Data_observation03-05.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cbcbaa24",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/mm/sy9415ls5x5b769gx9tfj8ph0000gn/T/ipykernel_41205/1457097889.py:13: DtypeWarning: Columns (5,6) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  df = pd.read_csv('combineddata.tsv',sep='\\t')\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn.cluster import AgglomerativeClustering, KMeans\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "import time\n",
    "import statsmodels.formula.api as smf\n",
    "import statsmodels.api as sm\n",
    "import scipy.stats as stats\n",
    "\n",
    "df = pd.read_csv('combineddata.tsv',sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74a1024b",
   "metadata": {},
   "source": [
    "## <span style='color:Blue'>Import the dataset</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1d0b8ff5",
   "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>tconst</th>\n",
       "      <th>language</th>\n",
       "      <th>title type</th>\n",
       "      <th>primary title</th>\n",
       "      <th>original title</th>\n",
       "      <th>isAdult</th>\n",
       "      <th>start year</th>\n",
       "      <th>end year</th>\n",
       "      <th>runtime minutes</th>\n",
       "      <th>genres</th>\n",
       "      <th>averageRating</th>\n",
       "      <th>numVotes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0123368</td>\n",
       "      <td>Czech</td>\n",
       "      <td>movie</td>\n",
       "      <td>...and the Fifth Horseman Is Fear</td>\n",
       "      <td>...a p√°t√Ω jezdec je Strach</td>\n",
       "      <td>0</td>\n",
       "      <td>1965</td>\n",
       "      <td>\\N</td>\n",
       "      <td>100</td>\n",
       "      <td>Drama,War</td>\n",
       "      <td>7.2</td>\n",
       "      <td>594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0123370</td>\n",
       "      <td>Hindi</td>\n",
       "      <td>movie</td>\n",
       "      <td>Return of Robin Hood</td>\n",
       "      <td>Aaj Ka Robin Hood</td>\n",
       "      <td>0</td>\n",
       "      <td>1988</td>\n",
       "      <td>\\N</td>\n",
       "      <td>80</td>\n",
       "      <td>Drama</td>\n",
       "      <td>7.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0123371</td>\n",
       "      <td>English</td>\n",
       "      <td>movie</td>\n",
       "      <td>Aberration</td>\n",
       "      <td>Aberration</td>\n",
       "      <td>0</td>\n",
       "      <td>1997</td>\n",
       "      <td>\\N</td>\n",
       "      <td>93</td>\n",
       "      <td>Horror,Sci-Fi</td>\n",
       "      <td>4.6</td>\n",
       "      <td>998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0123373</td>\n",
       "      <td>Danish</td>\n",
       "      <td>movie</td>\n",
       "      <td>Evening Land</td>\n",
       "      <td>Aftenlandet</td>\n",
       "      <td>0</td>\n",
       "      <td>1977</td>\n",
       "      <td>\\N</td>\n",
       "      <td>109</td>\n",
       "      <td>Drama,Sci-Fi</td>\n",
       "      <td>7.2</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0123376</td>\n",
       "      <td>English</td>\n",
       "      <td>movie</td>\n",
       "      <td>Alegría</td>\n",
       "      <td>Alegr√≠a</td>\n",
       "      <td>0</td>\n",
       "      <td>1999</td>\n",
       "      <td>\\N</td>\n",
       "      <td>94</td>\n",
       "      <td>Drama,Fantasy,Romance</td>\n",
       "      <td>6.5</td>\n",
       "      <td>565</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tconst language title type                      primary title  \\\n",
       "0  tt0123368    Czech      movie  ...and the Fifth Horseman Is Fear   \n",
       "1  tt0123370    Hindi      movie               Return of Robin Hood   \n",
       "2  tt0123371  English      movie                         Aberration   \n",
       "3  tt0123373   Danish      movie                       Evening Land   \n",
       "4  tt0123376  English      movie                            Alegría   \n",
       "\n",
       "                 original title isAdult start year end year  runtime minutes  \\\n",
       "0  ...a p√°t√Ω jezdec je Strach       0       1965       \\N              100   \n",
       "1             Aaj Ka Robin Hood       0       1988       \\N               80   \n",
       "2                    Aberration       0       1997       \\N               93   \n",
       "3                   Aftenlandet       0       1977       \\N              109   \n",
       "4                      Alegr√≠a       0       1999       \\N               94   \n",
       "\n",
       "                  genres  averageRating  numVotes  \n",
       "0              Drama,War            7.2       594  \n",
       "1                  Drama            7.0        30  \n",
       "2          Horror,Sci-Fi            4.6       998  \n",
       "3           Drama,Sci-Fi            7.2       135  \n",
       "4  Drama,Fantasy,Romance            6.5       565  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Preview the dataset\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11a40a79",
   "metadata": {},
   "source": [
    "## <span style='color:Blue'>Try to find correlation of continuous variables</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "742f4810",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Preview the correlation matrix\n",
    "corrMatrix = df.corr()\n",
    "\n",
    "corr = np.corrcoef(np.random.randn(10, 200))\n",
    "mask = np.zeros_like(corrMatrix)\n",
    "mask[np.triu_indices_from(mask)] = True\n",
    "with sns.axes_style(\"white\"):\n",
    "    f, ax = plt.subplots(figsize=(10,6))\n",
    "    ax = sns.heatmap(corrMatrix, mask=mask, vmax=1, vmin=-1,annot=True, center=0, \n",
    "                     square=True, cmap=sns.diverging_palette(230, 22, s=100, as_cmap=True), \n",
    "                     annot_kws={\"fontsize\":10})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a74b0255",
   "metadata": {},
   "source": [
    "## <span style='color:Blue'>Between Number of Votes and Average Rating might see the correlation a bit</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ec78b3f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Add English or Non English Column\n",
    "df.loc[df['language']=='English', 'engVsNot'] = 'English'\n",
    "df.loc[df['language']!='English', 'engVsNot'] = 'NonEnglish'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "02e4c4f0",
   "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>tconst</th>\n",
       "      <th>language</th>\n",
       "      <th>title type</th>\n",
       "      <th>primary title</th>\n",
       "      <th>original title</th>\n",
       "      <th>isAdult</th>\n",
       "      <th>start year</th>\n",
       "      <th>end year</th>\n",
       "      <th>runtime minutes</th>\n",
       "      <th>genres</th>\n",
       "      <th>averageRating</th>\n",
       "      <th>numVotes</th>\n",
       "      <th>engVsNot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0123368</td>\n",
       "      <td>Czech</td>\n",
       "      <td>movie</td>\n",
       "      <td>...and the Fifth Horseman Is Fear</td>\n",
       "      <td>...a p√°t√Ω jezdec je Strach</td>\n",
       "      <td>0</td>\n",
       "      <td>1965</td>\n",
       "      <td>\\N</td>\n",
       "      <td>100</td>\n",
       "      <td>Drama,War</td>\n",
       "      <td>7.2</td>\n",
       "      <td>594</td>\n",
       "      <td>NonEnglish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0123370</td>\n",
       "      <td>Hindi</td>\n",
       "      <td>movie</td>\n",
       "      <td>Return of Robin Hood</td>\n",
       "      <td>Aaj Ka Robin Hood</td>\n",
       "      <td>0</td>\n",
       "      <td>1988</td>\n",
       "      <td>\\N</td>\n",
       "      <td>80</td>\n",
       "      <td>Drama</td>\n",
       "      <td>7.0</td>\n",
       "      <td>30</td>\n",
       "      <td>NonEnglish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0123371</td>\n",
       "      <td>English</td>\n",
       "      <td>movie</td>\n",
       "      <td>Aberration</td>\n",
       "      <td>Aberration</td>\n",
       "      <td>0</td>\n",
       "      <td>1997</td>\n",
       "      <td>\\N</td>\n",
       "      <td>93</td>\n",
       "      <td>Horror,Sci-Fi</td>\n",
       "      <td>4.6</td>\n",
       "      <td>998</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0123373</td>\n",
       "      <td>Danish</td>\n",
       "      <td>movie</td>\n",
       "      <td>Evening Land</td>\n",
       "      <td>Aftenlandet</td>\n",
       "      <td>0</td>\n",
       "      <td>1977</td>\n",
       "      <td>\\N</td>\n",
       "      <td>109</td>\n",
       "      <td>Drama,Sci-Fi</td>\n",
       "      <td>7.2</td>\n",
       "      <td>135</td>\n",
       "      <td>NonEnglish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0123376</td>\n",
       "      <td>English</td>\n",
       "      <td>movie</td>\n",
       "      <td>Alegría</td>\n",
       "      <td>Alegr√≠a</td>\n",
       "      <td>0</td>\n",
       "      <td>1999</td>\n",
       "      <td>\\N</td>\n",
       "      <td>94</td>\n",
       "      <td>Drama,Fantasy,Romance</td>\n",
       "      <td>6.5</td>\n",
       "      <td>565</td>\n",
       "      <td>English</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      tconst language title type                      primary title  \\\n",
       "0  tt0123368    Czech      movie  ...and the Fifth Horseman Is Fear   \n",
       "1  tt0123370    Hindi      movie               Return of Robin Hood   \n",
       "2  tt0123371  English      movie                         Aberration   \n",
       "3  tt0123373   Danish      movie                       Evening Land   \n",
       "4  tt0123376  English      movie                            Alegría   \n",
       "\n",
       "                 original title isAdult start year end year  runtime minutes  \\\n",
       "0  ...a p√°t√Ω jezdec je Strach       0       1965       \\N              100   \n",
       "1             Aaj Ka Robin Hood       0       1988       \\N               80   \n",
       "2                    Aberration       0       1997       \\N               93   \n",
       "3                   Aftenlandet       0       1977       \\N              109   \n",
       "4                      Alegr√≠a       0       1999       \\N               94   \n",
       "\n",
       "                  genres  averageRating  numVotes    engVsNot  \n",
       "0              Drama,War            7.2       594  NonEnglish  \n",
       "1                  Drama            7.0        30  NonEnglish  \n",
       "2          Horror,Sci-Fi            4.6       998     English  \n",
       "3           Drama,Sci-Fi            7.2       135  NonEnglish  \n",
       "4  Drama,Fantasy,Romance            6.5       565     English  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Preview the dataset\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aacc6d60",
   "metadata": {},
   "source": [
    "## <span style='color:green'>1. Do non-English films have fewer reviews and ratings, regardless of their average rating?</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fd8a3560",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:               numVotes   R-squared:                       0.009\n",
      "Model:                            OLS   Adj. R-squared:                  0.009\n",
      "Method:                 Least Squares   F-statistic:                     2122.\n",
      "Date:                Wed, 19 Apr 2023   Prob (F-statistic):               0.00\n",
      "Time:                        15:48:47   Log-Likelihood:            -2.9018e+06\n",
      "No. Observations:              242550   AIC:                         5.804e+06\n",
      "Df Residuals:                  242548   BIC:                         5.804e+06\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==========================================================================================\n",
      "                             coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------------\n",
      "Intercept               8133.0676    114.090     71.287      0.000    7909.455    8356.680\n",
      "engVsNot[T.NonEnglish] -7131.0226    154.793    -46.068      0.000   -7434.412   -6827.633\n",
      "==============================================================================\n",
      "Omnibus:                   577713.315   Durbin-Watson:                   1.906\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):       8531798618.994\n",
      "Skew:                          24.341   Prob(JB):                         0.00\n",
      "Kurtosis:                     920.518   Cond. No.                         2.73\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "model1 = smf.ols(formula='numVotes ~ engVsNot', data=df).fit()\n",
    "print(model1.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc2192e4",
   "metadata": {},
   "source": [
    "## <span style='color:Blue'>Running a T-test, the Non-English group of movies have significantly less votes (reviews).  On average the Non-English films had 7131 fewer votes than English films (t_{242548} = 46.068, p < 0.001). </span>\n",
    "    \n",
    "\n",
    "## <span style='color:Blue'>This assumes that variance is roughly equivilant, which was not tested for.  Also, number of samples in each group</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "79ee9db9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:          averageRating   R-squared:                       0.033\n",
