STorrico_dsi_inferential_statistics_exercise_2.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "# 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. Try using both the bootstrapping and the frequentist statistical approaches.\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",
    "#### 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",
    "+ Formulas for the Bernoulli distribution: https://en.wikipedia.org/wiki/Bernoulli_distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.mlab as mlab\n",
    "import seaborn as sns\n",
    "import pylab"
   ]
  },
  {
   "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": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "235.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of callbacks for white-sounding names\n",
    "sum(data[data.race=='w'].call)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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>id</th>\n",
       "      <th>ad</th>\n",
       "      <th>education</th>\n",
       "      <th>ofjobs</th>\n",
       "      <th>yearsexp</th>\n",
       "      <th>honors</th>\n",
       "      <th>volunteer</th>\n",
       "      <th>military</th>\n",
       "      <th>empholes</th>\n",
       "      <th>occupspecific</th>\n",
       "      <th>...</th>\n",
       "      <th>compreq</th>\n",
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       "      <th>trade</th>\n",
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       "      <th>othservice</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>b</td>\n",
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       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>316</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>313</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <td>b</td>\n",
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       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Nonprofit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 65 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  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]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<div class=\"span5 alert alert-success\">\n",
    "<p>EXPLORATORY DATA ANALYS</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "w = data[data.race=='w']\n",
    "b = data[data.race=='b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      Callback  No Callback  Total % Success\n",
      "Black-sounding names       157         2278   2435     6.45%\n",
      "White-sounding names       235         2200   2435     9.65%\n",
      "Total Callbacks: 392.0 Total N: 4870\n"
     ]
    }
   ],
   "source": [
    "# Black-sounding names\n",
    "b_yes = np.sum(b.call)\n",
    "b_n1 = len(b)\n",
    "b_p = (b_yes / b_n1)\n",
    "\n",
    "\n",
    "# White-sounding names\n",
    "w_yes = np.sum(w.call)\n",
    "w_n2 = len(w)\n",
    "w_p = (w_yes / w_n2)\n",
    "\n",
    "\n",
    "summary = {'Callback': np.array([b_yes, w_yes]).astype(int),\n",
    "        'No Callback': np.array([b_n1 - b_yes, w_n2 - w_yes]).astype(int),\n",
    "        'Total': np.array([b_n1, w_n2]).astype(int),\n",
    "        '% Success': np.array(['{:.2%}'.format(b_yes/b_n1), '{:.2%}'.format(w_yes/w_n2)])}\n",
    "\n",
    "tb2 = pd.DataFrame(summary, columns = ['Callback', 'No Callback', 'Total', '% Success'], \n",
    "                   index = ['Black-sounding names', 'White-sounding names'])\n",
    "print(tb2)\n",
    "\n",
    "total_yes = b_yes + w_yes\n",
    "total_cases = b_n1 + w_n2\n",
    "\n",
    "print('Total Callbacks: {}'.format(total_yes),\n",
    "      'Total N: {}'.format(total_cases))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ECDF Function\n",
    "\n",
    "def ecdf(data):\n",
    "    \"\"\"Compute ECDF for a one-dimensional array of measurements.\"\"\"\n",
    "\n",
    "    # Number of data points: n\n",
    "    n = len(data)\n",
    "\n",
    "    # x: sort the data\n",
    "    x = np.sort(data)\n",
    "\n",
    "    # y: range for y-axis\n",
    "    y = np.arange(1, n+1) / n\n",
    "\n",
    "    return x, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a15ddb8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "_ = plt.style.use('fivethirtyeight')\n",
    "_ = plt.rcParams[\"figure.figsize\"] = (10, 8)\n",
    "\n",
    "#Plot CDFs of callbacks for black- vs. white-sounding names\n",
    "b_samples = np.random.binomial(b_n1, b_p, size=10000)\n",
    "w_samples = np.random.binomial(w_n2, w_p, size=10000)\n",
    "\n",
    "bx, by = ecdf(b_samples)\n",
    "_ = plt.plot(bx, by, marker='.', linestyle='none')\n",
    "\n",
    "wx, wy = ecdf(w_samples)\n",
    "_ = plt.plot(wx, wy, marker='.', linestyle='none')\n",
    "\n",
    "plt.margins = 0.02\n",
    "_ = plt.xlabel('Number of callbacks')\n",
    "_ = plt.ylabel('CDF')\n",
    "_ = plt.title('Names Variable: Black vs. White')\n",
    "_ = plt.legend(('Black-sounding Names', 'White-sounding Names'), loc='lower right', fontsize='large', markerscale=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"span5 alert alert-success\">\n",
    "<p>Q1: Appropriate Test? Does CLT apply? and Q2 State your Hypothesis</p>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q1.The CLT applies for the Gender and Call variables because the key requirements of having randon, indepedent variables that tend towards a Normal distribution are met as it can be observed in the CDF graph shown above. \n",
    "\n",
    "For this particular situation, the suggested test to use is the Z-test for proportions since each sample observation can result in either a success or a failure, the sample includes at least 10 successes and 10 failures. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True True True True\n"
     ]
    }
   ],
   "source": [
    "r1 = w_n2 * w_p >= 10\n",
    "r2 = b_n1 * b_p >= 10\n",
    "r3 = w_n2 * (1-w_p) >= 10\n",
    "r4 = b_n1 * (1-b_p) >= 10\n",
    "\n",
    "print(r1,r2,r3,r4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q2. My hypothesis will be Ho: p1 = p2 and Ha: p1 - p2 ≠ 0 with alpha=.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z_value:-4.108412152434346\n"
     ]
    }
   ],
   "source": [
    "# Frequentist Approach --> Z test for proportions with Zvalue of 1.96\n",
    "\n",
    "p_total = (b_yes+ w_yes)/(w_n2 + b_n1)\n",
    "\n",
    "q= 1-p_total\n",
    "\n",
    "\n",
    "total_pn = np.sqrt((2*p_total*q)*(1/2435)) # Formula states (1/n1) + (1/n2)  \n",
    "\n",
    "z_value = (b_p - w_p)*(1/total_pn)\n",
    "\n",
    "print('Z_value:{}'.format(z_value))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "95% conf int for N: \t 0.07285185698623672 to 0.08813376929713082\n",
      "95% conf int for only Black: \t 0.05472122800904957 to 0.0742315440648724\n",
      "95% conf int for only White: \t 0.08478045877727557 to 0.10823802171553758\n"
     ]
    }
   ],
   "source": [
    "#95% confidence interval for N\n",
    "\n",
    "z_alpha = 1.96\n",
    "ci_high = p_total + z_alpha*(np.sqrt((p_total*q)*(1/total_cases)))\n",
    "ci_low = p_total - z_alpha*(np.sqrt((p_total*q* 1/total_cases)))\n",
    "\n",
    "#95% confidence interval for Black-sounded names\n",
    "\n",
    "q_b = 1-b_p\n",
    "ci_high_b = b_p + z_alpha*(np.sqrt((b_p*q_b)/b_n1))\n",
    "ci_low_b = b_p - z_alpha*(np.sqrt((b_p*(q_b))/b_n1))\n",
    "\n",
    "#95% confidence interval for White-sounded names\n",
    "\n",
    "q_w = 1-w_p\n",
    "ci_high_w = w_p + z_alpha*(np.sqrt((w_p * q_w)/w_n2))\n",
    "ci_low_w = w_p - z_alpha*(np.sqrt((w_p * q_w)/w_n2))\n",
    "\n",
    "print('95% conf int for N: \\t {} to {}'.format(ci_low, ci_high))\n",
    "print('95% conf int for only Black: \\t {} to {}'.format(ci_low_b, ci_high_b))\n",
    "print('95% conf int for only White: \\t {} to {}'.format(ci_low_w, ci_high_w))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Margin of err(black + white): \t 0.7640956155447047\n",
