{
    "cells": [
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# The Tampa Bay Times and school performance\n",
                "\n",
                "**Story:** [The story](http://www.tampabay.com/projects/2015/investigations/pinellas-failure-factories/), and [a critique](https://rogueedu.blogspot.com/2015/08/fcat-reading-scores-only-two-of-five.html)\n",
                "\n",
                "**Author:** Various parts are various people! Nathaniel Lash did the good investigation, but we're reproducing a less-than-stellar approach here.\n",
                "\n",
                "**Topics:** Linear Regression, Residuals\n",
                "\n",
                "**Datasets**\n",
                "\n",
                "* **0066897-gr04_rsch_2014.xls:** 4th grader pass rates for standardized tests, from Florida Dept of Education\n",
                "* **FRL-1314-School-Web-Survey-3-Final.xls:** Free and reduced price lunch data, from Florida Dept of Education\n",
                "* **MembershipSchoolRaceGender1415.xls:** School population by gender, from Florida Dept of Education\n",
                "\n",
                "## What's the story?\n",
                "\n",
                "We're trying to see what kind of effect things like race and poverty might have on school test score data. In this section, **our analysis is a naive approach that yields inaccurate results.** While the _math_ is correct the data behind it contains a fatal flaw."
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "<p class=\"reading-options\">\n  <a class=\"btn\" href=\"/tampa-bay-times-schools/linear-regression-on-florida-schools-no-cleaning\">\n    <i class=\"fa fa-sm fa-book\"></i>\n    Read online\n  </a>\n  <a class=\"btn\" href=\"/tampa-bay-times-schools/notebooks/Linear regression on Florida schools (No cleaning).ipynb\">\n    <i class=\"fa fa-sm fa-download\"></i>\n    Download notebook\n  </a>\n  <a class=\"btn\" href=\"https://colab.research.google.com/github/littlecolumns/ds4j-notebooks/blob/master/tampa-bay-times-schools/notebooks/Linear regression on Florida schools (No cleaning).ipynb\" target=\"_new\">\n    <i class=\"fa fa-sm fa-laptop\"></i>\n    Interactive version\n  </a>\n</p>"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Prep work: Downloading necessary files\n",
                "Before we get started, we need to download all of the data we'll be using.\n",
                "* **data-cleaned-merged.csv:** cleaned and merged school data - including free/reduced lunch, race, gender, and test scores\n"
            ]
        },
        {
            "cell_type": "code",
            "metadata": {},
            "source": [
                "# Make data directory if it doesn't exist\n",
                "!mkdir -p data\n",
                "!wget -nc https://nyc3.digitaloceanspaces.com/ml-files-distro/v1/tampa-bay-times-schools/data/data-cleaned-merged.csv -P data"
            ],
            "outputs": [],
            "execution_count": null
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Imports\n",
                "\n",
                "We'll want pandas and seaborn. You'll want want to set pandas to display a lot of columns and rows at a time."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 2,
            "metadata": {},
            "outputs": [],
            "source": [
                "import pandas as pd\n",
                "import seaborn as sns\n",
                "\n",
                "pd.set_option(\"display.max_columns\", 200)\n",
                "pd.set_option(\"display.max_rows\", 200)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Reading in our data\n",
                "\n",
                "We'll start by reading in the dataset, being sure to read in the **district and school number as strings** in case we need to merge on anything later. If pandas gets its way, it would read the district/school numbers in as integers and turn something like `0001` into `1`. This is unbelievably common when reading datasets with IDs into Excel or pandas, and is always something you should watch out for!"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 3,
            "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>District Number</th>\n",
                            "      <th>District Name</th>\n",
                            "      <th>School Number</th>\n",
                            "      <th>School Name</th>\n",
                            "      <th>pct_passing</th>\n",
                            "      <th>pct_free_or_reduced</th>\n",
                            "      <th>pct_black</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>01</td>\n",
                            "      <td>ALACHUA</td>\n",
                            "      <td>0021</td>\n",
                            "      <td>CHARLES W. DUVAL ELEM SCHOOL</td>\n",
                            "      <td>36.0</td>\n",
                            "      <td>0.959119</td>\n",
                            "      <td>0.903226</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>01</td>\n",
                            "      <td>ALACHUA</td>\n",
                            "      <td>0031</td>\n",
                            "      <td>J. J. FINLEY ELEMENTARY SCHOOL</td>\n",
                            "      <td>74.0</td>\n",
                            "      <td>0.546689</td>\n",
                            "      <td>0.287375</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>01</td>\n",
                            "      <td>ALACHUA</td>\n",
                            "      <td>0041</td>\n",
                            "      <td>STEPHEN FOSTER ELEMENTARY SCHOOL</td>\n",
                            "      <td>71.0</td>\n",
                            "      <td>0.606987</td>\n",
                            "      <td>0.383158</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>01</td>\n",
                            "      <td>ALACHUA</td>\n",
                            "      <td>0052</td>\n",
                            "      <td>A.QUINN JONES/EXCEP.STUDENT CENTER</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>0.802817</td>\n",
                            "      <td>0.666667</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>4</th>\n",
                            "      <td>01</td>\n",
                            "      <td>ALACHUA</td>\n",
                            "      <td>0071</td>\n",
                            "      <td>LAKE FOREST ELEMENTARY SCHOOL</td>\n",
                            "      <td>19.0</td>\n",
                            "      <td>0.957655</td>\n",
                            "      <td>0.849231</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "  District Number District Name School Number  \\\n",
                            "0              01       ALACHUA          0021   \n",
                            "1              01       ALACHUA          0031   \n",
                            "2              01       ALACHUA          0041   \n",
                            "3              01       ALACHUA          0052   \n",
                            "4              01       ALACHUA          0071   \n",
                            "\n",
                            "                          School Name  pct_passing  pct_free_or_reduced  \\\n",
                            "0        CHARLES W. DUVAL ELEM SCHOOL         36.0             0.959119   \n",
                            "1      J. J. FINLEY ELEMENTARY SCHOOL         74.0             0.546689   \n",
                            "2    STEPHEN FOSTER ELEMENTARY SCHOOL         71.0             0.606987   \n",
                            "3  A.QUINN JONES/EXCEP.STUDENT CENTER          NaN             0.802817   \n",
                            "4       LAKE FOREST ELEMENTARY SCHOOL         19.0             0.957655   \n",
                            "\n",
                            "   pct_black  \n",
                            "0   0.903226  \n",
                            "1   0.287375  \n",
                            "2   0.383158  \n",
                            "3   0.666667  \n",
                            "4   0.849231  "
                        ]
                    },
                    "execution_count": 3,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "df = pd.read_csv(\"data/data-cleaned-merged.csv\", dtype={'District Number': str, 'School Number': 'str'})\n",
                "df.head()"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "This dataset includes school information, as well as\n",
                "\n",
                "* The percentage of students who passed their 4th grade standardized reading test\n",
                "* The percentage of students receiving free or reduced price lunch, as a proxy for poverty\n",
                "* The percentage of students that are Black/African-American"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Converting to percentages\n",
                "\n",
                "It's really easy to get mixed up later if we don't have our percentage columns as actual percents. Multiply any percentages that go 0-1 by 100 to turn them into 0-100 instead.\n",
                "\n",
                "* **Tip:** Make sure your numbers are 1-100 after you multiply!"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 11,
            "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>District Number</th>\n",
                            "      <th>District Name</th>\n",
                            "      <th>School Number</th>\n",
                            "      <th>School Name</th>\n",
                            "      <th>pct_passing</th>\n",
                            "      <th>pct_free_or_reduced</th>\n",
                            "      <th>pct_black</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>01</td>\n",
                            "      <td>ALACHUA</td>\n",
                            "      <td>0021</td>\n",
                            "      <td>CHARLES W. DUVAL ELEM SCHOOL</td>\n",
                            "      <td>36.0</td>\n",
                            "      <td>95.911950</td>\n",
                            "      <td>90.322581</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>01</td>\n",
                            "      <td>ALACHUA</td>\n",
                            "      <td>0031</td>\n",
                            "      <td>J. J. FINLEY ELEMENTARY SCHOOL</td>\n",
                            "      <td>74.0</td>\n",
                            "      <td>54.668930</td>\n",
                            "      <td>28.737542</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>01</td>\n",
                            "      <td>ALACHUA</td>\n",
                            "      <td>0041</td>\n",
                            "      <td>STEPHEN FOSTER ELEMENTARY SCHOOL</td>\n",
                            "      <td>71.0</td>\n",
                            "      <td>60.698690</td>\n",
                            "      <td>38.315789</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>3</th>\n",
                            "      <td>01</td>\n",
                            "      <td>ALACHUA</td>\n",
                            "      <td>0052</td>\n",
                            "      <td>A.QUINN JONES/EXCEP.STUDENT CENTER</td>\n",
                            "      <td>NaN</td>\n",
                            "      <td>80.281690</td>\n",
                            "      <td>66.666667</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>4</th>\n",
                            "      <td>01</td>\n",
                            "      <td>ALACHUA</td>\n",
                            "      <td>0071</td>\n",
                            "      <td>LAKE FOREST ELEMENTARY SCHOOL</td>\n",
                            "      <td>19.0</td>\n",
                            "      <td>95.765472</td>\n",
                            "      <td>84.923077</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "  District Number District Name School Number  \\\n",
                            "0              01       ALACHUA          0021   \n",
                            "1              01       ALACHUA          0031   \n",
                            "2              01       ALACHUA          0041   \n",
                            "3              01       ALACHUA          0052   \n",
                            "4              01       ALACHUA          0071   \n",
                            "\n",
                            "                          School Name  pct_passing  pct_free_or_reduced  \\\n",
                            "0        CHARLES W. DUVAL ELEM SCHOOL         36.0            95.911950   \n",
                            "1      J. J. FINLEY ELEMENTARY SCHOOL         74.0            54.668930   \n",
                            "2    STEPHEN FOSTER ELEMENTARY SCHOOL         71.0            60.698690   \n",
                            "3  A.QUINN JONES/EXCEP.STUDENT CENTER          NaN            80.281690   \n",
                            "4       LAKE FOREST ELEMENTARY SCHOOL         19.0            95.765472   \n",
                            "\n",
                            "   pct_black  \n",
                            "0  90.322581  \n",
                            "1  28.737542  \n",
                            "2  38.315789  \n",
                            "3  66.666667  \n",
                            "4  84.923077  "
                        ]
                    },
                    "execution_count": 11,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "df.pct_free_or_reduced = df.pct_free_or_reduced * 100\n",
                "df.pct_black = df.pct_black * 100\n",
                "df.head()"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Graphing our data"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Use seaborn's `regplot` to plot the relationship between free/reduced lunch and percent passing, and the same with percent black and percent passing.\n",
                "\n",
                "* **Tip:** You can use `scatter_kws={'alpha':0.3}` to see things a bit more nicely"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 12,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "<matplotlib.axes._subplots.AxesSubplot at 0x117d69358>"
                        ]
                    },
                    "execution_count": 12,
                    "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": [
                "sns.regplot(data=df, x='pct_free_or_reduced', y='pct_passing', scatter_kws={'alpha':0.3})"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 13,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "<matplotlib.axes._subplots.AxesSubplot at 0x117ea7208>"
                        ]
                    },
                    "execution_count": 13,
                    "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": [
                "sns.regplot(data=df, x='pct_black', y='pct_passing', scatter_kws={'alpha':0.3})"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Linear regression\n",
                "\n",
                "Now let's be a little more exact: run a linear regression for percent passing that takes into account both percent black and percent free or reduced."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 18,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<table class=\"simpletable\">\n",
