{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 1. Conceptual Questions (7 Points)\n", "\n", "*Answer these in Markdown*\n", "\n", "1. [1 point] What is the difference between a probability mass function and a probability density function?\n", "2. [1 point] What is the difference between a cumulative distribution function and a prediction interval?\n", "3. [1 point] Is the exponential distribution a continuous or discrete distribution? Is it valid to compute the probability of a single element in the sample space?\n", "4. [2 points] What is the probability of $t > 8$ in an exponential distribution with $\\lambda = \\frac{1}{4}$? Leave your answer as an unevaluated exponential. \n", "5. [1 point] This slice must have how many elements: `a[5:2]`? How can you tell without counting?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.1\n", "PMF is for discrete sample space, PDF is for continuous\n", "\n", "### 1.2\n", "CDF is probability of an interval, prediction interval is interval given a probability\n", "\n", "### 1.3\n", "Continuous, no\n", "\n", "### 1.4\n", "$$\n", "\\int_8^{\\infty} e^{-t / 4} \\, dt = \\left. -e^{-t / 4}\\right]_4^{\\infty} = 0 - - e^{-2} = e^{-2}\n", "$$\n", "\n", "### 1.5\n", "$3$, because $5 - 2$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2. Car Stopping Distance (10 Points)\n", "\n", "1. [4 points] Load the cars dataset and create a scatter plot. It contains measurements a cars' stopping distance in feet as a function of speed in mph. If you get an error when loading `pydataset` that says `No Module named 'pydataset'`, then execute this code in a new cell once: `!pip install --user pydataset`\n", "\n", "2. [4 points] Compute the sample correlation coefficient between stopping distance and speed in python and report your answer by writing a complete sentence in Markdown.\n", "\n", "2. [2 points] Why might there be multiple stopping distances for a single speed?" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#2.1\n", "import pydataset\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "cars = pydataset.data('cars').values\n", "plt.plot(cars[:,0], cars[:,1], 'o')\n", "plt.xlabel('Speed [mph]')\n", "plt.ylabel('Stopping Distance [ft]')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1. , 0.8068949],\n", " [ 0.8068949, 1. ]])" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#1.2\n", "np.corrcoef(cars[:,0].astype(float), cars[:,1].astype(float))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.2\n", "The correlation coefficient is 0.81." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2.3\n", "\n", "Multiple cars were tested or one car was tested multiple times or both." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3. Housing Prices (24 Points)\n", "\n", "1. [8 points] Load the 'House' dataset and use `pydataset.data('Housing', show_doc=True)` to see information about the dataset. Use the snippet below to format your ticks with dollar signs and commas for thousands. Note that this data is from the 1970s. Assess the correlation between lotsize and price. Use plots and sample correlation coefficient as evidence to support a written answer.\n", "\n", "```python\n", "import matplotllib.ticker\n", "fmt = '${x:,.0f}'\n", "tick = matplotllib.ticker.StrMethodFormatter(fmt)\n", "plt.gca().yaxis.set_major_formatter(tick) \n", "```\n", "\n", "2. [8 points] Use a violin plot to show if being in a preferred neighborhood affects price. You may use any other calculations (e.g., sample standard deviation) to support your conclusions. Write out your conclusion.\n", "\n", "3. [8 points] Use a boxplot to determine if bedroom number affects price. What is your conclusion?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.1" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 1. , 0.53579567],\n", " [ 0.53579567, 1. ]])" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.ticker\n", "fmt = '${x:,.0f}'\n", "tick = matplotlib.ticker.StrMethodFormatter(fmt)\n", "\n", "fmt = '{x:,.0f}'\n", "xtick = matplotlib.ticker.StrMethodFormatter(fmt)\n", "\n", "house = pydataset.data('Housing').values\n", "\n", "plt.gca().yaxis.set_major_formatter(tick) \n", "plt.gca().xaxis.set_major_formatter(xtick) \n", "plt.plot(house[:, 0], house[:,1], 'o')\n", "plt.ylabel('House Price')\n", "plt.xlabel('Lot Size [sq ft]')\n", "np.corrcoef(house[:,0].astype(np.float), house[:,1].astype(np.float))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There is a weak correlation. The correlation coefficient is low at 0.53, but there is so much data that we can see a weak correlation especially at small lot sizes. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.2" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [], "source": [ "import seaborn as sns\n", "p = house[:,-1] == 'yes'" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "79750.0\n", "58500.0\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.violinplot(data=[house[p,0], house[~p,0]])\n", "plt.gca().yaxis.set_major_formatter(tick)\n", "print(np.median(house[p,0]))\n", "print(np.median(house[~p,0]))\n", "plt.xticks(range(2), ['Preferred Neighborhood', 'Normal Neighborhood'])\n", "plt.ylabel('House Price')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The preferred neighborhood has a \\$20,000 higher median price and has a longer tail at high prices, indicating many expensive homes. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3.3" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "labels = np.unique(house[:,2])\n", "ldata = []\n", "#slice out each set of rows that matches label\n", "#and add to list\n", "for l in labels:\n", " ldata.append(house[house[:,2] == l, 0].astype(float))" ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.boxplot(data=ldata)\n", "plt.xticks(range(len(labels)), labels)\n", "plt.xlabel('Number of Bedrooms')\n", "plt.gca().yaxis.set_major_formatter(tick)\n", "plt.ylabel('House Price')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The number of bedrooms is important up until 4, after which it seems to have less effect. Having 1 bedroom has a very narrow distribution. There appears to be a correlation overall with bedroom number. " ] } ], "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": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }