our cover crop treatments to have lower aggregate stability values from the SLAKES app."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab8678f1",
   "metadata": {},
   "source": [
    "We have different 12 different treatments in triplicate on our 36 plots. I have these listed below.    \n",
    "\n",
    "* 1C: Corn (on corn/soy rotation) with spring UAN    \n",
    "* 1S: Soy (on corn/soy rotation) with spring UAN     \n",
    "* 2C: Corn (on corn/soy rotation) with spring manure     \n",
    "* 2S: Soy (on corn/soy rotation) with spring manure     \n",
    "* 3.1: Continuous Corn     \n",
    "* 3.2: Continuous Corn + Interseeded Cover crop (30 inch row)    \n",
    "* 4.1: Continuous Corn + Perennial Groundcover    \n",
    "* 4.2: Continuous Corn + Interseeded Cover crop (60 inch row)     \n",
    "* 5C: Corn (on corn/soy rotation) + cereal rye     \n",
    "* 5S: Soy (on corn/soy rotation) + cereal rye     \n",
    "* 6C: Corn (on corn/soy rotation) + fall manure     \n",
    "* 6S: Soy (on corn/soy rotation) + fall manure    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dec9b3a",
   "metadata": {},
   "source": [
    "### Research Question:\n",
    "\n",
    "Is there any relationship between aggregate stability and moisture content?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "40929ec1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "# allow plots to appear directly in the notebook\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49ec3ad1",
   "metadata": {},
   "source": [
    "#### Read the soil moisture data into its own data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3aca81e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Plot Number</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Block</th>\n",
       "      <th>Sample Date</th>\n",
       "      <th>Moisture Content</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2S</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.050720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>6S</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.053265</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1C</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.075589</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.059354</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.082545</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Plot Number Treatment  Block  Sample Date  Moisture Content  Unnamed: 5  \\\n",
       "0            1        2S      2            1          0.050720         NaN   \n",
       "1            2        6S      2            1          0.053265         NaN   \n",
       "2            3        1C      2            1          0.075589         NaN   \n",
       "3            4       3.1      2            1          0.059354         NaN   \n",
       "4            5       4.1      2            1          0.082545         NaN   \n",
       "\n",
       "   Unnamed: 6  Unnamed: 7  \n",
       "0         NaN         NaN  \n",
       "1         NaN         NaN  \n",
       "2         NaN         NaN  \n",
       "3         NaN         NaN  \n",
       "4         NaN         NaN  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soil_moisture = pd.read_excel(___, sheet_name = ______)\n",
    "soil_moisture.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e169d5f",
   "metadata": {},
   "source": [
    "Get rid of unneeded columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "20394f09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Plot Number</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Block</th>\n",
       "      <th>Sample Date</th>\n",
       "      <th>Moisture Content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2S</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.050720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>6S</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.053265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1C</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.075589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.059354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.082545</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Plot Number Treatment  Block  Sample Date  Moisture Content\n",
       "0            1        2S      2            1          0.050720\n",
       "1            2        6S      2            1          0.053265\n",
       "2            3        1C      2            1          0.075589\n",
       "3            4       3.1      2            1          0.059354\n",
       "4            5       4.1      2            1          0.082545"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soil_moisture = soil_moisture.iloc[____:____ ]\n",
    "soil_moisture.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc793af3",
   "metadata": {},
   "source": [
    "See how many weeks of data you have for soil moisture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3428bdf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(_____)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34958712",
   "metadata": {},
   "source": [
    "We have 12 weeks of data for the soil moisture content. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d2b93ab",
   "metadata": {},
   "source": [
    "#### Read the aggregate stability data into its own data frame. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e2c922f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Plot</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Block</th>\n",
       "      <th>Date</th>\n",
       "      <th>Aggregate Stability</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>Unnamed: 6</th>\n",
       "      <th>Unnamed: 7</th>\n",
       "      <th>Unnamed: 8</th>\n",
       "      <th>Unnamed: 9</th>\n",
       "      <th>Unnamed: 10</th>\n",
       "      <th>Unnamed: 11</th>\n",
       "      <th>Unnamed: 12</th>\n",
       "      <th>Unnamed: 13</th>\n",
       "      <th>Unnamed: 14</th>\n",
       "      <th>Unnamed: 15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2S</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>6S</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1C</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Plot Treatment  Block  Date  Aggregate Stability  Unnamed: 5  Unnamed: 6  \\\n",
       "0     1        2S      2     1                  2.5         NaN         NaN   \n",
       "1     2        6S      2     1                  6.2         NaN         NaN   \n",
       "2     3        1C      2     1                  2.5         NaN         NaN   \n",
       "3     4       3.1      2     1                  1.7         NaN         NaN   \n",
       "4     5       4.1      2     1                  0.8         NaN         NaN   \n",
       "\n",
       "   Unnamed: 7  Unnamed: 8  Unnamed: 9  Unnamed: 10  Unnamed: 11  Unnamed: 12  \\\n",
       "0         NaN         NaN         NaN          NaN          NaN          NaN   \n",
       "1         NaN         NaN         NaN          NaN          NaN          NaN   \n",
       "2         NaN         NaN         NaN          NaN          NaN          NaN   \n",
       "3         NaN         NaN         NaN          NaN          NaN          NaN   \n",
       "4         NaN         NaN         NaN          NaN          NaN          NaN   \n",
       "\n",
       "   Unnamed: 13  Unnamed: 14  Unnamed: 15  \n",
       "0          NaN          NaN          NaN  \n",
       "1          NaN          NaN          NaN  \n",
       "2          NaN          NaN          NaN  \n",
       "3          NaN          NaN          NaN  \n",
       "4          NaN          NaN          NaN  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aggregate_stability = pd.read_excel(____, sheet_name = _____ )\n",
    "aggregate_stability.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5f9e9c5",
   "metadata": {},
   "source": [
    "We need to filter out the extra columns that are contained in the aggregate stability data frame. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "78c30fb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Plot</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Block</th>\n",
       "      <th>Date</th>\n",
       "      <th>Aggregate Stability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2S</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>6S</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1C</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Plot Treatment  Block  Date  Aggregate Stability\n",
       "0     1        2S      2     1                  2.5\n",
       "1     2        6S      2     1                  6.2\n",
       "2     3        1C      2     1                  2.5\n",
       "3     4       3.1      2     1                  1.7\n",
       "4     5       4.1      2     1                  0.8"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aggregate_stability = aggregate_stability.iloc[___:___ ]\n",
    "aggregate_stability.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8ba85f6",
   "metadata": {},
   "source": [
    "Make sure that the dates match the soil moisture so we have the same amount of data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2d1a42b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(________)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aa84032",
   "metadata": {},
   "source": [
    "We only have 10 weeks of data for aggregate stability, so we need to filter out the extra two weeks of soil moisture data. 10 weeks * 36 plots = 360 rows of data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "44ab1b52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soil_moisture = soil_moisture.iloc[___:____]\n",
    "max(soil_moisture['Sample Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cb896722",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Plot Number</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Block</th>\n",
       "      <th>Sample Date</th>\n",
       "      <th>Moisture Content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2S</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.050720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>6S</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.053265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1C</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.075589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.059354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.082545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>355</th>\n",
       "      <td>32</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0.159200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>356</th>\n",
       "      <td>33</td>\n",
       "      <td>3.1</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>0.153900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>34</td>\n",
       "      <td>5C</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>0.148500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>35</td>\n",
       "      <td>4.2</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>0.161500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>36</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>0.168700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>360 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Plot Number Treatment  Block  Sample Date  Moisture Content\n",
       "0              1        2S      2            1          0.050720\n",
       "1              2        6S      2            1          0.053265\n",
       "2              3        1C      2            1          0.075589\n",
       "3              4       3.1      2            1          0.059354\n",
       "4              5       4.1      2            1          0.082545\n",
       "..           ...       ...    ...          ...               ...\n",
       "355           32       3.2      1           10          0.159200\n",
       "356           33       3.1      3           10          0.153900\n",
       "357           34        5C      3           10          0.148500\n",
       "358           35       4.2      3           10          0.161500\n",
       "359           36       3.2      3           10          0.168700\n",
       "\n",
       "[360 rows x 5 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soil_moisture"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "683576a5",
   "metadata": {},
   "source": [
    "I wanted to make sure that the last row of data is correct. It is because it is plot 36 on date 10. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe6c9906",