      "Model:                            OLS   Adj. R-squared:                  0.033\n",
      "Method:                 Least Squares   F-statistic:                     42.84\n",
      "Date:                Wed, 19 Apr 2023   Prob (F-statistic):               0.00\n",
      "Time:                        15:48:58   Log-Likelihood:            -4.0713e+05\n",
      "No. Observations:              242550   AIC:                         8.147e+05\n",
      "Df Residuals:                  242354   BIC:                         8.167e+05\n",
      "Df Model:                         195                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=======================================================================================================\n",
      "                                          coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------------------------------\n",
      "Intercept                               7.4667      0.749      9.971      0.000       5.999       8.934\n",
      "language[T.Afrikaans]                  -0.7782      0.755     -1.031      0.302      -2.257       0.701\n",
      "language[T.Akan]                       -0.7333      1.059     -0.692      0.489      -2.809       1.342\n",
      "language[T.Albanian]                   -0.1909      0.754     -0.253      0.800      -1.670       1.288\n",
      "language[T.American Sign Language]     -1.3667      1.059     -1.291      0.197      -3.442       0.709\n",
      "language[T.Amharic]                    -0.5640      0.779     -0.724      0.469      -2.090       0.962\n",
      "language[T.Arabic]                     -1.0392      0.749     -1.387      0.165      -2.508       0.429\n",
      "language[T.Armenian]                   -1.8461      0.754     -2.447      0.014      -3.324      -0.368\n",
      "language[T.Assamese]                    0.0500      0.767      0.065      0.948      -1.454       1.554\n",
      "language[T.Aymara]                     -0.5417      0.991     -0.547      0.585      -2.483       1.400\n",
      "language[T.Azerbaijani]                -0.2030      0.754     -0.269      0.788      -1.681       1.275\n",
      "language[T.Bable]                       1.1333      1.498      0.757      0.449      -1.802       4.069\n",
      "language[T.Bambara]                    -0.5667      0.801     -0.708      0.479      -2.136       1.002\n",
      "language[T.Bangla]                     -0.4227      0.764     -0.553      0.580      -1.919       1.074\n",
      "language[T.Bashkir]                    -3.3667      1.498     -2.248      0.025      -6.302      -0.431\n",
      "language[T.Basque]                     -0.8371      0.764     -1.095      0.274      -2.335       0.661\n",
      "language[T.Belarusian]                 -0.8101      0.796     -1.018      0.309      -2.371       0.750\n",
      "language[T.Bengali]                    -0.6190      0.750     -0.826      0.409      -2.088       0.850\n",
      "language[T.Berber languages]           -0.4000      1.059     -0.378      0.706      -2.476       1.676\n",
      "language[T.Bhojpuri]                   -0.7275      0.796     -0.914      0.361      -2.288       0.833\n",
      "language[T.Bodo]                        1.0333      1.184      0.873      0.383      -1.287       3.354\n",
      "language[T.Bokmål]                     -0.1000      0.865     -0.116      0.908      -1.795       1.595\n",
      "language[T.Bokm�l]                     -1.5667      0.947     -1.654      0.098      -3.423       0.290\n",
      "language[T.Bosnian]                    -0.7949      0.760     -1.046      0.296      -2.285       0.695\n",
      "language[T.Brazilian Sign Language]     0.4667      1.059      0.441      0.659      -1.609       2.542\n",
      "language[T.Breton]                      0.6333      1.184      0.535      0.593      -1.687       2.954\n",
      "language[T.Bulgarian]                  -0.5718      0.751     -0.762      0.446      -2.043       0.899\n",
      "language[T.Burmese]                    -0.1972      0.779     -0.253      0.800      -1.725       1.330\n",
      "language[T.Cantonese]                  -1.5709      0.749     -2.097      0.036      -3.039      -0.102\n",
      "language[T.Catalan]                    -1.1052      0.754     -1.466      0.143      -2.583       0.373\n",
      "language[T.Central Khmer]              -0.1667      1.059     -0.157      0.875      -2.242       1.909\n",
      "language[T.Chaozhou]                   -0.7667      1.498     -0.512      0.609      -3.702       2.169\n",
      "language[T.Chechen]                    -0.9667      1.184     -0.816      0.414      -3.287       1.354\n",
      "language[T.Chhattisgarhi]               0.9933      0.947      1.049      0.294      -0.863       2.850\n",
      "language[T.Chichewa]                   -0.4333      1.059     -0.409      0.682      -2.509       1.642\n",
      "language[T.Chinese]                    -1.0653      0.750     -1.421      0.155      -2.535       0.404\n",
      "language[T.Corsican]                   -0.6667      1.498     -0.445      0.656      -3.602       2.269\n",
      "language[T.Cree]                       -1.7167      1.184     -1.450      0.147      -4.037       0.604\n",
      "language[T.Croatian]                   -0.7654      0.752     -1.018      0.309      -2.239       0.708\n",
      "language[T.Czech]                      -1.3456      0.749     -1.796      0.073      -2.814       0.123\n",
      "language[T.Danish]                     -1.5445      0.749     -2.061      0.039      -3.013      -0.076\n",
      "language[T.Dari]                       -0.4667      0.947     -0.493      0.622      -2.323       1.390\n",
      "language[T.Divehi]                      0.1583      0.991      0.160      0.873      -1.783       2.100\n",
      "language[T.Dutch]                      -1.0955      0.749     -1.462      0.144      -2.564       0.373\n",
      "language[T.Dzongkha]                   -0.4067      0.854     -0.476      0.634      -2.080       1.267\n",
      "language[T.Eastern Frisian]            -0.1667      1.498     -0.111      0.911      -3.102       2.769\n",
      "language[T.Egyptian (Ancient)]         -1.2667      1.498     -0.846      0.398      -4.202       1.669\n",
      "language[T.English]                    -1.3869      0.749     -1.852      0.064      -2.855       0.081\n",
      "language[T.Esperanto]                  -1.2917      0.991     -1.304      0.192      -3.233       0.650\n",
      "language[T.Estonian]                   -1.0254      0.753     -1.363      0.173      -2.500       0.450\n",
      "language[T.Faorese]                    -0.8667      1.184     -0.732      0.464      -3.187       1.454\n",
      "language[T.Faroese]                    -1.3095      0.895     -1.463      0.143      -3.064       0.445\n",
      "language[T.Fijian]                     -0.4667      1.498     -0.312      0.755      -3.402       2.469\n",
      "language[T.Filipino]                   -0.7667      0.787     -0.975      0.330      -2.308       0.775\n",
      "language[T.Finnish]                    -1.6769      0.749     -2.237      0.025      -3.146      -0.208\n",
      "language[T.Flemish]                    -0.0667      1.498     -0.045      0.964      -3.002       2.869\n",
      "language[T.French]                     -1.2759      0.749     -1.704      0.088      -2.744       0.192\n",
      "language[T.Fulah]                      -0.6417      0.991     -0.648      0.517      -2.583       1.300\n",
      "language[T.Gaelic]                     -0.5667      0.991     -0.572      0.567      -2.508       1.375\n",
      "language[T.Galician]                   -1.1602      0.784     -1.479      0.139      -2.697       0.377\n",
      "language[T.Ganda]                      -1.5667      1.498     -1.046      0.296      -4.502       1.369\n",
      "language[T.Georgian]                   -0.4122      0.752     -0.548      0.583      -1.886       1.061\n",
      "language[T.German]                     -1.1968      0.749     -1.598      0.110      -2.665       0.271\n",
      "language[T.Greek]                      -1.6922      0.749     -2.259      0.024      -3.161      -0.224\n",
      "language[T.Greenlandic]                -0.1667      1.498     -0.111      0.911      -3.102       2.769\n",
      "language[T.Guarani]                    -0.8000      1.059     -0.755      0.450      -2.876       1.276\n",
      "language[T.Gujarati]                   -0.1363      0.756     -0.180      0.857      -1.618       1.345\n",
      "language[T.Haitian]                    -0.3667      0.947     -0.387      0.699      -2.223       1.490\n",
      "language[T.Haitian Creole]             -0.4167      0.878     -0.475      0.635      -2.138       1.304\n",
      "language[T.Haryanvi]                    1.2733      0.947      1.344      0.179      -0.583       3.130\n",
      "language[T.Hausa]                      -0.4556      0.865     -0.527      0.598      -2.150       1.239\n",
      "language[T.Hawaiian]                    1.3333      1.498      0.890      0.373      -1.602       4.269\n",
      "language[T.Hebrew]                     -0.6818      0.750     -0.909      0.363      -2.152       0.788\n",
      "language[T.Hindi]                      -1.4313      0.749     -1.911      0.056      -2.899       0.037\n",
      "language[T.Hokkien]                    -2.4667      1.498     -1.647      0.100      -5.402       0.469\n",
      "language[T.Hungarian]                  -0.8837      0.750     -1.179      0.238      -2.353       0.585\n",
      "language[T.Ibo]                        -2.5667      1.498     -1.714      0.087      -5.502       0.369\n",
      "language[T.Icelandic]                  -0.8379      0.754     -1.111      0.267      -2.316       0.640\n",
      "language[T.Igbo]                        0.0833      1.184      0.070      0.944      -2.237       2.404\n",
      "language[T.Indian Sign Language]       -0.7667      1.498     -0.512      0.609      -3.702       2.169\n",
      "language[T.Indonesian]                 -1.1567      0.750     -1.543      0.123      -2.626       0.313\n",
      "language[T.Inuktitut]                  -0.2542      0.878     -0.289      0.772      -1.975       1.467\n",
      "language[T.Irish]                      -0.1400      0.820     -0.171      0.864      -1.748       1.468\n",
      "language[T.Italian]                    -1.4990      0.749     -2.002      0.045      -2.967      -0.031\n",
      "language[T.Japanese]                   -1.0968      0.749     -1.465      0.143      -2.565       0.371\n",
      "language[T.Javanese]                   -0.6417      0.878     -0.731      0.465      -2.363       1.079\n",
      "language[T.Kabuverdianu]               -1.6667      1.498     -1.113      0.266      -4.602       1.269\n",
      "language[T.Kalaallisut]                -0.8267      0.947     -0.873      0.383      -2.683       1.030\n",
      "language[T.Kannada]                    -0.3965      0.750     -0.529      0.597      -1.867       1.074\n",
      "language[T.Karen]                       1.5333      1.498      1.024      0.306      -1.402       4.469\n",
      "language[T.Kashmiri]                    0.3667      0.917      0.400      0.689      -1.431       2.164\n",
      "language[T.Kazakh]                     -0.8211      0.765     -1.073      0.283      -2.321       0.679\n",
      "language[T.Khmer]                      -0.8814      0.781     -1.128      0.259      -2.412       0.650\n",
      "language[T.Kinyarwanda]                -0.2333      0.865     -0.270      0.787      -1.928       1.461\n",
      "language[T.Kongo]                      -2.1667      1.498     -1.447      0.148      -5.102       0.769\n",
      "language[T.Korean]                     -1.2945      0.749     -1.728      0.084      -2.763       0.174\n",
      "language[T.Kurdish]                    -0.7585      0.762     -0.996      0.319      -2.252       0.734\n",
      "language[T.Kyrgyz]                     -0.6218      0.787     -0.791      0.429      -2.164       0.920\n",
      "language[T.Lao]                        -1.2897      0.831     -1.553      0.121      -2.918       0.338\n",
      "language[T.Latin]                      -1.2758      0.845     -1.510      0.131      -2.931       0.380\n",
      "language[T.Latvian]                    -0.6018      0.753     -0.799      0.424      -2.078       0.874\n",
      "language[T.Limburgish]                 -0.5667      1.498     -0.378      0.705      -3.502       2.369\n",
      "language[T.Lingala]                    -0.8867      0.947     -0.936      0.349      -2.743       0.970\n",
      "language[T.Lithuanian]                 -0.6042      0.754     -0.802      0.423      -2.081       0.873\n",
      "language[T.Luxembourgish]              -0.8400      0.785     -1.070      0.285      -2.379       0.699\n",
      "language[T.Macedonian]                 -0.2988      0.759     -0.394      0.694      -1.786       1.189\n",
      "language[T.Malagasy]                   -0.3500      0.917     -0.382      0.703      -2.147       1.447\n",
      "language[T.Malay]                      -1.5299      0.751     -2.037      0.042      -3.002      -0.058\n",
      "language[T.Malayalam]                  -1.4153      0.749     -1.889      0.059      -2.884       0.053\n",
      "language[T.Maldivian]                   0.6833      0.991      0.690      0.490      -1.258       2.625\n",
      "language[T.Maltese]                    -1.7667      0.812     -2.175      0.030      -3.359      -0.175\n",
      "language[T.Mandarin]                   -1.4940      0.749     -1.995      0.046      -2.962      -0.026\n",
      "language[T.Mandingo]                    0.3333      1.498      0.223      0.824      -2.602       3.269\n",
      "language[T.Manipuri]                   -0.0667      0.991     -0.067      0.946      -2.008       1.875\n",
      "language[T.Maori]                      -0.6667      1.059     -0.630      0.529      -2.742       1.409\n",
      "language[T.Marathi]                    -0.1400      0.751     -0.186      0.852      -1.612       1.332\n",
      "language[T.Marshallese]                -0.1333      1.059     -0.126      0.900      -2.209       1.942\n",
      "language[T.Min Nan]                    -0.1167      1.184     -0.099      0.922      -2.437       2.204\n",
      "language[T.Moiso]                      -1.2333      1.059     -1.165      0.244      -3.309       0.842\n",
      "language[T.Moldovan]                    0.3833      0.991      0.387      0.699      -1.558       2.325\n",
      "language[T.Mongolian]                  -0.0610      0.764     -0.080      0.936      -1.559       1.437\n",
      "language[T.More]                       -0.1333      1.059     -0.126      0.900      -2.209       1.942\n",
      "language[T.Nepali]                     -0.0467      0.760     -0.061      0.951      -1.536       1.443\n",
      "language[T.None]                       -1.5230      0.751     -2.028      0.043      -2.995      -0.051\n",
      "language[T.North Ndebele]              -1.2667      1.498     -0.846      0.398      -4.202       1.669\n",
      "language[T.Northern Sami]               0.0333      0.991      0.034      0.973      -1.908       1.975\n",
      "language[T.Norwegian]                  -1.4350      0.750     -1.914      0.056      -2.905       0.035\n",
      "language[T.Norwegian Bokmål]           -1.3319      0.796     -1.673      0.094      -2.892       0.229\n",
      "language[T.Norwegian Nynorsk]          -1.2667      1.498     -0.846      0.398      -4.202       1.669\n",
      "language[T.Occitan]                    -0.8667      1.498     -0.579      0.563      -3.802       2.069\n",
      "language[T.Odia]                       -0.5624      0.758     -0.741      0.458      -2.049       0.924\n",
      "language[T.Oriya]                       0.2638      0.773      0.341      0.733      -1.251       1.779\n",
      "language[T.Ossetian]                   -3.7667      1.498     -2.515      0.012      -6.702      -0.831\n",
      "language[T.Parsee]                     -0.5667      1.498     -0.378      0.705      -3.502       2.369\n",
      "language[T.Pashto]                     -0.5000      0.865     -0.578      0.563      -2.195       1.195\n",
      "language[T.Persian]                    -1.6734      0.749     -2.233      0.026      -3.142      -0.205\n",
      "language[T.Polish]                     -1.5144      0.749     -2.021      0.043      -2.983      -0.046\n",
      "language[T.Portuguese]                 -0.9791      0.749     -1.307      0.191      -2.447       0.489\n",
      "language[T.Punjabi]                    -0.8806      0.752     -1.171      0.241      -2.354       0.593\n",
      "language[T.Pushto]                     -0.5667      0.917     -0.618      0.537      -2.364       1.231\n",
      "language[T.Quechua]                    -0.5889      0.865     -0.681      0.496      -2.284       1.106\n",
      "language[T.Rajasthani]                 -0.5267      0.947     -0.556      0.578      -2.383       1.330\n",
      "language[T.Rawan]                       0.3333      1.498      0.223      0.824      -2.602       3.269\n",
      "language[T.Romanian]                   -0.5914      0.750     -0.789      0.430      -2.061       0.879\n",
      "language[T.Romansh]                    -1.1167      1.184     -0.943      0.346      -3.437       1.204\n",
      "language[T.Romany]                      0.3833      1.184      0.324      0.746      -1.937       2.704\n",
      "language[T.Russian]                    -1.2823      0.749     -1.712      0.087      -2.750       0.186\n",
      "language[T.Russian Sign Language]      -0.4667      1.498     -0.312      0.755      -3.402       2.469\n",
      "language[T.Samoan]                     -0.6667      1.059     -0.630      0.529      -2.742       1.409\n",
      "language[T.Sanskrit]                    0.8167      0.917      0.890      0.373      -0.981       2.614\n",
      "language[T.Sardinian]                  -0.9667      1.498     -0.645      0.519      -3.902       1.969\n",
      "language[T.Scottish Gaelic]             0.2333      1.498      0.156      0.876      -2.702       3.169\n",
      "language[T.Serbian]                    -0.7451      0.751     -0.992      0.321      -2.217       0.727\n",
      "language[T.Serbo-Croatian]             -0.6129      0.750     -0.817      0.414      -2.084       0.858\n",
      "language[T.Shona]                      -0.0667      1.498     -0.045      0.964      -3.002       2.869\n",
      "language[T.Sign Languages]             -1.4917      0.878     -1.699      0.089      -3.213       0.229\n",
      "language[T.Sindhi]                      2.0333      1.498      1.358      0.175      -0.902       4.969\n",
      "language[T.Sinhala]                    -0.2322      0.762     -0.305      0.760      -1.725       1.261\n",
      "language[T.Sinhalese]                  -0.2051      0.777     -0.264      0.792      -1.728       1.318\n",
      "language[T.Slovak]                     -0.9205      0.752     -1.224      0.221      -2.395       0.554\n",
      "language[T.Slovenian]                  -0.9122      0.752     -1.213      0.225      -2.386       0.562\n",
      "language[T.Somali]                      0.4933      0.947      0.521      0.602      -1.363       2.350\n",
      "language[T.Sotho]                      -0.7667      1.498     -0.512      0.609      -3.702       2.169\n",
      "language[T.Southern Sotho]             -1.1467      0.947     -1.211      0.226      -3.003       0.710\n",
      "language[T.Spanish]                    -1.3357      0.749     -1.784      0.074      -2.803       0.132\n",
      "language[T.Sundanese]                  -0.8667      1.498     -0.579      0.563      -3.802       2.069\n",
      "language[T.Swahili]                    -0.3394      0.782     -0.434      0.664      -1.872       1.193\n",
      "language[T.Swati]                      -3.4667      1.498     -2.315      0.021      -6.402      -0.531\n",
      "language[T.Swedish]                    -1.7714      0.749     -2.364      0.018      -3.240      -0.303\n",
      "language[T.Swiss German]               -0.3010      0.783     -0.384      0.701      -1.836       1.234\n",
      "language[T.Tagalog]                    -0.9858      0.749     -1.315      0.188      -2.455       0.483\n",
      "language[T.Tajik]                      -0.6212      0.845     -0.735      0.462      -2.277       1.035\n",
      "language[T.Tamil]                      -1.1952      0.749     -1.595      0.111      -2.664       0.273\n",
      "language[T.Tatar]                       0.2333      1.498      0.156      0.876      -2.702       3.169\n",
      "language[T.Telugu]                     -0.9495      0.749     -1.267      0.205      -2.418       0.519\n",
      "language[T.Thai]                       -1.5491      0.750     -2.065      0.039      -3.020      -0.079\n",
      "language[T.Tibetan]                    -0.4267      0.785     -0.543      0.587      -1.966       1.113\n",
      "language[T.Tigrinya]                   -2.3167      1.184     -1.957      0.050      -4.637       0.004\n",
      "language[T.Tulu]                        0.1133      0.947      0.120      0.905      -1.743       1.970\n",
      "language[T.Tupi]                       -0.8667      1.498     -0.579      0.563      -3.802       2.069\n",
      "language[T.Turkish]                    -1.8826      0.749     -2.513      0.012      -3.351      -0.414\n",
      "language[T.Turkmen]                    -1.5333      0.917     -1.672      0.095      -3.331       0.264\n",
      "language[T.Twi]                        -2.4667      1.184     -2.083      0.037      -4.787      -0.146\n",
      "language[T.Ukrainian]                  -0.4422      0.752     -0.588      0.556      -1.915       1.031\n",
      "language[T.Urdu]                       -1.0038      0.755     -1.330      0.183      -2.483       0.475\n",
      "language[T.Uyghur]                      0.1333      1.498      0.089      0.929      -2.802       3.069\n",
      "language[T.Uzbek]                      -0.6404      0.778     -0.823      0.410      -2.165       0.884\n",
      "language[T.Vietnamese]                 -1.1037      0.752     -1.467      0.142      -2.578       0.371\n",
      "language[T.Wayuu]                       1.7333      1.498      1.157      0.247      -1.202       4.669\n",
      "language[T.Welsh]                      -0.6292      0.816     -0.771      0.441      -2.228       0.970\n",
      "language[T.Western Frisian]            -0.1917      0.991     -0.193      0.847      -2.133       1.750\n",
      "language[T.Wolof]                      -0.8431      0.812     -1.038      0.299      -2.435       0.749\n",
      "language[T.Xhosa]                      -1.1167      0.991     -1.127      0.260      -3.058       0.825\n",
      "language[T.Yiddish]                    -1.0810      0.788     -1.372      0.170      -2.625       0.463\n",
      "language[T.Yoruba]                  -1.107e-11      0.917  -1.21e-11      1.000      -1.797       1.797\n",
      "language[T.Zulu]                       -1.2784      0.812     -1.574      0.115      -2.870       0.313\n",
      "==============================================================================\n",
      "Omnibus:                     7716.099   Durbin-Watson:                   1.902\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             8727.219\n",
      "Skew:                          -0.422   Prob(JB):                         0.00\n",
      "Kurtosis:                       3.390   Cond. No.                     4.49e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 4.49e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "model3 = smf.ols(formula='averageRating ~ language', data=df).fit()\n",
    "print(model3.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62a8be37",
   "metadata": {},
   "source": [
    "## <span style='color:Blue'>Running an ANOVA for average rating by languages, there are significant differences between the averages.  (F = 42.84, p<0.01) </span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36cc09f9",
   "metadata": {},
   "source": [
    "## <span style='color:green'>3. Do certain languages do better when it comes to ratings/reviews overall?  </span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a62008cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:          averageRating   R-squared:                       0.002\n",
      "Model:                            OLS   Adj. R-squared:                  0.002\n",
      "Method:                 Least Squares   F-statistic:                     555.3\n",
      "Date:                Wed, 19 Apr 2023   Prob (F-statistic):          1.20e-122\n",
      "Time:                        15:48:59   Log-Likelihood:            -4.1097e+05\n",
      "No. Observations:              242550   AIC:                         8.219e+05\n",
      "Df Residuals:                  242548   BIC:                         8.220e+05\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==========================================================================================\n",
      "                             coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------------\n",
      "Intercept                  6.0798      0.004   1536.447      0.000       6.072       6.088\n",
      "engVsNot[T.NonEnglish]     0.1265      0.005     23.565      0.000       0.116       0.137\n",
      "==============================================================================\n",
      "Omnibus:                     7066.219   Durbin-Watson:                   1.892\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             7823.080\n",
      "Skew:                          -0.411   Prob(JB):                         0.00\n",
      "Kurtosis:                       3.315   Cond. No.                         2.73\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "model3b = smf.ols(formula='averageRating ~ engVsNot', data=df).fit()\n",
    "print(model3b.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c2c697d",
   "metadata": {},
   "source": [
    "## <span style='color:Blue'>NonEnglish movie rating on average (about 6.2) are larger than the average rating on English movies (6.0798). Looking at a standard error of 0.005, T value of 23.565, and P value less than 0.001. It is significant, but may not be practically significant. </span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c5e6074",
   "metadata": {},
   "source": [
    "## <span style='color:green'>5. Which films are “underrated gems,” that have high ratings, but relatively low view counts? </span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0712ff31",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:          averageRating   R-squared:                       0.005\n",
      "Model:                            OLS   Adj. R-squared:                  0.005\n",
      "Method:                 Least Squares   F-statistic:                     1337.\n",
      "Date:                Wed, 19 Apr 2023   Prob (F-statistic):          6.09e-292\n",
      "Time:                        15:48:59   Log-Likelihood:            -4.1058e+05\n",