                            "<caption>OLS Regression Results</caption>\n",
                            "<tr>\n",
                            "  <th>Dep. Variable:</th>       <td>pct_passing</td>   <th>  R-squared:         </th> <td>   0.575</td> \n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.575</td> \n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1398.</td> \n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>Date:</th>             <td>Thu, 07 Nov 2019</td> <th>  Prob (F-statistic):</th>  <td>  0.00</td>  \n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>Time:</th>                 <td>12:58:10</td>     <th>  Log-Likelihood:    </th> <td> -7963.4</td> \n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>No. Observations:</th>      <td>  2070</td>      <th>  AIC:               </th> <td>1.593e+04</td>\n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>Df Residuals:</th>          <td>  2067</td>      <th>  BIC:               </th> <td>1.595e+04</td>\n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>    \n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
                            "</tr>\n",
                            "</table>\n",
                            "<table class=\"simpletable\">\n",
                            "<tr>\n",
                            "           <td></td>              <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>Intercept</th>           <td>   89.3659</td> <td>    0.707</td> <td>  126.460</td> <td> 0.000</td> <td>   87.980</td> <td>   90.752</td>\n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>pct_black</th>           <td>   -0.2041</td> <td>    0.011</td> <td>  -18.669</td> <td> 0.000</td> <td>   -0.226</td> <td>   -0.183</td>\n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>pct_free_or_reduced</th> <td>   -0.3984</td> <td>    0.012</td> <td>  -34.271</td> <td> 0.000</td> <td>   -0.421</td> <td>   -0.376</td>\n",
                            "</tr>\n",
                            "</table>\n",
                            "<table class=\"simpletable\">\n",
                            "<tr>\n",
                            "  <th>Omnibus:</th>       <td>178.385</td> <th>  Durbin-Watson:     </th> <td>   1.569</td> \n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 560.988</td> \n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>Skew:</th>          <td>-0.423</td>  <th>  Prob(JB):          </th> <td>1.52e-122</td>\n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>Kurtosis:</th>      <td> 5.406</td>  <th>  Cond. No.          </th> <td>    213.</td> \n",
                            "</tr>\n",
                            "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
                        ],
                        "text/plain": [
                            "<class 'statsmodels.iolib.summary.Summary'>\n",
                            "\"\"\"\n",
                            "                            OLS Regression Results                            \n",
                            "==============================================================================\n",
                            "Dep. Variable:            pct_passing   R-squared:                       0.575\n",
                            "Model:                            OLS   Adj. R-squared:                  0.575\n",
                            "Method:                 Least Squares   F-statistic:                     1398.\n",
                            "Date:                Thu, 07 Nov 2019   Prob (F-statistic):               0.00\n",
                            "Time:                        12:58:10   Log-Likelihood:                -7963.4\n",
                            "No. Observations:                2070   AIC:                         1.593e+04\n",
                            "Df Residuals:                    2067   BIC:                         1.595e+04\n",
                            "Df Model:                           2                                         \n",
                            "Covariance Type:            nonrobust                                         \n",
                            "=======================================================================================\n",
                            "                          coef    std err          t      P>|t|      [0.025      0.975]\n",
                            "---------------------------------------------------------------------------------------\n",
                            "Intercept              89.3659      0.707    126.460      0.000      87.980      90.752\n",
                            "pct_black              -0.2041      0.011    -18.669      0.000      -0.226      -0.183\n",
                            "pct_free_or_reduced    -0.3984      0.012    -34.271      0.000      -0.421      -0.376\n",
                            "==============================================================================\n",
                            "Omnibus:                      178.385   Durbin-Watson:                   1.569\n",
                            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              560.988\n",
                            "Skew:                          -0.423   Prob(JB):                    1.52e-122\n",
                            "Kurtosis:                       5.406   Cond. No.                         213.\n",
                            "==============================================================================\n",
                            "\n",
                            "Warnings:\n",
                            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
                            "\"\"\""
                        ]
                    },
                    "execution_count": 18,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "import statsmodels.formula.api as smf\n",
                "\n",
                "model = smf.ols(\"pct_passing ~ pct_black + pct_free_or_reduced\", data=df)\n",
                "result = model.fit()\n",
                "result.summary()"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Describe the relationship coefficient using \"real\" words\n",
                "\n",
                "For example, \"For every X change, we get Y change\""
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": []
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# Overperformers and underperformers\n",
                "\n",
                "The point of the regression is to predict the percent passing, right? We can use `result.predict()` to get the predicted passing rate for each school. Try to run it below: "
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {},
            "outputs": [],
            "source": [
                "result.predict()"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "Now, let's **save that value into a new column**, we can call it `predicted_passing`. It won't work for schools that are missing `pct_black` or `pct_free_or_reduced`, though, so first we'll need to drop those rows."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 26,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "(2070, 7)"
                        ]
                    },
                    "execution_count": 26,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "df.shape"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 27,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "(2070, 7)"
                        ]
                    },
                    "execution_count": 27,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "df = df.dropna(subset=['pct_black', 'pct_free_or_reduced'])\n",