   "metadata": {},
   "source": [
    "#### Merging the data frames\n",
    "First we need to rename the columns so the are the same across the different data frames. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c1548f92",
   "metadata": {},
   "outputs": [],
   "source": [
    "soil_moisture = soil_moisture.rename(columns={'Plot Number': 'Plot'})\n",
    "soil_moisture = soil_moisture.rename(columns={'Sample Date': 'Date'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6182b952",
   "metadata": {},
   "source": [
    "Now we can merge the two data frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cdfb893f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Plot</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Block</th>\n",
       "      <th>Date</th>\n",
       "      <th>Moisture Content</th>\n",
       "      <th>Aggregate Stability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2S</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.050720</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>6S</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.053265</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1C</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.075589</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.059354</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>4.1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.082545</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Plot Treatment  Block  Date  Moisture Content  Aggregate Stability\n",
       "0     1        2S      2     1          0.050720                  2.5\n",
       "1     2        6S      2     1          0.053265                  6.2\n",
       "2     3        1C      2     1          0.075589                  2.5\n",
       "3     4       3.1      2     1          0.059354                  1.7\n",
       "4     5       4.1      2     1          0.082545                  0.8"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_data = pd.merge(left=___, right=______, \n",
    "                       left_on=[__,___,__,__], right_on=[__,___,__,__])\n",
    "merged_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d632ced",
   "metadata": {},
   "source": [
    "Count the number of na values in the data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "38490dd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Plot                   0\n",
       "Treatment              0\n",
       "Block                  0\n",
       "Date                   0\n",
       "Moisture Content       0\n",
       "Aggregate Stability    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "dfae02e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(360, 6)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0faa77c1",
   "metadata": {},
   "source": [
    "There are four na values in the aggregate stability column. Drop those rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "15fd3c50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(356, 6)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_data = merged_data.____()\n",
    "merged_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ff610e0",
   "metadata": {},
   "source": [
    "#### Explore the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "548f5d7d",
   "metadata": {},
   "source": [
    "###### Make box plots of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "c9c2e025",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Moisture Content by Treatment')"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(x = ________, y = _________, data = merged_data)\n",
    "plt.title('Moisture Content by Treatment')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "646a1816",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Aggregate Stability by Treatment')"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(x = __________, y = ________, data = merged_data)\n",
    "plt.title('Aggregate Stability by Treatment')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a95b4ab",
   "metadata": {},
   "source": [
    "###### How many unstable aggregate stability measurements do we have and which treatments have the most?\n",
    "\n",
    "Unstable is defined as values greater than or equal to 7. \n",
    "\n",
    "Start by making a data frame of the unstable measurements:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "608ba708",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Plot</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Block</th>\n",
       "      <th>Date</th>\n",
       "      <th>Moisture Content</th>\n",
       "      <th>Aggregate Stability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>3.2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.106014</td>\n",
       "      <td>10.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>6S</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.164036</td>\n",
       "      <td>11.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>1C</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.046167</td>\n",
       "      <td>7.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.082044</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>6</td>\n",
       "      <td>3.2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.297426</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Plot Treatment  Block  Date  Moisture Content  Aggregate Stability\n",
       "5      6       3.2      2     1          0.106014                 10.3\n",
       "19    20        6S      1     1          0.164036                 11.1\n",
       "27    28        1C      1     1          0.046167                  7.8\n",
       "31    32       3.2      1     1          0.082044                  9.9\n",
       "41     6       3.2      2     2          0.297426                  8.1"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unstable_df = merged_data[____]>=___]\n",
    "unstable_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "3ba5d223",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Plot</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Block</th>\n",
       "      <th>Date</th>\n",
       "      <th>Moisture Content</th>\n",