      "No. Observations:              242550   AIC:                         8.212e+05\n",
      "Df Residuals:                  242548   BIC:                         8.212e+05\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      6.1376      0.003   2284.499      0.000       6.132       6.143\n",
      "numVotes     2.56e-06      7e-08     36.567      0.000    2.42e-06     2.7e-06\n",
      "==============================================================================\n",
      "Omnibus:                     7641.329   Durbin-Watson:                   1.889\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             8529.810\n",
      "Skew:                          -0.428   Prob(JB):                         0.00\n",
      "Kurtosis:                       3.336   Cond. No.                     3.86e+04\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 3.86e+04. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n",
      "Mean of num of Votes 4259.206810966811\n",
      "Mean of Average Rating 6.148526489383678\n",
      "SD of num of Votes 38139.98937516637\n",
      "SD of Average Rating 1.318592461153622\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model5 = smf.ols(formula='averageRating ~ numVotes', data=df).fit()\n",
    "print(model5.summary())\n",
    "\n",
    "plt.plot(df.numVotes, df.averageRating, 'bo', alpha = 0.05);\n",
    "plt.xlabel('# votes')\n",
    "plt.ylabel('Avg rating');\n",
    "\n",
    "a, b = model5.params\n",
    "\n",
    "ratingStd = np.std(df.averageRating)\n",
    "\n",
    "# Create sequence \n",
    "xseq = np.linspace(0, 1700000, num=10)\n",
    "xsequ = np.linspace(0, 700000, num=10)\n",
    "xseql = np.linspace(0, 2700000, num=10)\n",
    "\n",
    "# Plot regression line\n",
    "plt.plot(xseq, a + b * xseq, color=\"k\", lw=2.5);\n",
    "plt.plot(xsequ, a + b * xsequ + 2.5 * ratingStd, color=\"r\", lw=1, ls=\"dashed\");\n",
    "plt.plot(xseql, a + b * xseql - 2.5 *ratingStd, color=\"r\", lw=1, ls = \"dashed\");\n",
    "\n",
    "print(\"Mean of num of Votes\", np.mean(df.numVotes))\n",
    "print(\"Mean of Average Rating\", np.mean(df.averageRating))\n",
    "print(\"SD of num of Votes\", np.std(df.numVotes))\n",
    "print(\"SD of Average Rating\", np.std(df.averageRating))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49a72dff",
   "metadata": {},
   "source": [
    "## <span style='color:Blue'>The average rating distribution is roughly similarly distributed in the chart between the average rating and the number of votes?? </span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2e5531d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = [2, 6]\n",
    "plt.rcParams[\"figure.autolayout\"] = True\n",
    "y, binEdges = np.histogram(df.averageRating, bins=30)\n",
    "bincenters = 0.5 * (binEdges[1:] + binEdges[:-1])\n",
    "plt.plot(y, bincenters, 'b-', lw=2);\n",
    "plt.ylabel('Avg rating');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b6304c5e",
   "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>tconst</th>\n",
       "      <th>language</th>\n",
       "      <th>title type</th>\n",
       "      <th>primary title</th>\n",
       "      <th>original title</th>\n",
       "      <th>isAdult</th>\n",
       "      <th>start year</th>\n",
       "      <th>end year</th>\n",
       "      <th>runtime minutes</th>\n",
       "      <th>genres</th>\n",
       "      <th>averageRating</th>\n",
       "      <th>numVotes</th>\n",
       "      <th>engVsNot</th>\n",
       "      <th>hiddenJems</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18526</th>\n",
       "      <td>tt0222023</td>\n",
       "      <td>Spanish</td>\n",
       "      <td>movie</td>\n",
       "      <td>Long Sleepless Nights</td>\n",
       "      <td>Hubo un tiempo en que los sue√±os dieron paso ...</td>\n",
       "      <td>0</td>\n",
       "      <td>2000</td>\n",
       "      <td>\\N</td>\n",
       "      <td>50</td>\n",
       "      <td>Drama</td>\n",
       "      <td>9.7</td>\n",
       "      <td>10</td>\n",
       "      <td>NonEnglish</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24485</th>\n",
       "      <td>tt0262389</td>\n",
       "      <td>Telugu</td>\n",
       "      <td>movie</td>\n",
       "      <td>Edadugula Bandham</td>\n",
       "      <td>Edadugula Bandham</td>\n",
       "      <td>0</td>\n",
       "      <td>1985</td>\n",
       "      <td>\\N</td>\n",
       "      <td>142</td>\n",
       "      <td>Drama</td>\n",
       "      <td>9.7</td>\n",
       "      <td>136</td>\n",
       "      <td>NonEnglish</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26389</th>\n",
       "      <td>tt0274494</td>\n",
       "      <td>Italian</td>\n",
       "      <td>movie</td>\n",
       "      <td>Faber</td>\n",
       "      <td>Faber</td>\n",
       "      <td>0</td>\n",
       "      <td>2001</td>\n",
       "      <td>\\N</td>\n",
       "      <td>45</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>9.8</td>\n",
       "      <td>9</td>\n",
       "      <td>NonEnglish</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26802</th>\n",
       "      <td>tt0276876</td>\n",
       "      <td>English</td>\n",
       "      <td>movie</td>\n",
       "      <td>Against Reason and by Force</td>\n",
       "      <td>Contra la razon y por la fuerza</td>\n",
       "      <td>0</td>\n",
       "      <td>1974</td>\n",
       "      <td>\\N</td>\n",
       "      <td>50</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>9.6</td>\n",
       "      <td>5</td>\n",
       "      <td>English</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44771</th>\n",
       "      <td>tt0444194</td>\n",
       "      <td>Tagalog</td>\n",
       "      <td>movie</td>\n",
       "      <td>Isa para sa lahat, lahat para sa isa</td>\n",
       "      <td>Isa para sa lahat, lahat para sa isa</td>\n",
       "      <td>0</td>\n",
       "      <td>1979</td>\n",
       "      <td>\\N</td>\n",
       "      <td>117</td>\n",
       "      <td>Action</td>\n",
       "      <td>9.7</td>\n",
       "      <td>10</td>\n",
       "      <td>NonEnglish</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47241</th>\n",
       "      <td>tt0473205</td>\n",
       "      <td>English</td>\n",
       "      <td>movie</td>\n",
       "      <td>Forbidden Forest</td>\n",
       "      <td>Forbidden Forest</td>\n",
       "      <td>0</td>\n",
       "      <td>2004</td>\n",
       "      <td>\\N</td>\n",
       "      <td>70</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>9.5</td>\n",
       "      <td>9</td>\n",
       "      <td>English</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47701</th>\n",
       "      <td>tt0478554</td>\n",
       "      <td>Tagalog</td>\n",
       "      <td>movie</td>\n",
       "      <td>Angelito San Miguel at Ang Mga Batang City Jail</td>\n",