                "df.shape"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 28,
            "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>District Number</th>\n",
                            "      <th>District Name</th>\n",
                            "      <th>School Number</th>\n",
                            "      <th>School Name</th>\n",
                            "      <th>pct_passing</th>\n",
                            "      <th>pct_free_or_reduced</th>\n",
                            "      <th>pct_black</th>\n",
                            "      <th>predicted_passing</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>01</td>\n",
                            "      <td>ALACHUA</td>\n",
                            "      <td>0021</td>\n",
                            "      <td>CHARLES W. DUVAL ELEM SCHOOL</td>\n",
                            "      <td>36.0</td>\n",
                            "      <td>95.911950</td>\n",
                            "      <td>90.322581</td>\n",
                            "      <td>32.722663</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>01</td>\n",
                            "      <td>ALACHUA</td>\n",
                            "      <td>0031</td>\n",
                            "      <td>J. J. FINLEY ELEMENTARY SCHOOL</td>\n",
                            "      <td>74.0</td>\n",
                            "      <td>54.668930</td>\n",
                            "      <td>28.737542</td>\n",
                            "      <td>61.722168</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>01</td>\n",
                            "      <td>ALACHUA</td>\n",
                            "      <td>0041</td>\n",
                            "      <td>STEPHEN FOSTER ELEMENTARY SCHOOL</td>\n",
                            "      <td>71.0</td>\n",
                            "      <td>60.698690</td>\n",
                            "      <td>38.315789</td>\n",
                            "      <td>57.365169</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>4</th>\n",
                            "      <td>01</td>\n",
                            "      <td>ALACHUA</td>\n",
                            "      <td>0071</td>\n",
                            "      <td>LAKE FOREST ELEMENTARY SCHOOL</td>\n",
                            "      <td>19.0</td>\n",
                            "      <td>95.765472</td>\n",
                            "      <td>84.923077</td>\n",
                            "      <td>33.883060</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>6</th>\n",
                            "      <td>01</td>\n",
                            "      <td>ALACHUA</td>\n",
                            "      <td>0091</td>\n",
                            "      <td>LITTLEWOOD ELEMENTARY SCHOOL</td>\n",
                            "      <td>56.0</td>\n",
                            "      <td>59.394904</td>\n",
                            "      <td>30.733229</td>\n",
                            "      <td>59.432166</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "  District Number District Name School Number  \\\n",
                            "0              01       ALACHUA          0021   \n",
                            "1              01       ALACHUA          0031   \n",
                            "2              01       ALACHUA          0041   \n",
                            "4              01       ALACHUA          0071   \n",
                            "6              01       ALACHUA          0091   \n",
                            "\n",
                            "                        School Name  pct_passing  pct_free_or_reduced  \\\n",
                            "0      CHARLES W. DUVAL ELEM SCHOOL         36.0            95.911950   \n",
                            "1    J. J. FINLEY ELEMENTARY SCHOOL         74.0            54.668930   \n",
                            "2  STEPHEN FOSTER ELEMENTARY SCHOOL         71.0            60.698690   \n",
                            "4     LAKE FOREST ELEMENTARY SCHOOL         19.0            95.765472   \n",
                            "6      LITTLEWOOD ELEMENTARY SCHOOL         56.0            59.394904   \n",
                            "\n",
                            "   pct_black  predicted_passing  \n",
                            "0  90.322581          32.722663  \n",
                            "1  28.737542          61.722168  \n",
                            "2  38.315789          57.365169  \n",
                            "4  84.923077          33.883060  \n",
                            "6  30.733229          59.432166  "
                        ]
                    },
                    "execution_count": 28,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "df['predicted_passing'] = result.predict()\n",
                "df.head()"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Confirm that Charles W. Duval had a predicted passing rate of 32."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 32,
            "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>District Number</th>\n",
                            "      <th>District Name</th>\n",
                            "      <th>School Number</th>\n",
                            "      <th>School Name</th>\n",
                            "      <th>pct_passing</th>\n",
                            "      <th>pct_free_or_reduced</th>\n",
                            "      <th>pct_black</th>\n",
                            "      <th>predicted_passing</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>01</td>\n",
                            "      <td>ALACHUA</td>\n",
                            "      <td>0021</td>\n",
                            "      <td>CHARLES W. DUVAL ELEM SCHOOL</td>\n",
                            "      <td>36.0</td>\n",
                            "      <td>95.911950</td>\n",
                            "      <td>90.322581</td>\n",
                            "      <td>32.722663</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>249</th>\n",
                            "      <td>06</td>\n",
                            "      <td>BROWARD</td>\n",
                            "      <td>3221</td>\n",
                            "      <td>CHARLES DREW ELEMENTARY SCHOOL</td>\n",
                            "      <td>38.0</td>\n",
                            "      <td>96.194825</td>\n",
                            "      <td>66.998342</td>\n",
                            "      <td>37.370480</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>349</th>\n",
                            "      <td>10</td>\n",
                            "      <td>CLAY</td>\n",
                            "      <td>0071</td>\n",
                            "      <td>CHARLES E. BENNETT ELEMENTARY SCHOO</td>\n",
                            "      <td>57.0</td>\n",
                            "      <td>78.465347</td>\n",
                            "      <td>16.586538</td>\n",
                            "      <td>54.722458</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>495</th>\n",
                            "      <td>13</td>\n",
                            "      <td>MIAMI DADE</td>\n",
                            "      <td>1401</td>\n",
                            "      <td>CHARLES R DREW K-8 CENTER</td>\n",
                            "      <td>25.0</td>\n",
                            "      <td>98.039216</td>\n",
                            "      <td>91.964286</td>\n",
                            "      <td>31.540153</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>530</th>\n",
