Other Projects and Exercises/Statistics - Racial Discrimination/sliderule_dsi_inferential_statistics_exercise_2.ipynb

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    "# Examining Racial Discrimination in the US Job Market\n",
    "\n",
    "### Background\n",
    "Racial discrimination continues to be pervasive in cultures throughout the world. Researchers examined the level of racial discrimination in the United States labor market by randomly assigning identical résumés to black-sounding or white-sounding names and observing the impact on requests for interviews from employers.\n",
    "\n",
    "### Data\n",
    "In the dataset provided, each row represents a resume. The 'race' column has two values, 'b' and 'w', indicating black-sounding and white-sounding. The column 'call' has two values, 1 and 0, indicating whether the resume received a call from employers or not.\n",
    "\n",
    "Note that the 'b' and 'w' values in race are assigned randomly to the resumes when presented to the employer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercises\n",
    "You will perform a statistical analysis to establish whether race has a significant impact on the rate of callbacks for resumes.\n",
    "\n",
    "Answer the following questions **in this notebook below and submit to your Github account**. \n",
    "\n",
    "   1. What test is appropriate for this problem? Does CLT apply?\n",
    "   2. What are the null and alternate hypotheses?\n",
    "   3. Compute margin of error, confidence interval, and p-value.\n",
    "   4. Write a story describing the statistical significance in the context or the original problem.\n",
    "   5. Does your analysis mean that race/name is the most important factor in callback success? Why or why not? If not, how would you amend your analysis?\n",
    "\n",
    "You can include written notes in notebook cells using Markdown: \n",
    "   - In the control panel at the top, choose Cell > Cell Type > Markdown\n",
    "   - Markdown syntax: http://nestacms.com/docs/creating-content/markdown-cheat-sheet\n",
    "\n",
    "\n",
    "#### Resources\n",
    "+ Experiment information and data source: http://www.povertyactionlab.org/evaluation/discrimination-job-market-united-states\n",
    "+ Scipy statistical methods: http://docs.scipy.org/doc/scipy/reference/stats.html \n",
    "+ Markdown syntax: http://nestacms.com/docs/creating-content/markdown-cheat-sheet\n",
    "****"
   ]
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.io.stata.read_stata('data/us_job_market_discrimination.dta')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "157.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of callbacks for black-sounding names\n",
    "sum(data[data.race=='b'].call)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  id ad  education  ofjobs  yearsexp  honors  volunteer  military  empholes  \\\n",
       "0  b  1          4       2         6       0          0         0         1   \n",
       "1  b  1          3       3         6       0          1         1         0   \n",
       "2  b  1          4       1         6       0          0         0         0   \n",
       "3  b  1          3       4         6       0          1         0         1   \n",
       "4  b  1          3       3        22       0          0         0         0   \n",
       "\n",
       "   occupspecific    ...      compreq  orgreq  manuf  transcom  bankreal trade  \\\n",
       "0             17    ...          1.0     0.0    1.0       0.0       0.0   0.0   \n",
       "1            316    ...          1.0     0.0    1.0       0.0       0.0   0.0   \n",
       "2             19    ...          1.0     0.0    1.0       0.0       0.0   0.0   \n",
       "3            313    ...          1.0     0.0    1.0       0.0       0.0   0.0   \n",
       "4            313    ...          1.0     1.0    0.0       0.0       0.0   0.0   \n",
       "\n",
       "  busservice othservice  missind  ownership  \n",
       "0        0.0        0.0      0.0             \n",
       "1        0.0        0.0      0.0             \n",
       "2        0.0        0.0      0.0             \n",
       "3        0.0        0.0      0.0             \n",
       "4        0.0        1.0      0.0  Nonprofit  \n",
       "\n",
       "[5 rows x 65 columns]"
      ]
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   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "235.0 of 2435 were called back, or 0.0965092402464 %\n",
      "235.0 of 2435 were called back, or 0.064476386037 %\n"
     ]
    }
   ],
   "source": [
    "white = data[data[\"race\"] == \"w\"][\"call\"]\n",
    "black = data[data[\"race\"] == \"b\"][\"call\"]\n",
    "\n",
    "print(white.sum(), \"of\", len(white), \"were called back, or\", white.sum()/len(white), \"%\")\n",
    "print(white.sum(), \"of\", len(black), \"were called back, or\", black.sum()/len(black), \"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/qpb0j08DLqyqxcCF/WNJ0pDMRuvmKOCcfvoc4OhZ2IckaZKmG/QFfDPJqiTL+rFdquoW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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b56c01ef98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.groupby(\"race\").sum()[\"call\"].plot(kind=\"bar\")\n",
    "plt.title(\"Number of callbacks by Race\")\n",
    "plt.xlabel(\"race\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. What test is appropriate for this problem? Does CLT apply?\n",
    "\n",