      "Margin of err(black): \t 0.9755158027911415\n",
      "Margin of err (white): \t 1.1728781469131009\n"
     ]
    }
   ],
   "source": [
    "moe_N = z_alpha*(np.sqrt((p_total*q)*(1/total_cases)))\n",
    "moe_b = z_alpha*(np.sqrt((b_p*q_b)/b_n1))\n",
    "moe_w = z_alpha*(np.sqrt((w_p*q_w)/w_n2))\n",
    "\n",
    "\n",
    "print('Margin of err(black + white): \\t {}'.format(moe_N *100))\n",
    "print('Margin of err(black): \\t {}'.format(moe_b * 100))\n",
    "print('Margin of err (white): \\t {}'.format(moe_w *100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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U3nPPPXr33Xf1+9//XoMHD1ZycrI2b96spUuXasiQIVqwYIHHe7zyyiu67rrr9Oyzz+rjjz/Wr3/9a0VERGjnzp1atmyZ5s+f77PVCADUHKESCEPDhw9Xp06dNG3aNK1YsULLly9XvXr11KRJE/Xr189j7b4LL7xQX3zxhSZOnKgvv/xSK1euVMeOHfXOO+9o//79XkNl27ZttXDhQj399NP6/PPPFRERoa5du+rjjz/Wtm3bvPa2dezYUatWrdL06dP16aefunqsGjdurHbt2mnMmDFeh3Frok+fPpo8ebJmzZqlV155RSdPnlRkZOQ5Q+XEiRO1fPlybdiwQZ9//rnq1KmjtLQ0ZWVlaeTIkYZ7BW+//Xbl5eXp/fff17Rp03T69Gm1bNmy0lBZMUGn4rXncs0116hp06ZavXq1tm7dqrZt27r2jR49Wv3791d2dra2b9+uuLg43XbbbXriiScqXXx9ypQp+uCDDzRr1izt2bNHiYmJeuCBB/TII4/UeFHwhg0b6rPPPtMLL7ygjz/+WNOnT1e9evV00UUXadSoURo4cKDh+IsvvlgLFy7UX//6Vy1ZskTl5eXq2LGj5syZo/r163sNlS1atND//vc/vfTSS1q0aJFmzpyp6OhopaWl6fbbb1dGRkaNagbgWzaHw+G5WiwAVNPs2bP14IMP6rHHHtO4cePMLifsjBw5UvPmzdPixYur3Ut37bXXau3atfq///u/81qgHQC84Z5KAAAA1BqhEgAAALVGqAQAAECtcU8lAAAAao2eSgAAANQaoRIAAAC1RqgEAABArbH4eQDl5OSwOK8PcT19x25PMLQdDh535yv8Tn3rfK+n/QXjU5gcY2v3uE8rSHB78lSRw+H1Opj5G01Y5FbjwMq/F2+fJxgF6nrW5NoFCj2VAAAAqDVCJQAAAGqNUAkAAIBaI1QCAACg1giVAAAAqDVCJQAAAGqNUAkAAIBaI1QCAACg1giVAAAAqDVCJQAAAGqNUAkAAIBaI1QCAACg1giVAAAAqDVCJQAAAGqNUAkAAIBaI1QCYa60tNRjm9PpNKESAICVESqBMPbKK6+odevWHts7deqklStXmlARAMCqCJVAmMrNzdX48eNVVFTksW/v3r269957vfZiAgDgDaESCFNvvvmmysvLK92fl5enL774IoAVAQCsjFAJhKHS0lK9/fbbVR43e/bsAFQDAAgFhEogDH322WfKz8+v8rglS5ZU6zgAAAiVQBiqbg9kWVmZ3nnnHT9XAwAIBYRKIMzs27dPn332WbWPnz17NksMAQCqRKgEwsw777xjmKDTvn17j2Pq1Knj+ufc3FytWrUqILUBAKyLUAmEkfLycr311luGbbfddpvHcdddd52h/eabb/q1LgCA9REqgTCycuVK7dixw9WOjo7WsGHDPI77/e9/b2h/9NFHcjgcfq8PAGBdhEogjLj3OA4aNEgXXHCBx3G/+c1v1KxZM1e7pKRE8+fP93t9AADrIlQCYcLhcGjhwoWGbd6GviUpIiJCv/vd7wzbWLMSAHAuhEogTKxcuVInT550tdPT03XFFVdUevytt94qm83mam/cuFGHDh3ya40AAOsiVAJh4vvvvze0+/Xrp4iIyv8voFmzZrr44ovPeQ4AACoQKoEw4R4Iu3TpUuVrOnfufM5zAABQocpQOXnyZNntdsOfCy+80LXf6XRq8uTJyszMVEpKigYOHKjNmzf7tWgANeN0OvXdd98ZtlUnVLof434OAAAqVKunMiMjQ1u3bnX9Wb16tWvfiy++qGnTpmnKlClatmyZkpKSNGTIEB09etRvRQOomT179qiwsNDVjo2NVUZGRpWvo6cSAFBd1QqVUVFRSk5Odv1JTEyUdKb3Izs7W2PHjtXgwYPVvn17ZWdn69ixYyw/AgQR9x7Giy66SJGRkVW+rl27dqpbt66rvW/fPuXn5/u8PgCA9VUrVO7cuVPt2rXTRRddpLvuuks7d+6UJO3atUv5+fnq3bu369iYmBj16tVL69at80vBAGpuw4YNhrZ7D2RloqOj1aFDB8M2eisBAN5EVXVAt27dNH36dGVkZKigoEDPPfec+vbtq7Vr17p6LJKSkgyvSUpK0v79+8953pycnFqUbV3h+rn9hetZPe7P7m7SpInbtetm2H/2vpYtW+rbb791tZcuXapWrVr5pc5Qxe/Ut3xxPcPhO+nm1vb2mSu2mXU9qlPj+RxrtkDUZtb1ONetU1WGymuuucbQ7tatmzp37qy3335bl1xyiSQZ1rKTzgyLu2+rSVGhKicnJyw/t79wPavH6XRq69athm39+/c/57U7e99vfvMbvf/++672rl27uO41wO/Ut3x1PcPxO/H2mTMyMsz9jW7zrKe6gvU7DNj1rMW185caLynUoEEDZWZmKjc3V8nJyZKkgwcPGo4pKCjw6L0EYI5du3YZntsdFxen1q1bV/v17jPAv//+ezmdTp/VBwAIDTUOlSUlJcrJyVFycrLS09OVnJys5cuXG/avWbNGPXr08GmhAM6Pt0k651r03F1mZqbq1avnaufn51d5ewsAIPxU+TfL448/rpUrV2rnzp36+uuvdfvtt+vEiRO65ZZbZLPZNGrUKL3wwgtauHChNm3apPvvv1+xsbEaOnRoIOoHUIXzWfT8bFFRUerUqdM5zwkAQJX3VO7bt0/33HOPCgsLlZiYqG7duunzzz9X8+bNJUljxoxRcXGxxo0bJ4fDoa5du2rBggWKi4vze/EAqnY+i56769y5s9avX28454ABA2pdGwAgdFQZKl977bVz7rfZbMrKylJWVpbPigLgG+Xl5bXuqfT2GnoqAQDuePY3EMJ27NihI0eOuNrx8fFq2bJljc/jvq7ld999x2QdAIABoRIIYe49ip07d65yuS9vLrzwQtWvX9/VLigoUF5eXq3rAwCEDkIlEMJ8cT+ldGayzkUXXXTOcwMAwhuhEghhvgqVkucQOPdVAgDORqgEQlR5ebk2btxo2FbdZ3574+2+SgAAKhAqgRC1fft2HT161NW22+1KT08/7/PxZB0AwLkQKoEQtWHDBkO7S5cu5zVJp0KbNm3UoEEDV/vnn3/Wnj17zvt8AIDQQqgEQtRPP/1kaLdv375W54uMjFS7du0M23Jzc2t1TgBA6CBUAiHKPfC1bt261ud0X+Ny+/bttT4nACA0ECqBEOUe+HwRKt3PQagEAFQgVAIhyj3wtWrVqtbnJFQCACpDqARC0OHDh+VwOFztunXrqmnTprU+r3uo3LFjR63PCQAIDYRKIAS59yC2bNlSERG1/9fd/Z7KnTt3qqysrNbnBQBYH6ESCEH+GPqWzqx12ahRI1f71KlTLCsEAJBEqARCkj9