       "      <th>Aggregate Stability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>3.2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.106014</td>\n",
       "      <td>10.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>6S</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.164036</td>\n",
       "      <td>11.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>1C</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.046167</td>\n",
       "      <td>7.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.082044</td>\n",
       "      <td>9.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>3.2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.297426</td>\n",
       "      <td>8.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Plot Treatment  Block  Date  Moisture Content  Aggregate Stability\n",
       "0     6       3.2      2     1          0.106014                 10.3\n",
       "1    20        6S      1     1          0.164036                 11.1\n",
       "2    28        1C      1     1          0.046167                  7.8\n",
       "3    32       3.2      1     1          0.082044                  9.9\n",
       "4     6       3.2      2     2          0.297426                  8.1"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unstable_df = unstable_df.reset_index(drop=True)\n",
    "unstable_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "b96db93d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(____)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef9eed0a",
   "metadata": {},
   "source": [
    "There are 17 unstable measurements. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72d1e263",
   "metadata": {},
   "source": [
    "See which treatments the unstable measurements belong to, using \"value_counts\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "340e99e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.2    4\n",
       "3.1    3\n",
       "6C     2\n",
       "1C     2\n",
       "2C     1\n",
       "6S     1\n",
       "1S     1\n",
       "5C     1\n",
       "5S     1\n",
       "4.2    1\n",
       "Name: Treatment, dtype: int64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a682b4cd",
   "metadata": {},
   "source": [
    "The treatment with the most unstable measurements is 3.2, followed by 3.1."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c0ba903",
   "metadata": {},
   "source": [
    "###### How are the unstable measurements distributed among the blocks?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "dd19158b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    7\n",
       "2    6\n",
       "3    4\n",
       "Name: Block, dtype: int64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e449958c",
   "metadata": {},
   "source": [
    "The block with the most unstable measurements is 1. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e968cda",
   "metadata": {},
   "source": [
    "### Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "858d0b5c",
   "metadata": {},
   "source": [
    "Plot moisture content vs. aggregate stability to see if you can see any relationship."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "94b723dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Aggregate Stability')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter()\n",
    "plt.title('MC vs. AS')\n",
    "plt.xlabel('Soil Moisture Content')\n",
    "plt.ylabel('Aggregate Stability')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "0143fdf6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(356,)\n",
      "(356,)\n"
     ]
    }
   ],
   "source": [
    "X = merged_data['Moisture Content']\n",
    "Y = merged_data['Aggregate Stability']\n",
    "print(X.shape)\n",
    "print(Y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaca029c",
   "metadata": {},
   "source": [
    "Use scipy.stats.linregress (https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.linregress.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "eaab2ea3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared: 0.001122\n",
      "Slope: 0.946637\n",
      "Intercept: 2.570731\n"
     ]
    }
   ],
   "source": [
    "res = sp.stats.___________\n",
    "print(f\"R-squared: {res.rvalue**2:.6f}\")\n",
    "print(f\"Slope: {res.slope:.6f}\")\n",
    "print(f\"Intercept: {res.intercept:.6f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "2e6e714b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X, Y, 'o', label='original data')\n",
    "plt.plot(X, res.intercept + res.slope*X, 'r', label='fitted line')\n",
    "plt.title('MC vs. AS')\n",
    "plt.xlabel('Soil Moisture Content')\n",
    "plt.ylabel('Aggregate Stability')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.savefig('MC_AS_Regress.jpg', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76e225fd",
   "metadata": {},
   "source": [
    "#### Using the method we learned in class: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "95bfbe3f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(356, 1)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-72-44f8d95ed655>:1: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  X = X[:, np.newaxis]\n"
     ]
    }
   ],
   "source": [
    "X = X[:, np.newaxis]\n",
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "124724eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LinearRegression(fit_intercept=True)\n",
    "model\n",
    "model.fit(____,________)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "f8c5397f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.94663685])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "2cc426c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.5707314408029305"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.intercept_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c73ccd84",
   "metadata": {},
   "source": [
    "### Equation: Y = 0.9466X + 2.57073"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83f668da",
   "metadata": {},
   "source": [
    "### R^2 value: 0.001122"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "982e2d6d",
   "metadata": {},
   "source": [
    "The regression model is not powerful at all, with a very low R^2 value. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d2131db",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}