                            "      <td>13</td>\n",
                            "      <td>MIAMI DADE</td>\n",
                            "      <td>2331</td>\n",
                            "      <td>CHARLES R HADLEY ELEM SCHOOL</td>\n",
                            "      <td>66.0</td>\n",
                            "      <td>84.563107</td>\n",
                            "      <td>0.203459</td>\n",
                            "      <td>55.637109</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>691</th>\n",
                            "      <td>13</td>\n",
                            "      <td>MIAMI DADE</td>\n",
                            "      <td>5991</td>\n",
                            "      <td>CHARLES DAVID WYCHE JR ELEMENTARY</td>\n",
                            "      <td>46.0</td>\n",
                            "      <td>89.185393</td>\n",
                            "      <td>17.622378</td>\n",
                            "      <td>50.240512</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "    District Number District Name School Number  \\\n",
                            "0                01       ALACHUA          0021   \n",
                            "249              06       BROWARD          3221   \n",
                            "349              10          CLAY          0071   \n",
                            "495              13    MIAMI DADE          1401   \n",
                            "530              13    MIAMI DADE          2331   \n",
                            "691              13    MIAMI DADE          5991   \n",
                            "\n",
                            "                             School Name  pct_passing  pct_free_or_reduced  \\\n",
                            "0           CHARLES W. DUVAL ELEM SCHOOL         36.0            95.911950   \n",
                            "249       CHARLES DREW ELEMENTARY SCHOOL         38.0            96.194825   \n",
                            "349  CHARLES E. BENNETT ELEMENTARY SCHOO         57.0            78.465347   \n",
                            "495            CHARLES R DREW K-8 CENTER         25.0            98.039216   \n",
                            "530         CHARLES R HADLEY ELEM SCHOOL         66.0            84.563107   \n",
                            "691    CHARLES DAVID WYCHE JR ELEMENTARY         46.0            89.185393   \n",
                            "\n",
                            "     pct_black  predicted_passing  \n",
                            "0    90.322581          32.722663  \n",
                            "249  66.998342          37.370480  \n",
                            "349  16.586538          54.722458  \n",
                            "495  91.964286          31.540153  \n",
                            "530   0.203459          55.637109  \n",
                            "691  17.622378          50.240512  "
                        ]
                    },
                    "execution_count": 32,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "df[df['School Name'].str.contains(\"CHARLES\")]"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Now let's find the difference between the predicted passing rate and the actual passing rate\n",
                "\n",
                "If we're being stats-y, this is called **the residual**. Save it into a new column called.... `residual`.\n",
                "\n",
                "You could calculate it as `df.predicted_passing - df.pct_passing` but you can also just ask for `result.resid`."
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 34,
            "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>District Number</th>\n",
                            "      <th>District Name</th>\n",
                            "      <th>School Number</th>\n",
                            "      <th>School Name</th>\n",
                            "      <th>pct_passing</th>\n",
                            "      <th>pct_free_or_reduced</th>\n",
                            "      <th>pct_black</th>\n",
                            "      <th>predicted_passing</th>\n",
                            "      <th>residual</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>0</th>\n",
                            "      <td>01</td>\n",
                            "      <td>ALACHUA</td>\n",
                            "      <td>0021</td>\n",
                            "      <td>CHARLES W. DUVAL ELEM SCHOOL</td>\n",
                            "      <td>36.0</td>\n",
                            "      <td>95.911950</td>\n",
                            "      <td>90.322581</td>\n",
                            "      <td>32.722663</td>\n",
                            "      <td>3.277337</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1</th>\n",
                            "      <td>01</td>\n",
                            "      <td>ALACHUA</td>\n",
                            "      <td>0031</td>\n",
                            "      <td>J. J. FINLEY ELEMENTARY SCHOOL</td>\n",
                            "      <td>74.0</td>\n",
                            "      <td>54.668930</td>\n",
                            "      <td>28.737542</td>\n",
                            "      <td>61.722168</td>\n",
                            "      <td>12.277832</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2</th>\n",
                            "      <td>01</td>\n",
                            "      <td>ALACHUA</td>\n",
                            "      <td>0041</td>\n",
                            "      <td>STEPHEN FOSTER ELEMENTARY SCHOOL</td>\n",
                            "      <td>71.0</td>\n",
                            "      <td>60.698690</td>\n",
                            "      <td>38.315789</td>\n",
                            "      <td>57.365169</td>\n",
                            "      <td>13.634831</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>4</th>\n",
                            "      <td>01</td>\n",
                            "      <td>ALACHUA</td>\n",
                            "      <td>0071</td>\n",
                            "      <td>LAKE FOREST ELEMENTARY SCHOOL</td>\n",
                            "      <td>19.0</td>\n",
                            "      <td>95.765472</td>\n",
                            "      <td>84.923077</td>\n",
                            "      <td>33.883060</td>\n",
                            "      <td>-14.883060</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>6</th>\n",
                            "      <td>01</td>\n",
                            "      <td>ALACHUA</td>\n",
                            "      <td>0091</td>\n",
                            "      <td>LITTLEWOOD ELEMENTARY SCHOOL</td>\n",
                            "      <td>56.0</td>\n",
                            "      <td>59.394904</td>\n",
                            "      <td>30.733229</td>\n",
                            "      <td>59.432166</td>\n",
                            "      <td>-3.432166</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "  District Number District Name School Number  \\\n",
                            "0              01       ALACHUA          0021   \n",
                            "1              01       ALACHUA          0031   \n",
                            "2              01       ALACHUA          0041   \n",
                            "4              01       ALACHUA          0071   \n",
                            "6              01       ALACHUA          0091   \n",
                            "\n",
                            "                        School Name  pct_passing  pct_free_or_reduced  \\\n",
                            "0      CHARLES W. DUVAL ELEM SCHOOL         36.0            95.911950   \n",
                            "1    J. J. FINLEY ELEMENTARY SCHOOL         74.0            54.668930   \n",
                            "2  STEPHEN FOSTER ELEMENTARY SCHOOL         71.0            60.698690   \n",
                            "4     LAKE FOREST ELEMENTARY SCHOOL         19.0            95.765472   \n",
                            "6      LITTLEWOOD ELEMENTARY SCHOOL         56.0            59.394904   \n",
                            "\n",
                            "   pct_black  predicted_passing   residual  \n",
                            "0  90.322581          32.722663   3.277337  \n",
                            "1  28.737542          61.722168  12.277832  \n",
                            "2  38.315789          57.365169  13.634831  \n",
                            "4  84.923077          33.883060 -14.883060  \n",
                            "6  30.733229          59.432166  -3.432166  "
                        ]
                    },
                    "execution_count": 34,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "df['residual'] = result.resid\n",
                "df.head()"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Find the 10 schools that did much worse than predicted, based on the demographics we're looking at\n",
                "\n",
                "* PRINCETON HOUSE CHARTER should be the worst, with PEPIN ACADEMIES below that"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 35,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
                            "    .dataframe tbody tr th:only-of-type {\n",
                            "        vertical-align: middle;\n",
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                            "\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>District Number</th>\n",
                            "      <th>District Name</th>\n",
                            "      <th>School Number</th>\n",
                            "      <th>School Name</th>\n",
                            "      <th>pct_passing</th>\n",
                            "      <th>pct_free_or_reduced</th>\n",
                            "      <th>pct_black</th>\n",
                            "      <th>predicted_passing</th>\n",
                            "      <th>residual</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>1410</th>\n",
                            "      <td>48</td>\n",
                            "      <td>ORANGE</td>\n",
                            "      <td>0055</td>\n",
                            "      <td>PRINCETON HOUSE CHARTER</td>\n",
                            "      <td>9.0</td>\n",
                            "      <td>25.000000</td>\n",
                            "      <td>14.743590</td>\n",
                            "      <td>76.397521</td>\n",
                            "      <td>-67.397521</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1073</th>\n",
                            "      <td>29</td>\n",
                            "      <td>HILLSBOROUGH</td>\n",
                            "      <td>6609</td>\n",
                            "      <td>PEPIN ACADEMIES</td>\n",
                            "      <td>4.0</td>\n",
                            "      <td>37.653240</td>\n",
                            "      <td>24.747475</td>\n",
                            "      <td>69.315062</td>\n",
                            "      <td>-65.315062</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2180</th>\n",
                            "      <td>68</td>\n",
                            "      <td>FSDB</td>\n",
                            "      <td>0011</td>\n",
                            "      <td>DEAF ELEMENTARY SCHOOL (FSDB)</td>\n",
                            "      <td>7.0</td>\n",
                            "      <td>69.491525</td>\n",
                            "      <td>14.782609</td>\n",
                            "      <td>58.665531</td>\n",
                            "      <td>-51.665531</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1717</th>\n",
                            "      <td>50</td>\n",
                            "      <td>PALM BEACH</td>\n",
                            "      <td>4037</td>\n",
                            "      <td>LEARNING PATH ACADEMY</td>\n",
                            "      <td>0.0</td>\n",
                            "      <td>93.650794</td>\n",
                            "      <td>14.569536</td>\n",
                            "      <td>49.084725</td>\n",
                            "      <td>-49.084725</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1948</th>\n",
                            "      <td>53</td>\n",
                            "      <td>POLK</td>\n",
                            "      <td>9203</td>\n",
                            "      <td>B.E.S.T.</td>\n",
                            "      <td>7.0</td>\n",
                            "      <td>69.767442</td>\n",
                            "      <td>50.000000</td>\n",
                            "      <td>51.367702</td>\n",
                            "      <td>-44.367702</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>420</th>\n",
                            "      <td>12</td>\n",
                            "      <td>COLUMBIA</td>\n",
                            "      <td>0400</td>\n",
                            "      <td>SHINING STAR ACADEMY OF THE ARTS</td>\n",
                            "      <td>43.0</td>\n",
                            "      <td>4.721030</td>\n",
                            "      <td>4.950495</td>\n",
                            "      <td>86.474808</td>\n",
                            "      <td>-43.474808</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1424</th>\n",
                            "      <td>48</td>\n",
                            "      <td>ORANGE</td>\n",
                            "      <td>0185</td>\n",
                            "      <td>RENAISSANCE CHTR SCH AT CHICKASAW</td>\n",
                            "      <td>44.0</td>\n",
                            "      <td>0.696056</td>\n",
                            "      <td>10.146444</td>\n",
                            "      <td>87.017732</td>\n",
                            "      <td>-43.017732</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1819</th>\n",
                            "      <td>52</td>\n",
                            "      <td>PINELLAS</td>\n",
                            "      <td>3231</td>\n",
                            "      <td>RICHARD L. SANDERS SCHOOL</td>\n",
                            "      <td>17.0</td>\n",
                            "      <td>50.000000</td>\n",
                            "      <td>51.886792</td>\n",
                            "      <td>58.857333</td>\n",
                            "      <td>-41.857333</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>879</th>\n",
                            "      <td>22</td>\n",
                            "      <td>GLADES</td>\n",
                            "      <td>0056</td>\n",
                            "      <td>PEMAYETV EMAHAKV CHARTER OUR WAY SC</td>\n",
                            "      <td>48.0</td>\n",
                            "      <td>0.000000</td>\n",
                            "      <td>0.581395</td>\n",
                            "      <td>89.247257</td>\n",
                            "      <td>-41.247257</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1810</th>\n",
                            "      <td>52</td>\n",
                            "      <td>PINELLAS</td>\n",
                            "      <td>2441</td>\n",
                            "      <td>CHI CHI RODRIQUEZ ACADEMY</td>\n",
                            "      <td>15.0</td>\n",
                            "      <td>74.025974</td>\n",
                            "      <td>26.760563</td>\n",
                            "      <td>54.414434</td>\n",
                            "      <td>-39.414434</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "     District Number District Name School Number  \\\n",
                            "1410              48        ORANGE          0055   \n",
                            "1073              29  HILLSBOROUGH          6609   \n",
                            "2180              68          FSDB          0011   \n",
                            "1717              50    PALM BEACH          4037   \n",
                            "1948              53          POLK          9203   \n",
                            "420               12      COLUMBIA          0400   \n",
                            "1424              48        ORANGE          0185   \n",
                            "1819              52      PINELLAS          3231   \n",
                            "879               22        GLADES          0056   \n",
                            "1810              52      PINELLAS          2441   \n",
                            "\n",
                            "                              School Name  pct_passing  pct_free_or_reduced  \\\n",
                            "1410              PRINCETON HOUSE CHARTER          9.0            25.000000   \n",
                            "1073                      PEPIN ACADEMIES          4.0            37.653240   \n",
                            "2180        DEAF ELEMENTARY SCHOOL (FSDB)          7.0            69.491525   \n",
                            "1717                LEARNING PATH ACADEMY          0.0            93.650794   \n",
                            "1948                             B.E.S.T.          7.0            69.767442   \n",
                            "420      SHINING STAR ACADEMY OF THE ARTS         43.0             4.721030   \n",
                            "1424    RENAISSANCE CHTR SCH AT CHICKASAW         44.0             0.696056   \n",
                            "1819            RICHARD L. SANDERS SCHOOL         17.0            50.000000   \n",
                            "879   PEMAYETV EMAHAKV CHARTER OUR WAY SC         48.0             0.000000   \n",
                            "1810            CHI CHI RODRIQUEZ ACADEMY         15.0            74.025974   \n",
                            "\n",
                            "      pct_black  predicted_passing   residual  \n",
                            "1410  14.743590          76.397521 -67.397521  \n",
                            "1073  24.747475          69.315062 -65.315062  \n",
                            "2180  14.782609          58.665531 -51.665531  \n",
                            "1717  14.569536          49.084725 -49.084725  \n",
                            "1948  50.000000          51.367702 -44.367702  \n",
                            "420    4.950495          86.474808 -43.474808  \n",
                            "1424  10.146444          87.017732 -43.017732  \n",
                            "1819  51.886792          58.857333 -41.857333  \n",
                            "879    0.581395          89.247257 -41.247257  \n",
                            "1810  26.760563          54.414434 -39.414434  "
                        ]
                    },
                    "execution_count": 35,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "df.sort_values(by='residual').head(10)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Find the top 10 schools that did better than predicted, based on the demographics we're looking at\n",
                "\n",
                "* PARKWAY MIDDLE SCHOOL should be the best, and PATHWAYS should be second"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 38,
            "metadata": {},
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<div>\n",
                            "<style scoped>\n",
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                            "\n",
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                            "\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>District Number</th>\n",
                            "      <th>District Name</th>\n",
                            "      <th>School Number</th>\n",
                            "      <th>School Name</th>\n",
                            "      <th>pct_passing</th>\n",
                            "      <th>pct_free_or_reduced</th>\n",
                            "      <th>pct_black</th>\n",
                            "      <th>predicted_passing</th>\n",
                            "      <th>residual</th>\n",
                            "    </tr>\n",
                            "  </thead>\n",
                            "  <tbody>\n",
                            "    <tr>\n",
                            "      <th>165</th>\n",
                            "      <td>06</td>\n",
                            "      <td>BROWARD</td>\n",
                            "      <td>0701</td>\n",
                            "      <td>PARKWAY MIDDLE SCHOOL</td>\n",
                            "      <td>100.0</td>\n",
                            "      <td>85.758706</td>\n",
                            "      <td>86.286788</td>\n",
                            "      <td>37.591106</td>\n",
                            "      <td>62.408894</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>304</th>\n",
                            "      <td>06</td>\n",
                            "      <td>BROWARD</td>\n",
                            "      <td>5372</td>\n",
                            "      <td>PATHWAYS ACADEMY K-8 CENTER</td>\n",
                            "      <td>83.0</td>\n",
                            "      <td>95.652174</td>\n",
                            "      <td>64.206642</td>\n",
                            "      <td>38.156444</td>\n",
                            "      <td>44.843556</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>661</th>\n",
                            "      <td>13</td>\n",
                            "      <td>MIAMI DADE</td>\n",
                            "      <td>5131</td>\n",
                            "      <td>N DADE CENTER FOR MODERN LANGUAGE</td>\n",
                            "      <td>89.0</td>\n",
                            "      <td>76.767677</td>\n",
                            "      <td>57.323232</td>\n",
                            "      <td>47.084347</td>\n",
                            "      <td>41.915653</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>566</th>\n",
                            "      <td>13</td>\n",
                            "      <td>MIAMI DADE</td>\n",
                            "      <td>3101</td>\n",
                            "      <td>FRANK CRAWFORD MARTIN K-8 CENTER</td>\n",
                            "      <td>91.0</td>\n",
                            "      <td>54.096916</td>\n",
                            "      <td>59.079284</td>\n",
                            "      <td>55.757254</td>\n",
                            "      <td>35.242746</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>2187</th>\n",
                            "      <td>74</td>\n",
                            "      <td>FAMU LAB SCH</td>\n",
                            "      <td>0351</td>\n",
                            "      <td>FAMU DEVELOP RESEARCH</td>\n",
                            "      <td>77.0</td>\n",
                            "      <td>68.710359</td>\n",
                            "      <td>96.881497</td>\n",
                            "      <td>42.220239</td>\n",
                            "      <td>34.779761</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>281</th>\n",
                            "      <td>06</td>\n",
                            "      <td>BROWARD</td>\n",
                            "      <td>5021</td>\n",
                            "      <td>SOMERSET NEIGHBORHOOD SCHOOL</td>\n",
                            "      <td>77.0</td>\n",
                            "      <td>73.611111</td>\n",
                            "      <td>80.834915</td>\n",
                            "      <td>43.543061</td>\n",
                            "      <td>33.456939</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>1943</th>\n",
                            "      <td>53</td>\n",
                            "      <td>POLK</td>\n",
                            "      <td>8121</td>\n",
                            "      <td>HARTRIDGE ACADEMY</td>\n",
                            "      <td>96.0</td>\n",
                            "      <td>61.157025</td>\n",
                            "      <td>9.243697</td>\n",
                            "      <td>63.116233</td>\n",
                            "      <td>32.883767</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>481</th>\n",
                            "      <td>13</td>\n",
                            "      <td>MIAMI DADE</td>\n",
                            "      <td>1001</td>\n",
                            "      <td>CORAL PARK ELEMENTARY SCHOOL</td>\n",
                            "      <td>90.0</td>\n",
                            "      <td>77.669903</td>\n",
                            "      <td>0.688468</td>\n",
                            "      <td>58.284153</td>\n",
                            "      <td>31.715847</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>285</th>\n",
                            "      <td>06</td>\n",
                            "      <td>BROWARD</td>\n",
                            "      <td>5031</td>\n",
                            "      <td>CHARTER SCHOOL OF EXCELLENCE</td>\n",
                            "      <td>77.0</td>\n",
                            "      <td>74.817518</td>\n",
                            "      <td>68.592058</td>\n",
                            "      <td>45.561248</td>\n",
                            "      <td>31.438752</td>\n",
                            "    </tr>\n",
                            "    <tr>\n",
                            "      <th>677</th>\n",
                            "      <td>13</td>\n",
                            "      <td>MIAMI DADE</td>\n",
                            "      <td>5561</td>\n",
                            "      <td>FRANCES S. TUCKER ELEM. SCHOOL</td>\n",
                            "      <td>75.0</td>\n",
                            "      <td>93.253012</td>\n",
                            "      <td>41.504854</td>\n",
                            "      <td>43.745658</td>\n",
                            "      <td>31.254342</td>\n",
                            "    </tr>\n",
                            "  </tbody>\n",
                            "</table>\n",
                            "</div>"
                        ],
                        "text/plain": [
                            "     District Number District Name School Number  \\\n",
                            "165               06       BROWARD          0701   \n",
                            "304               06       BROWARD          5372   \n",
                            "661               13    MIAMI DADE          5131   \n",
                            "566               13    MIAMI DADE          3101   \n",
                            "2187              74  FAMU LAB SCH          0351   \n",
                            "281               06       BROWARD          5021   \n",
                            "1943              53          POLK          8121   \n",
                            "481               13    MIAMI DADE          1001   \n",
                            "285               06       BROWARD          5031   \n",
                            "677               13    MIAMI DADE          5561   \n",
                            "\n",
                            "                            School Name  pct_passing  pct_free_or_reduced  \\\n",
                            "165               PARKWAY MIDDLE SCHOOL        100.0            85.758706   \n",
                            "304         PATHWAYS ACADEMY K-8 CENTER         83.0            95.652174   \n",
                            "661   N DADE CENTER FOR MODERN LANGUAGE         89.0            76.767677   \n",
                            "566    FRANK CRAWFORD MARTIN K-8 CENTER         91.0            54.096916   \n",
                            "2187              FAMU DEVELOP RESEARCH         77.0            68.710359   \n",
                            "281        SOMERSET NEIGHBORHOOD SCHOOL         77.0            73.611111   \n",
                            "1943                  HARTRIDGE ACADEMY         96.0            61.157025   \n",
                            "481        CORAL PARK ELEMENTARY SCHOOL         90.0            77.669903   \n",
                            "285        CHARTER SCHOOL OF EXCELLENCE         77.0            74.817518   \n",
                            "677      FRANCES S. TUCKER ELEM. SCHOOL         75.0            93.253012   \n",
                            "\n",
                            "      pct_black  predicted_passing   residual  \n",
                            "165   86.286788          37.591106  62.408894  \n",
                            "304   64.206642          38.156444  44.843556  \n",
                            "661   57.323232          47.084347  41.915653  \n",
                            "566   59.079284          55.757254  35.242746  \n",
                            "2187  96.881497          42.220239  34.779761  \n",
                            "281   80.834915          43.543061  33.456939  \n",
                            "1943   9.243697          63.116233  32.883767  \n",
                            "481    0.688468          58.284153  31.715847  \n",
                            "285   68.592058          45.561248  31.438752  \n",
                            "677   41.504854          43.745658  31.254342  "
                        ]
                    },
                    "execution_count": 38,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "df.sort_values(by='residual', ascending=False).head(10)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# What problems might our analysis have?\n",
                "\n",
                "We brought in two things we thought would do a good job covering socioeconomics and demographic patterns. What else might we be missing?\n",
                "\n",
                "* **Tip:** Pay attention to the names of the schools\n",
                "\n",
                "Is there a between using the raw number for the residual as opposed to standard deviation? (See Texas schools cheating scandal)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "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": "3.6.8"
        },
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