    "The goal is to discern whether the observed difference between the proportions of resume-callbacks generated by resumes of white and black sounding names is statistically significant. A Chi-quared goodness of fit test can be used to evaluate the null hypothesis that the callback probabilities are the same across race-names. The Chi-squared test is a derivation of the CLT, and thus must meet the following criteria:\n",
    "\n",
    " - Simple Random Sample\n",
    " - Independence\n",
    " - Sample size\n",
    " \n",
    "Because the names are randomly assigned, each resume is independently evaluated by employers, and the saple size is greater than 30, the CLT conditions are met and the CLT does apply. In a Chi-Squared test, a Chi-Squared statistic is compared to the chi-squared distribution for the relevant degrees of freedom. From this, a P value is calculated.\n",
    "\n",
    "## 2. What are the null and alternate hypotheses?\n",
    "\n",
    "Null Hypothesis: There is no significant difference between proportions of callbacks between white and black-sounding names\n",
    "\n",
    "Alternative Hypothesis: There is significant difference between proportions of callbacks between white and black-sounding names\n",
    "\n",
    "Significance: 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#organize the sumamry data\n",
    "df = pd.DataFrame(data={'white': [white.count()-white.sum(), white.sum()], 'black': [black.count()-black.sum(), black.sum()]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>black</th>\n",
       "      <th>white</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2278.0</td>\n",
       "      <td>2200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>157.0</td>\n",
       "      <td>235.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    black   white\n",
       "0  2278.0  2200.0\n",
       "1   157.0   235.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28.654816247582204\n"
     ]
    }
   ],
   "source": [
    "#prepare values for chi-squared_Stat calculation\n",
    "observed = df[\"black\"]\n",
    "expected_ratio = df[\"white\"]/len(white)\n",
    "expected = expected_ratio * len(black)\n",
    "\n",
    "chi_squared_stat = (((observed - expected)**2) / expected).sum()\n",
    "print(chi_squared_stat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Power_divergenceResult(statistic=28.654816247582204, pvalue=8.6498591362764807e-08)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.chisquare(f_obs= observed, f_exp= expected)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Compute margin of error, confidence interval, and p-value.\n",
    "\n",
    "A T-Test would give these values, but instead I've used a Chi-Squared test. The P-Value here is 8.64e-08\n",
    "\n",
    "## 4. Write a story describing the statistical significance in the context or the original problem.\n",
    "\n",
    "The Chi-squared test provides a chi-squared value of 28. That's very large. A P value of 8.64e-08 is also given. That's very small. Much smaller than the 0.05 significance required to retain the null hypothesis. Thus, the null hypothesis is rejected. To illustrate just how this test works, a graph is included below. The area under the blue curve to the right of the vertical red line represents the likelihood of observing the given data if the null hypothesis were true. Pretty slim!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7pqamkpub2+bXFRERkc6r6zw9DaDPT2HPm1BRCv5BAAT5e2ulkYiISBewdOlS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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b56d44bef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = 1\n",
    "\n",
    "x = np.linspace(stats.chi2.ppf(0.01, df), stats.chi2.ppf(0.999999, df), 300)\n",
    "\n",
    "plt.figure(figsize=(20,10))\n",
    "\n",
    "plt.plot(x, stats.chi2.pdf(x, df), label=\"1 degree of freedom\")\n",
    "plt.plot(x, stats.chi2.pdf(x, 2), label=\"2 degrees of freedom\")\n",
    "plt.plot(x, stats.chi2.pdf(x, 3), label=\"3 degrees of freedom\")\n",
    "plt.plot(x, stats.chi2.pdf(x, 4), label=\"4 degrees of freedom\")\n",
    "plt.plot(x, stats.chi2.pdf(x, 5), label=\"5 degrees of freedom\")\n",
    "\n",
    "plt.vlines(ymin=0, ymax=4, x=chi_squared_stat, color=\"red\")\n",
    "\n",
    "plt.ylim(0, 0.45)\n",
    "plt.xlim(0, 30)\n",
    "\n",
    "plt.legend(loc=\"upper center\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Does your analysis mean that race/name is the most important factor in callback success? Why or why not? If not, how would you amend your analysis?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A conclusion that race is a significant factor does not imply that race is the most significant factor. Other quantifiable data is included in the sample such as years of experience, gender, and military experience, among others, which would warrant further examination. \n",
    "\n",
    "To answer the question of which factor is most important, I would repeat this analysis for each other factor to determine which exhibited the lowest likelihood of occurring under assumption of normal distribution of outcomes, in other words, the highest chi-squared value. \n",
    "\n",
    "However, this would not account for the interplay of combined factors. Does military experience boost the likelihood of a callback for women more than men? Great question! More advanced analysis would be needed. Tune in next time."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}