       "      <td>Angelito San Miguel at Ang Mga Batang City Jail</td>\n",
       "      <td>0</td>\n",
       "      <td>1991</td>\n",
       "      <td>\\N</td>\n",
       "      <td>107</td>\n",
       "      <td>Action,Crime,Drama</td>\n",
       "      <td>9.8</td>\n",
       "      <td>13</td>\n",
       "      <td>NonEnglish</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54778</th>\n",
       "      <td>tt0997212</td>\n",
       "      <td>English</td>\n",
       "      <td>movie</td>\n",
       "      <td>Cafe Coexistence</td>\n",
       "      <td>Cafe Coexistence</td>\n",
       "      <td>0</td>\n",
       "      <td>2009</td>\n",
       "      <td>\\N</td>\n",
       "      <td>68</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "      <td>9.6</td>\n",
       "      <td>13</td>\n",
       "      <td>English</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57213</th>\n",
       "      <td>tt0247655</td>\n",
       "      <td>Hindi</td>\n",
       "      <td>movie</td>\n",
       "      <td>Rangili</td>\n",
       "      <td>Rangili</td>\n",
       "      <td>0</td>\n",
       "      <td>1952</td>\n",
       "      <td>\\N</td>\n",
       "      <td>150</td>\n",
       "      <td>Drama</td>\n",
       "      <td>9.6</td>\n",
       "      <td>8</td>\n",
       "      <td>NonEnglish</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58441</th>\n",
       "      <td>tt0305275</td>\n",
       "      <td>English</td>\n",
       "      <td>movie</td>\n",
       "      <td>Battleship Texas: The Lone Star Ship</td>\n",
       "      <td>Battleship Texas: The Lone Star Ship</td>\n",
       "      <td>0</td>\n",
       "      <td>2001</td>\n",
       "      <td>\\N</td>\n",
       "      <td>46</td>\n",
       "      <td>Documentary,War</td>\n",
       "      <td>9.5</td>\n",
       "      <td>7</td>\n",
       "      <td>English</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          tconst language title type  \\\n",
       "18526  tt0222023  Spanish      movie   \n",
       "24485  tt0262389   Telugu      movie   \n",
       "26389  tt0274494  Italian      movie   \n",
       "26802  tt0276876  English      movie   \n",
       "44771  tt0444194  Tagalog      movie   \n",
       "47241  tt0473205  English      movie   \n",
       "47701  tt0478554  Tagalog      movie   \n",
       "54778  tt0997212  English      movie   \n",
       "57213  tt0247655    Hindi      movie   \n",
       "58441  tt0305275  English      movie   \n",
       "\n",
       "                                         primary title  \\\n",
       "18526                            Long Sleepless Nights   \n",
       "24485                                Edadugula Bandham   \n",
       "26389                                            Faber   \n",
       "26802                      Against Reason and by Force   \n",
       "44771             Isa para sa lahat, lahat para sa isa   \n",
       "47241                                 Forbidden Forest   \n",
       "47701  Angelito San Miguel at Ang Mga Batang City Jail   \n",
       "54778                                 Cafe Coexistence   \n",
       "57213                                          Rangili   \n",
       "58441             Battleship Texas: The Lone Star Ship   \n",
       "\n",
       "                                          original title isAdult start year  \\\n",
       "18526  Hubo un tiempo en que los sue√±os dieron paso ...       0       2000   \n",
       "24485                                  Edadugula Bandham       0       1985   \n",
       "26389                                              Faber       0       2001   \n",
       "26802                    Contra la razon y por la fuerza       0       1974   \n",
       "44771               Isa para sa lahat, lahat para sa isa       0       1979   \n",
       "47241                                   Forbidden Forest       0       2004   \n",
       "47701    Angelito San Miguel at Ang Mga Batang City Jail       0       1991   \n",
       "54778                                   Cafe Coexistence       0       2009   \n",
       "57213                                            Rangili       0       1952   \n",
       "58441               Battleship Texas: The Lone Star Ship       0       2001   \n",
       "\n",
       "      end year  runtime minutes              genres  averageRating  numVotes  \\\n",
       "18526       \\N               50               Drama            9.7        10   \n",
       "24485       \\N              142               Drama            9.7       136   \n",
       "26389       \\N               45         Documentary            9.8         9   \n",
       "26802       \\N               50         Documentary            9.6         5   \n",
       "44771       \\N              117              Action            9.7        10   \n",
       "47241       \\N               70         Documentary            9.5         9   \n",
       "47701       \\N              107  Action,Crime,Drama            9.8        13   \n",
       "54778       \\N               68        Comedy,Drama            9.6        13   \n",
       "57213       \\N              150               Drama            9.6         8   \n",
       "58441       \\N               46     Documentary,War            9.5         7   \n",
       "\n",
       "         engVsNot hiddenJems  \n",
       "18526  NonEnglish       True  \n",
       "24485  NonEnglish       True  \n",
       "26389  NonEnglish       True  \n",
       "26802     English       True  \n",
       "44771  NonEnglish       True  \n",
       "47241     English       True  \n",
       "47701  NonEnglish       True  \n",
       "54778     English       True  \n",
       "57213  NonEnglish       True  \n",
       "58441     English       True  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['averageRating']>a +b * df['numVotes'] + 2.5 * ratingStd, 'hiddenJems'] = True\n",
    "df.loc[df['averageRating']<= a +b * df['numVotes'] + 2.5 * ratingStd, 'hiddenJems'] = False\n",
    "onlyTrue = df[df['hiddenJems']== True]\n",
    "onlyTrue.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07ac736f",
   "metadata": {},
   "source": [
    "## <span style='color:Blue'>Here is a list of the hidden jems. These are defined as the movies that are rated average 2.5 standard deviations above the average when controlling for the number of votes. </span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5753d78",
   "metadata": {},
   "source": [
    "## <span style='color:Blue'>This is not normal data, however.  Perpaps standard linear regression is not ideal method. Maybe Gamma regression.</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f6faf22",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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