{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Homework 7 Key\n", "====\n", "#### CHE 116: Numerical Methods and Statistics\n", "\n", "3/8/2018\n", "\n", "----" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import random\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import scipy\n", "import scipy.stats\n", "import seaborn as sns\n", "plt.style.use('seaborn-whitegrid')\n", "\n", "import pydataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. Conceptual Questions (8 Points)\n", "===\n", "*Answer these in Markdown*\n", "\n", "1. [1 point] In problem 4 from HW 3 we discussed probabilities of having HIV and results of a test being positive. What was the sample space for this problem? \n", "2. [4 points] One of the notations in the answer key is a random variable $H$ which indicated if a person has HIV. Make a table showing this functions inputs and outputs for the sample space. [Making Markdown Tables](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet#tables)\n", "3. [1 point] A probability density function is used for what types of probability distributions?\n", "4. [2 points] What is the probability of $t > 4$ in an exponential distribution with $\\lambda = 1$? Leave your answer in terms of an exponential." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1.1\n", "First element is HIV and second is test\n", "$$\n", "\\{ (0,0), (1,0), (0,1), (1,1)\\}\n", "$$\n", "\n", "### 1.2\n", "|$x$|$H$|\n", "|---|---:|\n", "|(0,0)| 0|\n", "|(0,1)| 0|\n", "|(1,0)| 1|\n", "|(1,1)| 1|\n", "\n", "### 1.3\n", "Continuous\n", "\n", "### 1.4\n", "$$\n", "\\int_4^{\\infty} e^{-t} \\, dt = \\left. -e^{-t}\\right]_4^{\\infty} = 0 - - e^{-4} = e^{-4}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2. The Nile (10 Points)\n", "===\n", "\n", "*Answer in Python*\n", "\n", "1. [4 points] Load the Nile dataset and convert to a numpy array. It contains measurements of the annual flow of the river Nile at Aswan. Make a scatter plot of the year vs flow rate. 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 pydataset`\n", "\n", "2. [2 points] Report the correlation coefficient between year and flow rate.\n", "\n", "2. [4 points] Create a histogram of the flow rates and show the median with a vertical line. Labels your axes and make a legend indicating what the vertical line is." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "image/png": 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K0c3c2eHAhxuuxvFHbhBOpwZaDTbGyc6KKDVyHsv3V7VBRQF9g6VprpRSvjUx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\ns1RgAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#2.1\n", "nile = pydataset.data('Nile').as_matrix()\n", "plt.plot(nile[:,0], nile[:,1], '-o')\n", "plt.xlabel('Year')\n", "plt.ylabel('Nile Flow Rate')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-0.465\n" ] } ], "source": [ "#2.2\n", "print('{:.3}'.format(np.corrcoef(nile[:,0], nile[:,1])[0,1]))" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "image/png": 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HimuAakYOjiY62LcH0nWstKyU6KjoHn8e76a2rixnPVdpWSkH65z7wXh01LUd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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#2.3 ok to distplot or plt.hist\n", "sns.distplot(nile[:,1])\n", "plt.axvline(np.mean(nile[:,1]), color='C2', label='Mean')\n", "plt.legend()\n", "plt.xlabel('Flow Rate')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "2. Insect Spray (10 Points)\n", "===\n", "\n", "*Answer in Python*\n", "\n", "1. [2 points] Load the 'InsectSpray' dataset, convert to a numpy array and print the number of rows and columns. Recall that numpy arrays can only hold one type of data (e.g., string, float, int). What is the data type of the loaded dataset?\n", "\n", "2. [2 points] Using `np.unique`, print out the list of insect spray used. This data is a count insects on a crop field with various insect sprays.\n", "\n", "3. [4 points] Create a violin plot of the data. Label your axes.\n", "\n", "4. [2 points] Which insect spray worked best? What is the mean number of insects for the best insect spray?" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(72, 2) string or object is acceptable\n" ] } ], "source": [ "#1.1\n", "insect = pydataset.data('InsectSprays').as_matrix()\n", "print(insect.shape, 'string or object is acceptable')\n" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['A' 'B' 'C' 'D' 'E' 'F']\n" ] } ], "source": [ "#1.2\n", "print(np.unique(insect[:,1]))" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "image/png": 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mSVrBH47peBqzWYlAkV++fDmvvfYazz//fGAy1NKlS8NhW7fR42WihRbDwmxt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\nQg2CN1QVc3w8i595hri4ridj7ty5k9r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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#1.3\n", "labels = np.unique(insect[:,1])\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(insect[insect[:,1] == l, 0].astype(float))\n", "sns.violinplot(data=ldata)\n", "plt.xticks(range(len(labels)), labels)\n", "plt.xlabel('Insecticide Type')\n", "plt.ylabel('Insect Count')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "C is best and its mean is 2.1\n" ] } ], "source": [ "#1.4\n", "print('C is best and its mean is {:.2}'.format(np.mean(ldata[2])))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "3. NY Air Quality (6 Points)\n", "===\n", "\n", "Load the 'airquality' dataset and convert into to a numpy array. Make a scatter plot of wind (column 2, mph) and ozone concentration (column 0, ppb). Using the `plt.text` command, display the correlation coefficient in the plot. This data as `nan`, which means \"not a number\". You can select non-nans by using `x[~numpy.isnan(x)]`. You'll need to remove these to calculate correlation coefficient. " ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "data": { "image/png": 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Va0GL0UXO2lxlbgijrYc2ymQq4erMJn9vb29zL+HlZXGEqDDD3smqooaqzytE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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "nyair = pydataset.data('airquality').as_matrix()\n", "plt.plot(nyair[:,2], nyair[:,0], 'o')\n", "plt.xlabel('Wind [mph]')\n", "plt.ylabel('Ozone [ppb]')\n", "nans = np.isnan(nyair[:,0])\n", "r = np.corrcoef(nyair[~nans,2], nyair[~nans,0])[0,1]\n", "plt.text(10, 130, 'Correlation Coefficient = {:.2}'.format(r))\n", "plt.show()" ] } ], "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": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }