one_way_anova_sat_scores.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hypothesis testing using the Welch's t-test is for two independent samples of data. Like Paid and Unpaid posts\n",
    "\n",
    "So what happens if we want to know the statistical significance for k groups of data?\n",
    "This is where the analysis of variance technique, or ANOVA is useful."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll be looking at SAT scores for five different districts in New York City. \n",
    "\n",
    "Specifically, we'll be using \"scores.csv\" from Kaggle. \n",
    "\n",
    "First let's get the assumptions out of the way:\n",
    "\n",
    "1) The dependent variable (SAT scores) should be continuous.\n",
    "\n",
    "2) The independent variables (districts) should be two or more categorical groups.\n",
    "\n",
    "3) There must be different participants in each group with no participant being in more than one group.\n",
    "In our case, each school cannot be in more than one district.\n",
    "\n",
    "4) The dependent variable (SAT scores) should be approximately normally distributed for each category.\n",
    "\n",
    "5) Variances of each group are approximately equal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>School ID</th>\n",
       "      <th>School Name</th>\n",
       "      <th>Borough</th>\n",
       "      <th>Building Code</th>\n",
       "      <th>Street Address</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Zip Code</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>...</th>\n",
       "      <th>End Time</th>\n",
       "      <th>Student Enrollment</th>\n",
       "      <th>Percent White</th>\n",
       "      <th>Percent Black</th>\n",
       "      <th>Percent Hispanic</th>\n",
       "      <th>Percent Asian</th>\n",
       "      <th>Average Score (SAT Math)</th>\n",
       "      <th>Average Score (SAT Reading)</th>\n",
       "      <th>Average Score (SAT Writing)</th>\n",
       "      <th>Percent Tested</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>02M260</td>\n",
       "      <td>Clinton School Writers and Artists</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>M933</td>\n",
       "      <td>425 West 33rd Street</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>NY</td>\n",
       "      <td>10001</td>\n",
       "      <td>40.75321</td>\n",
       "      <td>-73.99786</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>06M211</td>\n",
       "      <td>Inwood Early College for Health and Informatio...</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>M052</td>\n",
       "      <td>650 Academy Street</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>NY</td>\n",
       "      <td>10002</td>\n",
       "      <td>40.86605</td>\n",
       "      <td>-73.92486</td>\n",
       "      <td>...</td>\n",
       "      <td>3:00 PM</td>\n",
       "      <td>87.0</td>\n",
       "      <td>3.4%</td>\n",
       "      <td>21.8%</td>\n",
       "      <td>67.8%</td>\n",
       "      <td>4.6%</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01M539</td>\n",
       "      <td>New Explorations into Science, Technology and ...</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>M022</td>\n",
       "      <td>111 Columbia Street</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>NY</td>\n",
       "      <td>10002</td>\n",
       "      <td>40.71873</td>\n",
       "      <td>-73.97943</td>\n",
       "      <td>...</td>\n",
       "      <td>4:00 PM</td>\n",
       "      <td>1735.0</td>\n",
       "      <td>28.6%</td>\n",
       "      <td>13.3%</td>\n",
       "      <td>18.0%</td>\n",
       "      <td>38.5%</td>\n",
       "      <td>657.0</td>\n",
       "      <td>601.0</td>\n",
       "      <td>601.0</td>\n",
       "      <td>91.0%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>02M294</td>\n",
       "      <td>Essex Street Academy</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>M445</td>\n",
       "      <td>350 Grand Street</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>NY</td>\n",
       "      <td>10002</td>\n",
       "      <td>40.71687</td>\n",
       "      <td>-73.98953</td>\n",
       "      <td>...</td>\n",
       "      <td>2:45 PM</td>\n",
       "      <td>358.0</td>\n",
       "      <td>11.7%</td>\n",
       "      <td>38.5%</td>\n",
       "      <td>41.3%</td>\n",
       "      <td>5.9%</td>\n",
       "      <td>395.0</td>\n",
       "      <td>411.0</td>\n",
       "      <td>387.0</td>\n",
       "      <td>78.9%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>02M308</td>\n",
       "      <td>Lower Manhattan Arts Academy</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>M445</td>\n",
       "      <td>350 Grand Street</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>NY</td>\n",
       "      <td>10002</td>\n",
       "      <td>40.71687</td>\n",
       "      <td>-73.98953</td>\n",
       "      <td>...</td>\n",
       "      <td>3:00 PM</td>\n",
       "      <td>383.0</td>\n",
       "      <td>3.1%</td>\n",
       "      <td>28.2%</td>\n",
       "      <td>56.9%</td>\n",
       "      <td>8.6%</td>\n",
       "      <td>418.0</td>\n",
       "      <td>428.0</td>\n",
       "      <td>415.0</td>\n",
       "      <td>65.1%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  School ID                                        School Name    Borough  \\\n",
       "0    02M260                 Clinton School Writers and Artists  Manhattan   \n",
       "1    06M211  Inwood Early College for Health and Informatio...  Manhattan   \n",
       "2    01M539  New Explorations into Science, Technology and ...  Manhattan   \n",
       "3    02M294                               Essex Street Academy  Manhattan   \n",
       "4    02M308                       Lower Manhattan Arts Academy  Manhattan   \n",
       "\n",
       "  Building Code        Street Address       City State  Zip Code  Latitude  \\\n",
       "0          M933  425 West 33rd Street  Manhattan    NY     10001  40.75321   \n",
       "1          M052    650 Academy Street  Manhattan    NY     10002  40.86605   \n",
       "2          M022   111 Columbia Street  Manhattan    NY     10002  40.71873   \n",
       "3          M445      350 Grand Street  Manhattan    NY     10002  40.71687   \n",
       "4          M445      350 Grand Street  Manhattan    NY     10002  40.71687   \n",
       "\n",
       "   Longitude  ... End Time Student Enrollment Percent White  Percent Black  \\\n",
       "0  -73.99786  ...      NaN                NaN           NaN            NaN   \n",
       "1  -73.92486  ...  3:00 PM               87.0          3.4%          21.8%   \n",
       "2  -73.97943  ...  4:00 PM             1735.0         28.6%          13.3%   \n",
       "3  -73.98953  ...  2:45 PM              358.0         11.7%          38.5%   \n",
       "4  -73.98953  ...  3:00 PM              383.0          3.1%          28.2%   \n",
       "\n",
       "  Percent Hispanic Percent Asian Average Score (SAT Math)  \\\n",
       "0              NaN           NaN                      NaN   \n",
       "1            67.8%          4.6%                      NaN   \n",
       "2            18.0%         38.5%                    657.0   \n",
       "3            41.3%          5.9%                    395.0   \n",
       "4            56.9%          8.6%                    418.0   \n",
       "\n",
       "  Average Score (SAT Reading)  Average Score (SAT Writing)  Percent Tested  \n",
       "0                         NaN                          NaN             NaN  \n",
       "1                         NaN                          NaN             NaN  \n",
       "2                       601.0                        601.0           91.0%  \n",
       "3                       411.0                        387.0           78.9%  \n",
       "4                       428.0                        415.0           65.1%  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "%matplotlib inline\n",
    "\n",
    "data = pd.read_csv(\"scores.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Brooklyn         121\n",
       "Bronx            118\n",
       "Manhattan        106\n",
       "Queens            80\n",
       "Staten Island     10\n",
       "Name: Borough, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Borough'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['total_score'] = data['Average Score (SAT Reading)'] +  \\\n",
    "                      data['Average Score (SAT Math)']    +  \\\n",
    "                      data['Average Score (SAT Writing)']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[['Borough', 'total_score']].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = ['Brooklyn', 'Bronx', 'Manhattan', 'Queens', 'Staten Island']\n",
    "district_dict = {}\n",
    "\n",
    "# Create df with total_score for each district\n",
    "for district in x:\n",
    "    district_dict[district] = data[data['Borough'] == district]['total_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Brooklyn_std : 154.8684270520867\n",
      "Bronx_std : 150.39390071890665\n",
      "Manhattan_std : 230.29413953637814\n",
      "Queens_std : 195.25289850192115\n",
      "Staten Island_std : 222.30359621222706\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = []\n",
    "yerror = []\n",
    "\n",
    "# Assigns the mean score and 95% confidence limit to each district\n",
    "for district in x:\n",
    "    y.append(district_dict[district].mean())\n",
    "    yerror.append(1.96*district_dict[district].std()/np.sqrt(district_dict[district].shape[0]))    \n",
    "    print(district + '_std : {}'.format(district_dict[district].std()))\n",
    "\n",
    "sns.set(font_scale=1.8)\n",
    "fig = plt.figure(figsize=(10,5))\n",
    "ax = sns.barplot(x, y, yerr=yerror)\n",
    "ax.set_ylabel('Average Total SAT Score')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NULL HYPOTHESIS: There are no significant differences between the groups' mean SAT scores.\n",
    "\n",
    "H0:μ1=μ2=μ3=μ4=μ5\n",
    "\n",
    "ALTERNATE HYPOTHESIS: There is a significant difference between the groups' mean SAT scores.\n",
    "\n",
    "H1:μi≠μj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=12.733085029201668, pvalue=1.0161974965566023e-09)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "\n",
    "stats.f_oneway(\n",
    "    district_dict['Brooklyn'], district_dict['Bronx'], \\\n",
    "    district_dict['Manhattan'], district_dict['Queens'], \\\n",
    "    district_dict['Staten Island']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting pvalue was less than 0.05. We can reject the null hypothesis \n",
    "\n",
    "and conclude that there is a significant difference between the SAT scores for each district. \n",
    "\n",
    "\n",
    "Even though we've obtained a very low p-value, we cannot make any assumptions about the magnitude of the effect.\n",
    "\n",
    "We can calculate the magnitude of the effect to determine how large the difference is. \n",
    "\n",
    "One of the measures we can use is Eta-squared.\n",
    "\n",
    "The general rules of thumb given by Cohen and Miles & Shevlin (2001) for analyzing eta-squared, η2:\n",
    "\n",
    "Small effect: 0.01\n",
    "Medium ffect: 0.06\n",
    "Large effect: 0.14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.5.0"
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 },
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}