mfld2LobAAQASoRIISe6h0lc9lRLLCgEAvCNUAiHIX8PfEjPAAQDeESqBEON0Ov2yRmVl5+KpOgAAiVAJhJzDhw+rqKjI1a5Xr55SU1N9dn5CJQDAG0IlEGL8tZzQ2ec7286dO1VaWuqz8wMArIlQCYQYf95PKUkJCQlKTEx0tU+fPq29e/f69D0AANZDqARCjD+XE6rsnAyBAwAIlUCI8edyQhVYVggA4I5QCYQYfw9/SywrBADwRKgEQoi/lxOq7JwMfwMACJVACPn555915MgRVzsmJkZNmjTx+fu4934SKgEAhEoghPh7OaEK7qGSZYUAAIRKIIQE4n5KSYqPj1dSUpKrXVpaqj179vjlvQAA1kCoBEJIIJYTquzcDIEDQHgjVAIhJBDLCVVgWSEAwNkIlUAICdTwt8SyQgAAI0IlECKcTifD3wAA0xAqgRBRWFgYkOWEKrCsEADgbIRKIER4W07IZrP57f3cQ+WuXbtYVggAwhihEggRgXiSztni4uLUuHFjV7u0tFS7d+/263sCAIIXoRIIETt37jS0/TlJp7L32LFjh9/fEwAQnAiVQIjYu3evod28eXO/v6f7e+Tl5fn9PQEAwYlQCYQI91CZlpbm9/d0fw+eqgMA4YtQCYSIYAiV7jUAAMIHoRIIAeXl5R5Dz4RKAEAgESqBEFBQUKCTJ0+62vHx8UpISPD7+xIqAQAVCJVACDBj6Nvb++Tl5am8vDwg7w0ACC6ESiAEuE+QadasWUDe171H9NSpUzp06FBA3hsAEFwIlUAIMKun0tt7MQQOAOGJUAmEAEIlAMBshEogBJgZKt2H2lmrEgDCE6ESCAH0VAIAzEaoBEIAoRIAYDZCJWBxxcXFhhnXERERatKkScDen1AJAJAIlYDluT9JJzU1VVFRUQF7f57/DQCQCJWA5Zk59C1JKSkpioyMdLULCwt14sSJgNYAADAfoRKwOLMWPq8QFRWl1NRUwzb33lMAQOgjVAIWZ3ZPpbf35L5KAAg/hErA4oIhVLJWJQCAUAlYXDCESnoqAQCESsDiCJUAgGBAqAQszOl0EioBAEGBUAlYWEFBgU6ePOlqx8fHKyEhIeB1ECoBAIRKwMKCoZfS2/vm5eWpvLzclFoAAOYgVAIW5j7L2qxQGR8fr/j4eFf75MmTKigoMKUWAIA5CJWAhQVLT6W392YIHADCC6ESsDD34Bbop+mc671ZqxIAwguhErCwYOqpdA+V9FQCQHghVAIWFkyhkuFvAAhvhErAwgiVAIBgQagELKqkpEQHDx50tSMiItSkSRPT6nEPldxTCQDhhVAJWFReXp6hnZqaqqioKJOqoacSAMIdoRKwqGAa+paklJQURUZGutoFBQUqLi42sSIAQCARKgGLCpaFzytERUV5DL+796YCAEIXoRKwqGDrqZRYVggAwlmNQ+XUqVNlt9s1btw41zan06nJkycrMzNTKSkpGjhwoDZv3uzTQgEYufcCNm3a1KRKfsF9lQAQvmoUKtevX69Zs2apQ4cOhu0vvviipk2bpilTpmjZsmVKSkrSkCFDdPToUZ8WC+AX+/fvN7RTU1NNquQX7sPf7jUCAEJXtUNlUVGR/vCHP+ill16S3W53bXc6ncrOztbYsWM1ePBgtW/fXtnZ2Tp27Jjmz5/vl6IBSPv27TO0CZUAADNVO1RWhMYrr7zSsH3Xrl3Kz89X7969XdtiYmLUq1cvrVu3zneVAjBwD2xmrlFZwT3YugdfAEDoqtaidrNmzVJubq5mzJjhsS8/P1+SlJSUZNielJR0zl6KnJycmtQZMsL1c/tLuF7PkpISHT582NWOjIyUw+GoxS0n3Qyt872upaWlhvbOnTvD9js6G9fAt3xxPcPhO+nm1vb2mSu2mXU9qlPj+RxrtkDUZtb1yMjIqHRflaEyJydHTz/9tBYvXqzo6OhKj7PZbIa20+n02FbdokJVTk5OWH5ufwnn67ljxw5DOyUlRZmZmT47//le13r16hnaP//8c9h+RxXC+XfqD766nuH4nXj7zBkZGeb+Rrd51lNdwfodBux61uLa+UuVw99fffWVCgsL1bNnTzVq1EiNGjXSqlWrNHPmTDVq1EgXXHCBJBkeFyedWfjYvfcSgG+4DysHw9C3dCbcnv0fk4cOHdLp06dNrAgAEChVhsqBAwdq9erVWrFihetPly5ddOONN2rFihVq06aNkpOTtXz5ctdrSkpKtGbNGvXo0cOvxQPhKhjvp5SkOnXqGP5j0ul06sCBAyZWBAAIlCqHv+12u2G2tyTVr19fDRs2VPv27SVJo0aN0tSpU5WRkaE2bdro+eefV2xsrIYOHeqfqoEw5x4qU1JSTKrEU0pKimHkYv/+/R6LogMAQk+1JupUZcyYMSouLta4cePkcDjUtWtXLViwQHFxcb44PQA3wbicUIUmTZpo48aNrjbLCgFAeDivULlo0SJD22azKSsrS1lZWT4pCsC5uQ8pB8vwt+QZcAmVABAeePY3YEHBek+lxALoABCuCJWABQX78PfZCJUAEB4IlYDFeJtRHUw9lTxVBwDCE6ESsJjCwkKdOnXK1Y6Pj1eDBg1MrMiInkoACE+ESsBignXh8wreJuo4nU6TqgEABAqhErCYYJ6kI0kJCQmKiYlxtU+cOKGioiITKwIABAKhErCYYA+VNpuNIXAACEOESsBignnmdwVCJQCEH0IlYDHB3lMpMQMcAMIRoRKwmGB+7ndl6vnZAAAgAElEQVQF95roqQSA0EeoBCyG4W8AQDAiVAIWw/A3ACAYESoBCykuLtbhw4dd7cjISDVu3NjEiryjpxIAwg+hErAQ98czJicnKzIy0qRqKkeoBIDwQ6gELCTYn6ZTwX2izqFDh3T69GmTqgEABAKhErAQK9xPKUnR0dFKSkpytZ1Op0cvKwAgtBAqAQuxSqiUPGsjVAJAaCNUAhbiHiqDcTmhCu6hkhngABDaCJWAhVipp9I98DJZBwBCG6ESsBArhUpmgANAeCFUAhZihafpVCBUAkB4IVQCFuFtBnUwPve7AvdUAkB4IVQCFlFYWKhTp0652nFxcYqLizOxonOjpxIAwguhErAIqyx8XsHbRB2n02lSNQAAfyNUAhZhpUk6kmS321WvXj1X+8SJEyoqKjKxIgCAPxEqAYuwWqi02WwMgQNAGCFUAhZhpZnfFQiVABA+CJWARVitp1LyDL7MAAeA0EWoBCzCSssJVaCnEgDCB6ESsAgrPfe7gnvwzc/PN6kSAIC/ESoBi6CnEgAQzAiVgAWcPn1aBQUFrrbNZlPjxo1NrKh66KkEgPBBqAQswD2MJSYmqk6dOiZVU33uodK9txUAEDoIlYAFWHHoW5KSk5MN7QMHDqi8vNykagAA/kSoBCzAissJSVJsbKzi4+Nd7dLSUhUWFppYEQDAXwiVgAW4D39bpadS8gzADIEDQGgiVAIWYNXhb4n7KgEgXBAqAQtwH/62Uqh0v6+SZYUAIDQRKgELsHJPJcPfABAeCJWABbgHMatM1JFYqxIAwgWhErCAUOqpZPgbAEIToRIIcidPnjQsw2Oz2ZSUlGRiRTXjba1KAEDoIVQCQc59uLhx48aKiooyqZqa455KAAgPhEogyFl5jUrJs6cyPz+fp+oAQAgiVAJBzsrLCUlSTEyM7Ha7q11WVqaCggITKwIA+AOhEghyVp75XYHJOgAQ+giVQJBzD5Xuw8lWwGQdAAh9hEogyIVCTyVrVQJA6CNUAkHOymtUVmD4GwBCH6ESCHKhECrda2b4GwBCD6ESCHLuvXqhMPxNTyUAhB5CJRDESkpKdPjwYVc7IiJCiYmJJlZ0fuipBIDQR6gEgpj7hJbk5GRFRkaaVM35Y6IOAIQ+QiUQxELhfkrJe6gsKyszqRoAgD8QKoEgFiqhsm7durrgggtc7fLych06dMjEigAAvkaoBIJYKEzSqcB9lQAQ2giVQBDzdk+lVREqASC0ESqBIEZPJQDAKgiVQBALlXsqJZ6qAwChjlAJBLFQCpX0VAJAaCNUAkHMPXgx/A0ACFaESiBIFRcXy+FwuNpRUVFq1KiRiRXVDqESAEIboRIIUt5mfkdEWPdfWUIlAIQ26/4NBYQ494ksVr6fUvJcDungwYMqLS01qRoAgK8RKoEg5d5TafVQGR0drcTERFfb6XTyVB0ACCGESiBIhVpPpeTZW8kQOACEDkIlEKRCaTmhCqxVCQChi1AJBKlQ7Klksg4AhC5CJRCk3O+ptPIalRXcQyU9lQAQOgiVQJBy78Vzvx/RityDsXtwBgBYF6ESCFLuvXipqakmVeI7DH8DQOiqMlS+8sor6tWrl5o1a6ZmzZrpmmuu0ZIlS1z7nU6nJk+erMzMTKWkpGjgwIHavHmzX4sGQt3x48d15MgRV7tOnTq64IILTKzIN5ioAwChq8pQmZqaqokTJ+rLL7/U8uXL9etf/1ojRozQjz/+KEl68cUXNW3aNE2ZMkXLli1TUlKShgwZoqNHj/q9eCBUeVuj0mazmVSN79BTCQChq8pQOXDgQF1zzTVq1aqV2rRpoyeeeEINGjTQ+vXr5XQ6lZ2drbFjx2rw4MFq3769srOzdezYMc2fPz8Q9QMhyb0HLxQm6UhS48aNDeH40KFDOn36tIkVAQB8pUb3VJaVlen999/X8ePH1b17d+3atUv5+fnq3bu365iYmBj16tVL69at83mxQLgIxTUqJSkqKkqNGzc2bGOyDgCEhqjqHPR///d/6tu3r0pKShQbG6u33npLHTp0cAXHpKQkw/FJSUlV3iuVk5NzniVbW7h+bn8J1ev5ww8/GNoxMTF+/qzdDC1/vlfDhg0NQXL9+vUqLi722/sFg1D9nZrFF9czHL6Tbm5tb5+5YptZ16M6NZ7PsWYLRG1mXY+MjIxK91UrVGZkZGjFihUqKirSwoULNWrUKH3yySeu/e73ejmdzirv/zpXUaEqJycnLD+3v4Ty9XQfEs7MzAzoZ/Xne6Wnp2vLli2udmRkZMh+j1Jo/07N4KvrGY7fibfPnJGRYe5vdJtnPdUVrN9hwK5nLa6dv1QrVEZHR6tVq1aSpC5duujbb7/V9OnT9fDDD0uSDh48qLS0NNfxBQUFHr2XAKovVIe/Jc/7Q5msAwCh4bzWqSwvL9epU6eUnp6u5ORkLV++3LWvpKREa9asUY8ePXxWJBBuQnWijsQMcAAIVVX2VE6YMEF9+/ZV06ZNXbO6V65cqXnz5slms2nUqFGaOnWqMjIy1KZNGz3//POKjY3V0KFDA1E/EJLCqaeStSoBIDRUGSrz8/M1cuRIHTx4UPHx8erQoYPmz5+vPn36SJLGjBmj4uJijRs3Tg6HQ127dtWCBQsUFxfn9+KBUOR0OkM6VNJTCQChqcpQmZ2dfc79NptNWVlZysrK8llRQDg7cuSITpw44WrHxMQoISHBxIp8i1AJAKGJZ38DQcZbL2UoPE2nAsPfABCaCJVAkAnloW9JSkxMVGRkpKvtcDhCfp1KAAgHhEogyITyzG9JioiI8AjKPFUHAKyPUAkEmVDvqZQ8PxND4ABgfYRKIMiEek+lxGQdAAhFhEogyIRDTyWTdQAg9BAqgSATDqGSnkoACD2ESiDIMPwNALAiQiUQRLw9TSc5OdmkavyH4W8ACD2ESiCIHD58WKdOnXK14+LiQvKRp/RUAkDoIVQCQcS9xy4U76eUPHsqCZUAYH2ESiCIhMMkHUlq2LChoqOjXe2jR4/q6NGjJlYEAKgtQiUQRMJhko4k2Ww2nqoDACGGUAkEkXDpqZSYrAMAoYZQCQSRcAqVTNYBgNBCqASCSLgMf0uESgAINYRKIIiEU08lw98AEFoIlUAQcQ+V9FQCAKyCUAkEifLy8rB4mk4FeioBILQQKoEgUVBQoLKyMlfbbrcrJibGxIr8i55KAAgthEogSITTJB3Je6h0Op0mVQMAqC1CJRAkwmmSjiTFx8erfv36rnZxcbGKiopMrAgAUBuESiBIhFuo9PZUHYbAAcC6CJVAkAi34W+J+yqtatu2bbrmmmuUnJysTp06STpzD/BHH31U6WsKCwtlt9u1YsWKQJUJIMAIlUCQCLeeSsnaM8CPHj2qRx55RB07dlRKSor69u2rb7/91nDMqFGjdMkll8hut7v+XH311YZjHn30UbVo0UIdOnTQvHnzDPsWL16sa6+9ttr3mi5cuFCDBg1S8+bNlZqaql69eumZZ57RoUOHavdh3UyaNEkxMTH66quvtHz5cknS1q1bde211/r0fQBYC6ESCBLugSocQqWVeyoffPBBLVu2TNnZ2Vq9erWuuuoq3XDDDdq3b5/huO7du2vr1q2uP++9955r3+LFizV//nx98MEHmjhxoh588EEVFhZKOhNaH330Ub3wwguy2WxV1vPMM8/ojjvuUKdOnTR37lytXbtWkydP1u7du/Xqq6/69LPn5ubq0ksvVXp6uhITEyWdWf6qbt26Pn0fANZCqASCBMPf8ghkwaq4uFgLFy7UU089pSuuuEKtWrVSVlaWWrZsqddee81wbHR0tJKTk11/GjZs6Nq3bds2XX755erSpYuGDh2quLg47dq1S5L09NNPa9iwYcrMzKyynm+++UZTp07V008/rWeffVY9e/ZU8+bNdeWVV+qVV17Rfffd5zr29ddfV5cuXZSUlKQuXbpo1qxZhnPZ7Xa98cYbuv3225WamqqLL75Yc+fONez/8ccf9fe//112u12TJ092bT97+Pvbb7/VlVdeqeTkZF1xxRX6+uuvPeresmWLhg0bprS0NLVp00Z333238vPzXftHjRql4cOHKzs7W+3atVN6eromTpyoEydOuI5xOp166aWX9Ktf/UqNGzdW+/btNXHiRNf+ffv26a677pL+pjN/5kgqrPKSAjgPhEogSITTwucVrDr8XVpaqrKyMtWrV8+wPSYmRmvWrDFs+/7779WmTRt17dpVDz74oGEoumPHjvruu+/kcDj0/fffq6SkRK1atdL69eu1cuVK/fnPf65WPfPmzVNsbKzuvfder/vtdrsk6eOPP9a4ceM0atQorVmzRvfdd5/+/Oc/a/HixYbj//73v2vAgAFauXKlfvvb3+qBBx7Q7t27JZ0Z5s7IyNADDzygrVu3avTo0R7vd/z4cQ0bNkwtWrTQ8uXLNWHCBD3xxBOGYw4cOKABAwaoXbt2Wrp0qT788EMdO3ZMt9xyi8rLy13HrVmzRps3b9aHH36o119/Xf/973/1r3/9y7X/6aef1nPPPac//elPWrt2rd544w01bdpUknTixAkNGjToTA/qHZLultRA0mwZgikA3yBUAkHg1KlTOnjwoKvtbWZ0KLJqqIyLi1P37t31/PPPa9++fSorK9PcuXP11VdfGXrarr76ak2YMEEfffSRJk2apG+++UbXX3+9Tp48KUnq06ePhg0bpquuukr333+/pk+frtjYWI0dO1b/+Mc/NGfOHHXv3l1XXnml1q1bV2k9ubm5atGiherUqXPOul9++WUNHz5cI0eOVJs2bXTvvffqpptu0osvvmg4bvjw4Ro+fLhatWqlxx57TFFRUa6wnJycrKioKMXGxio5OVkNGjTweJ/33ntPp06d0rRp09S+fXv16dPHIyC/+uqr6tixoyZOnKi2bduqY8eOmjFjhr799lt99913hmv9j3/8Q23btlXv3r3Vp08fffnll5KkY8eOafr06ZowYYJ+//vfq1WrVurevbvuueceSdL7778vp9Op6dOnSymSkiQNknRKWrJkyTmvFYCaizK7AACevZRJSUmKjo42qZrASU1NNbStEiolacaMGfrjH/+o9u3bKzIyUhdffLGGDh2qDRs2uI658cYblZOTo4yMDHXo0EGdO3dWp06dtGTJEl1//fWSpKysLGVlZble89xzz6l79+6Kj4/Xs88+qxUrVmjTpk264447tGHDBq+/i+pO5Nm6datGjBhh2NazZ0+PnsoOHTq4/jkqKkqNGjWq0WSfrVu3qkOHDobA2b17d8MxGzZs0OrVq129imfbsWOHunbtKklq27atoqJ++asqKSlJOTk5rvc5efKkrrzySq91bNiwQbt27VJaWpp06qwdp8+8BwDfIlQCQcD9XkL3sBWq3HsqDxw4oNLSUkOICFYtW7bUf/7zHx0/flxHjx5VSkqK7rzzTqWnp1f6miZNmig1NVW5uble9//0009666239L///U/vvPOOevXqpZSUFKWkpOjUqVPKyckxBL4KrVu31po1a3Tq1Kkq/2PE26Qf923uPZ42m61GTzuqzrHl5eXq27evJk2a5LEvKSmpWrVU9T7l5eXq1KmTXnvtNf3q9V8Z9t15551V1gigZhj+BoJAOE7SkaR69erpggsucLXLy8sNtwFYQWxsrFJSUuRwOLR06VINGDCg0mMLCwu1f/9+r/fLOp1OjR07Vs8884wSEhJUXl6u06dPu/adPn3a8Gz4s9100006fvy4/v3vf3vd73A4JJ3p9Vu7dq1h35o1a6o1GagmMjMztWnTJh0/fty1bf369YZjLr74Ym3ZskXNmjVTq1atDH/i4uKq9T5t27ZV3bp1XcPh7i6++GLl5uae+Y01kuHP2ROmAPgGoRIIAnl5eYa2tyHBUGXVIfClS5fq888/186dO7V8+XJdd911ysjIcA0vHzt2TI8//rg2btyoXbt2acWKFbr55puVlJSk6667zuN8b775phISElzD4j179tSKFSu0Zs0avfrqq6pTp44yMjK81tKtWzeNGTNGTz75pB599FGtXbtWu3fv1ooVKzRy5EjXxJbRo0dr7ty5euWVV7R9+3bNmDFD7733nh588EGfXpuhQ4cqKipKDzzwgDZv3qzly5dr6tSphmPuueceHTlyRHfeeae+/vpr7dy5U//97381ZswYHT16tFrvExcXp/vuu08TJ07UW2+9pR07duibb75xLaF00003qXHjxrr11lulnZIO68z/LpG2b9/uy48MQAx/A0EhXHsqpTOh8scff3S18/LyXPfTBbMjR45o4sSJ2rdvnxo2bKjrr79ejz/+uGu4NjIyUps2bdKcOXN07Ngx19I6r7/+ukdP3MGDB/Xcc88ZJo906dJFf/rTn/S73/1ODRo00IwZMxQTE1NpPRMnTlSXLl30yiuv6K233lJpaanS09M1YMAA18SV6667Tn//+9/10ksvKSsrS82aNdPUqVPVv39/n16bBg0aaO7cuXrooYd05ZVXKiMjQxMmTNAtt9ziOqZJkyZasmSJJk6cqBtvvFEnT55UWlqarrrqqhqtd/nUU0/Jbre7ZoA3btxYN998sySpfv36+s9//qMJEyZo9bzV0klJcZJa/DIjHoDv2BwOR/VvlEGtVNywD98Ipet555136oMPPnC1s7OzDX8B+5vdnmBoOxxFAXvvsWPH6o033nC1p0yZUunSOFYUSr/TYHC+19P+gjFEOsY6fFVS0EpwC85FDofX62DmbzRhkVuNAyv/Xrx9nmAUqOtZk2sXKAx/A0HAvacyXCbqSJ69slZZAB0AYESoBIKAe5AKp+Fvq65VCQAwIlQCJisvLw/reyrdJyW5T1oCAFgDoRIwWWFhoWvpGEmKj4+v9pIqoYCeSgAIDYRKwGTuPXPhdD+l5Pl59+3bV6OFthGc5syZE1ZLYwEgVAKmC+ehb0lKSEhQ/fr1Xe2SkhLXYt3haNeuXbLb7YbnX1dlxYoVstvtKiws9GNlAHBuhErAZOH6iMYKNpvNa28lzHH2rRgAUBOESsBk4d5TKbGs0LlU9Fx+9NFHuuGGG9SkSRP16NFDy5cvd+0fNGiQpDPPALfb7Ro1apSkM493fPHFF9W5c2elpKSoV69emjt3rse558+fr0GDBiklJUUzZ85USkqKFi9ebKhj2bJlSkxM1KFDhyRJEyZMULdu3ZSSkqJOnTrpySefVElJSSAuCYAgRagETBbOj2isYNVHNQbSpEmTdO+992rlypXq0qWL7rrrLh07dkxpaWmaPXu2JGnt2rXaunWr/va3v7le8+abb+r555/X2rVr9ac//Ul/+tOfDE/ukc48jeeee+7R2rVrdf3116tfv3567733DMfMmzdPvXv3VlJSkqQzT6t5+eWXtW7dOk2dOlULFizQ888/H4ArASBYESoBk9FT6RkqWVbI0/3336/+/furdevWevLJJ3X48GH98MMPioyMVMOGDSVJSUlJSk5OVkJCgo4fP65p06bpn//8p66++mq1aNFCN910k2677TbNnDnTcO6RI0dq8ODBatGihZo2baphw4Zp8eLFrmdwFxcXa9GiRRo2bJjrNX/5y1906aWXKj09XX379tVDDz2k999/P3AXBEDQ4dnfgMnC/Z5KiZ7K6ujQoYPrnyv+w6NiKNqbrVu3qqSkREOHDpXNZnNtP336tJo3b244tkuXLoZ23759FRMTo08++US33HKLFi9eLKfTqQEDBriO+eijj5Sdna3c3FwdP35cZWVlKisrq9VnBGBthErAZOH8iMYK3FNZtTp16rj+uSIknmvppfLycknSO++8o2bNmhn2RUUZ/68/NjbW471uuOEGvffee7rllls0b948XXfdda5Z+uvXr9ddd92l8ePH69lnn1VCQoL+85//6Iknnjj/DwjA8giVgImOHDniGmKUpOjoaDVq1MjEiszhfh8pobJmoqOjJcnQU9i2bVvVrVtXe/bs0ZVXXlnjcw4bNkwDBw7Uli1btHTpUs2bN8+1b+3atWrSpIn+8pe/uLbt2bOnFp8AQCggVAIm8nY/5dlDleGCnsraadasmWw2m5YsWaL+/furXr16iouL0+jRo/XEE0/I6XTqsssu07Fjx/T1118rIiJCd9xxxznPeemll6pZs2a655571KhRI/3617927WvTpo3279+vefPmqXv37lq6dCn3UwJgog5gJu6nPCMpKckwJOtwOHTixAkTK7KW1NRUZWVladKkScrIyNC4ceMkSY899pgeeeQRvfzyy7r00ks1ZMgQLVy4UOnp6dU670033aQff/xRN954oyIjI13b+/fvrwcffFBZWVm67LLLtHz5cj366KN++WwArMPmcDh4HlqA5OTkKCMjw+wyQkYoXM85c+boj3/8o6t944036tVXXw14HXZ7gqHtcBQFvIaOHTtq7969rvY333yj1q1bB7wOXwuF32kwOd/raX/Bbmg7xob+U5sS7MbPXORweL0OZv5GExa51Tiw8u/F2+cJRoG6njW5doFCTyVgInoqf8FTdQDA2giVgIlYo/IX7p+dZYUAwFoIlYCJeJrOL+ipBABrI1QCJqKn8heESgCwNkIlYCLuqfwFoRIArI1QCZjk5MmTKigocLVtNpuSk5NNrMhc3FMJANZGqARMcuDAAUM7OTnZ8Ci+cENPJQBYG6ESMIl7aArn+yklz8+fn5+v0tJSk6oBANQUoRIwifvwbjjfTylJdevWVWJioqtdXl6u/Px8EysCANQEoRIwiftyQuEeKiXuqwQAKyNUAiZhOSFP3FcJANZFqARMwnJCngiVAGBdhErAJPRUemL4GwCsi1AJmIRHNHqipxIArItQCZigvLzcY51Keio9Q6V78AYABC9CJWAC9zUYGzZsqNjYWBMrCg5paWmG9t69e02qBABQU4RKwAR79uwxtN3DVLhyvwVg3759KisrM6kaAEBNECoBE7j3wBEqz4iNjdUFF1zgapeWlrIAOgBYBKESMAGhsnIMgQOANVUZKv/xj3/oqquuUrNmzdS6dWsNHz5cmzZtMhzjdDo1efJkZWZmKiUlRQMHDtTmzZv9VjRgde7D382bNzepkuDTrFkzQ9v9WgEAglOVoXLlypW6++67tWTJEi1cuFBRUVG64YYbdPjwYdcxL774oqZNm6YpU6Zo2bJlSkpK0pAhQ3T06FG/Fg9YFfdUVo6eSgCwpqiqDliwYIGhPWPGDDVv3lxr165V//795XQ6lZ2drbFjx2rw4MGSpOzsbGVkZGj+/Pm68847/VM5YGEMf1eOUAkA1lTjeyqPHTum8vJy2e12SdKuXbuUn5+v3r17u46JiYlRr169tG7dOt9VCoQQQmXlGP4GAGuqsqfS3SOPPKJOnTqpe/fukuSamZmUlGQ4Likp6ZyPWMvJyanpW4eEcP3c/mLF63n8+HE5HA5XOyoqSkeOHNGxY8dMrKqboRVM13X79u1BVc/5sHr9wcYX1zMcvpNubm1vn7lim1nXozo1ns+xZgtEbWZdj4yMjEr31ShUPvroo1q7dq0+/fRTRUZGGvbZbDZD2+l0emyrblGhKicnJyw/t79Y9Xpu2bLF0E5LS1Pbtm1NqsY7M69rfHy8oX3w4EFLfs8VrPo7DVa+up7h+J14+8wZGRnm/ka3edZTXcH6HQbsetbi2vlLtYe/s7Ky9P7772vhwoVq0aKFa3tycrKkM//Hf7aCggKP3ksATNKpSlJSkqKjo13toqIiHTlyxMSKAADVUa1QOX78eM2fP18LFy7UhRdeaNiXnp6u5ORkLV++3LWtpKREa9asUY8ePXxbLRACuJ/y3CIiIjyerMMzwAEg+FUZKh9++GG9/fbbmjlzpux2u/Lz85Wfn++6/8tms2nUqFF64YUXtHDhQm3atEn333+/YmNjNXToUL9/AMBqCJVVYwY4AFhPlfdUzpw5U5JcywVVGD9+vLKysiRJY8aMUXFxscaNGyeHw6GuXbtqwYIFiouL80PJgLWx8HnVmAEOANZTZag8e5ZqZWw2m7KyslwhE0DluKeyavRUAoD18OxvIMAY/q4aoRIArIdQCQRQWVmZ9u3bZ9jmPikFDH8DgBURKoEAOnDggMrKylztRo0aKTY21sSKghOhEgCsh1AJBBBD39Xj3nu7f/9+lZaWmlQNAKA6CJVAADFJp3piYmKUmJjoapeVlenAgQMmVgQAqAqhEgggeiqrj8k6AGAthEoggAiV1ed+bbivEgCCG6ESCCAWPq8+98k69FQCQHAjVAIBRE9l9TH8DQDWQqgEAoiJOtVHqAQAayFUAgFSVFSkI0eOuNrR0dFKSkoysaLgxlqVAGAthEogQPLy8gzttLQ0RUTwr2BluKcSAKyFv9GAAOF+yppJTExU3bp1Xe0jR46oqKjIxIoAAOdCqAQChPspa8Zms3FfJQBYCKESCBB6KmuOtSoBwDoIlUCAuIdK93sG4Yn7KgHAOgiVQIAQKmuO4W8AsA5CJRAg3FNZc4RKALAOQiUQAKWlpdq/f79hW9OmTU2qxjpYqxIArINQCQRAXl6eysrKXO3ExETFxMSYWJE1uPdU7t6926RKAABVIVQCAbBz505Du0WLFqbUYTXNmjUzLBC/f/9+FRcXm1gRAKAyhEogANxDZcuWLc0pxGKio6M9bhPYtWuXSdUAAM6FUAkEgHuoTE9PN6cQC3Lv1XW/lgCA4ECoBAJgx44dhjY9ldXnfq3cryUAIDgQKoEA4J7K80dPJQBYA6ESCAB6Ks+f+7UiVAJAcCJUAn7mcDhUVFTkatetW1cpKSkmVmQt9FQCgDUQKgE/c++lbNGihWGZHJybt57K8vJyk6oBAFSGv9kAP+N+ytqx2+2y2+2u9smTJ3XgwAETKwIAeEOoBPzMW08lasb9mjEDHACCD6ES8DMWPq89JusAQPAjVNF0lUwAACAASURBVAJ+Rk9l7dFTCQDBj1AJ+BnLCdUePZUAEPwIlYAfnTp1Snl5eYZtzZs3N6ka63J/rCWhEgCCD6ES8KPdu3fL6XS62qmpqYqJiTGxImviUY0AEPwIlYAfufeoufe4oXqaNm2qOnXquNqFhYU6cuSIiRUBANwRKgE/4n5K34iMjPS4bYAhcAAILoRKwI9Y+Nx3eFwjAAQ3QiXgR/RU+g4zwAEguBEqAT9i4XPfoacSAIIboRLwE6fTqV27dhm2Mfx9/lgAHQCCG6ES8JNDhw7p+PHjrnZcXJwaNWpkYkXWxvA3AAQ3QiXgJ+49aenp6bLZbCZVY33uyzHt2bNHpaWlJlUDAHBHqAT8hEk6vtWgQQM1btzY1S4tLdXevXtNrAgAcDZCJeAnLCfke0zWAYDgRagE/ISeSt9jsg4ABC9CJeAnzPz2PXoqASB4ESoBP6Gn0vfcryE9lQAQPAiVgB+cOHFC+fn5rnZkZKTS0tJMrCg00FMJAMGLUAn4gXvYSUtLU506dcwpJoR466l0Op0mVQMAOBuhEvCDbdu2GdqtW7c2qZLQkpycrAYNGrjaR48e1YEDB0ysCABQgVAJ+MHWrVsN7QsvvNCkSkKLzWZTRkaGYZt7gAcAmINQCfhBTk6Ood22bVuTKgk97gGdUAkAwYFQCfiBe0+le+8azh+hEgCCE6ES8LHy8nL99NNPhm30VPqOe6h0D/AAAHMQKgEf2717t4qLi13thg0bKjEx0cSKQot7QKenEgCCA6ES8DH3kNO2bVvZbDaTqgk9LVu2VFRUlKt94MABFRUVmVgRAEAiVAI+x8xv/6pTp47HEk30VgKA+QiVgI+5BxxCpe9xXyUABB9CJeBj3oa/4VvcVwkAwYdQCfiQ0+lkOaEAYAF0AAg+hErAhwoKCuRwOFztmJgYNW/e3MSKQhM9lQAQfAiVgA+591K2adNGERH8a+Zr7j2VO3fuVElJiUnVAAAkQiXgU9xPGRixsbFKS0tztcvLy7V9+3YTKwIAECoBH2I5ocBhCBwAgguhEvAheioDh2WFACC4ECoBH3IPlcz89h/3UElPJQCYi1AJ+MjRo0eVl5fnakdERHg8+QW+Q08lAAQXQiXgIz/99JOh3bJlS9WtW9ekakKf+60F27dvV1lZmUnVAAAIlYCPMEknsBITE3XBBRe42iUlJdqzZ4+JFQFAeCNUAj7CJJ3Ac7/GDIEDgHmqFSpXrVqlm2++We3atZPdbtecOXMM+51OpyZPnqzMzEylpKRo4MCB2rx5s18KBoIVPZWBx2QdAAge1QqVx48fV/v27fW3v/1NMTExHvtffPFFTZs2TVOmTNGyZcuUlJSkIUOG6OjRoz4vGAhW9FQGHpN1ACB4VCtU9u3bV08++aQGDx7s8cg5p9Op7OxsjR07VoMHD1b79u2VnZ2tY8eOaf78+X4pGgg2p06dUm5urmFbmzZtTKomfNBTCQDBo9b3VO7atUv5+fnq3bu3a1tMTIx69eqldevW1fb0gCXk5uYaZh43adJECQkJJlYUHrz1VDqdTpOqAYDwFlXbE+Tn50uSkpKSDNuTkpK0f//+Sl+Xk5NT27e2pHD93P4SLNdz+fLlhnZaWlrQ1FY93Qwtq9ReXl6uevXqqaSkRJJUVFSk1atXq3HjxiZXZmSV62kVvrie4fCddHNre/vMFdvMuh7VqfF8jjVbIGoz63qc66EetQ6VFWw2m6HtdDo9tlW3qFCVk5MTlp/bX4Lper799tuGdvfu3YOmtvNhpdo7duyor7/+2tU+evSoLrvsMhMrMgqm32ko8NX1DMfvxNtnzsjIMPc36nbHSk3qCNbvMGDXsxbXzl9qPfydnJwsSTp48KBhe0FBgUfvJRCqNmzYYGhffPHFJlUSftyvtft3AQAIjFqHyvT0dCUnJxuG/0pKSrRmzRr16NGjtqcHgp7T6SRUmohQCQDBoVrD38eOHXPNbC0vL9fevXu1ceNGNWzYUM2aNdOoUaM0depUZWRkqE2bNnr++ecVGxuroUOH+rV4IBjk5eWpsLDQ1a5fvz4zvwPooosuMrQ3btxoUiUAEN6qFSq/++47DRo0yNWePHmyJk+erFtuuUXZ2dkaM2aMiouLNW7cODkcDnXt2lULFixQXFyc3woHgoV7z1inTp0UGRlpUjXhp127dqpTp45Onz4tSdq7d68KCgqUmJhocmUAEF6qFSqvuOIKORyOSvfbbDZlZWUpKyvLZ4UBVuEeKt17zuBfdevWVbt27Qw9lBs3bjQscwYA8D+e/Q3UEvdTmo/7KgHAfIRKoJbc7+EjVAYeoRIAzEeoBGohPz/fsMh/3bp1lZmZaWJF4YlQCQDmI1QCteDeS9mhQwfVqVPHpGrCV8eOHQ2To3bs2HHO+8ABAL5HqARqgfspg0NMTIzatm1r2PbDDz+YVA0AhCdCJVALhMrg4T7rniFwAAgsQiVQC4TK4OF+7VkEHQACi1AJnKfDhw/r/9u786AozvQP4N9hAFHAIIigAqICQQFFRECNSTw2rroeQfHIrjdCucbEuEFR/BVqXCmDWY/ErKiQdbfMesdAQjQpgyYKBoxLKWS9gqIYQQ45wzkzvz8Ms/QMcsxBM8P3UzWl7zt9PP10M/NM9zs9Dx48ULZNTU0xZMgQESPq2vhlHSIicbGoJNKQ6pkwT09PWFhYiBQN+fj4CNq3b99GVVWVSNEQEXU9LCqJNMRL352LtbW14DfXFQoFsrKyRIyIiKhrYVFJpCEWlZ0PL4ETEYmHRSWRhlhUdj4sKomIxMOikkgD5eXluHv3rrItkUjg7e0tYkQEsKgkIhITi0oiDaiO1fPw8IClpaVI0VAj1XtV3rx5EzU1NSJFQ0TUtbCoJNLA1atXBW3VYobE0atXL7i4uCjbDQ0NPFtJRNRBWFQSaeDy5cuCdkBAgEiRkCrVfaG6r4iISD9YVBK1k0wmQ1pamqBv7NixIkVDqlT3BYtKIqKOwaKSqJ1u3LiB8vJyZdvW1haenp4iRkRNqRaVV65cQX19vUjREBF1HSwqidrp0qVLgvaYMWNgYsI/pc7C3d0dffr0Ubarqqo4rpKIqAPwnZConVQvp/LSd+cikUgwZswYQR8vgRMR6R+LSqJ2kMvlauMpX3rpJZGioedR3ScsKomI9I9FJVE7ZGdno7S0VNm2sbGBl5eXiBFRc5obV9nQ0CBSNEREXQOLSqJ2UB1POXr0aI6n7IQ8PT1hZ2enbJeXl+PGjRsiRkREZPz4bkjUDhxPaRiaG1ep+oGAiIh0i0UlURvJ5XKkpqYK+jiesvPi/SqJiDoWi0qiNrp58yZKSkqU7Z49e8LHx0fEiKglqkVlWloaZDKZSNEQERk/FpVEbaR6pisoKAhSqVSkaKg1Xl5esLGxUbbLysqQnZ0tYkRERMaNRSVRG6kWlbz03bmZmJjwfpVERB2IRSVRGygUCn5JxwCp7iN+WYeISH9YVBK1we3bt1FYWKhsW1lZYfjw4SJGRG2hWlSmpqZCLpeLFA0RkXFjUUnUBqpnuAIDA2FqaipSNNRWPj4+6Nmzp7L99OlTjqskItITFpVEbZCcnCxoczylYZBKpRg9erSg76uvvhIpGiIi48aikqgVpaWluHjxoqBvypQpIkVD7TV16lRBOzExUaRIiIiMG4tKolacPXtW8LvRHh4e8PT0FDEiao+pU6cKfkozKysL9+7dEzEiIiLjxKKSqBVJSUmC9vTp00WKhDRhb2+vdgn8iy++ECkaIiLjxaKSqAWVlZU4f/68oI9FpeFR3We8BE5EpHssKolacP78edTU1Cjbzs7OvJWQAfrDH/4gaGdkZOCXX34RKRoiIuPEopKoBc1d+pZIJCJFQ5pycnLCyJEjBX1ffvmlSNEQERknFpVEz1FbW4tz584J+njp23Cp7jvVDwxERKQdFpVEz3HhwgVUVFQo2w4ODggMDBQxItKGalF5+fJlFBcXixQNEZHxYVFJ9ByqZ7KmTZsmuDUNGZbBgwdj6NChyrZMJlO7qT0REWmO75BEzWhoaFArOGbMmCFSNKQrqvuQtxYiItIdFpVEzbh8+TJKSkqUbRsbG4wdO1bEiEgXVC+Bp6SkoLy8XKRoiIiMC4tKomacOHFC0J4yZQrMzMxEioZ0ZejQoRg0aJCyXVdXhzNnzogYERGR8WBRSaSiqKhIraicNWuWSNGQLkkkErV9GRcXB4VCIVJERETGg0UlkYrDhw+jtrZW2R4wYAAmTZokYkSkS0uWLBF84So7OxuXL18WMSIiIuPAopKoifr6esTHxwv6VqxYAalUKlJEpGsuLi6YNm2aoG///v0iRUNEZDxYVBI1kZiYKPj5PktLS/zpT38SMSLSh/DwcEE7OTkZubm5IkVDRGQcWFQSNaF6xmrBggWwsbERKRrSl7Fjx8Lb21vZlsvlOHjwoIgREREZPhaVRL/58ccfkZGRIegLCwsTKRrSJ4lEona28p///CcqKytFioiIyPCxqCT6TVxcnKA9ceJEeHh4iBQN6VtISAjs7OyU7fLychw7dkzEiIiIDBuLSiIA+fn5+OyzzwR9qmeyyLhYWFhgyZIlgr64uDjI5XJxAiIiMnAsKonwrJior69Xtt3c3HgboS5g+fLlMDU1VbZv376Nc+fOiRgREZHhYlFJXV5OTg727dsn6AsLCxPcy5CMU79+/TBz5kxBX1RUlOA+pURE1DZ816QuTaFQIDIyEnV1dco+BwcHLFiwQMSoqCO99dZbkEgkynZOTg4++ugjESMiIjJMLCqpS/vqq6/w9ddfC/q2bt0Ka2trkSKijjZ8+HC1sZU7d+7Ew4cPxQmIiMhAsaikLqu6uhqRkZGCvtGjR2Pu3LkiRURi+b//+z/06tVL2a6urkZUVJSIERERGR4WldRl7d69Gw8ePFC2pVIpYmNjBZdCqWuwtbVFdHS0oC8xMREpKSkiRUREZHhYVFKXdP/+fezevVvQFxoaKviVFepaFi5cCD8/P0HfunXrBONtiYjo+VhUUpdTU1ODsLAwwTd87e3tsWHDBhGjIrFJpVLs3LlTcKb6zp07vAxORNRGLCqpS1EoFFi1ahXS09MF/Vu2bOFvfBP8/PywaNEiQd/BgwfVfm2JiIjUsaikLiUmJganTp0S9I0fPx7z588XKSLqbKKjo9G/f39B34YNG9TuEkBEREIsKqnLOH78ON5//31Bn4eHBz755BPe6JyUbG1tcfToUVhZWSn75HI5li1bhqysLBEjIyLq3PhOSl3CxYsX8eabbwr67OzscOzYMV72JjU+Pj6Ij48XfNiorKzE/PnzkZubK2JkRESdF4tKMmoKhQJxcXGYPXu24Fu85ubmOHLkCAYOHChidNSZTZ48Gdu3bxf05eXlYfz48bh48aJIURERdV4sKslo1dTU4M9//jPWr1+PhoYGwXP79u1DUFCQSJGRoQgPD8eKFSsEfSUlJQgODsbHH38MhUIhUmRERJ0Pi0oySj///DOmTp2Kf//732rPvffeewgJCREhKjI0EokEMTExeP311wX9MpkMGzduRHh4OEpLS0WKjoioc2FRSUbl8ePHWLt2LQIDA3Ht2jXBc9bW1jhy5AhWr14tUnRkiExNTREfH4+oqCi1X1s6fvw4hg8fjl27duHXX38VKUIios6BRSUZhby8PGzevBl+fn5ISEhQu9zt5uaG8+fPY9q0aSJFSIbMxMQEEREROHr0KHr27Cl4rqysDFu2bIGfnx8OHjyIsrIykaIkIhIXi0oyWEVFRThw4AB+//vfw9vbG7t370Z1dbXadJMnT8b58+fh4eEhQpRkTCZPnoxvv/0WL774otpz+fn5iIiIgLu7O9544w2cOnUKVVVVIkRJRCQOU10u7NChQ9i7dy8KCgrg6emJmJgYjBkzRperoC6qqqoKd+/exbVr13D16lX8+OOPuHXrVotflBg4cCCioqIwe/ZstcuWRJpyc3PDhQsXEBcXh127dqmdmayrq0NycjKSk5Nhbm4Od3d3jB07Fv7+/hgxYgQGDBgAc3NzkaInItIfnRWVp0+fRmRkJD744AMEBQXh0KFDCAkJwZUrV+Ds7Kyr1ZABksvlkMlkaGhoQG1tLerr61FbW4va2lpUVVXh119/RVVVFSorK1FSUoLi4mIUFxejsLAQubm5uH//PgoLC9u8PgcHB6xfvx4LFy6EmZmZHreMuqru3btjzZo1WLJkCfbs2YP9+/c3e5a8rq4O2dnZyM7OxoEDBwA8u5Ter18/uLq6wtnZGb1794adnR1sbW3Rq1cvWFtbo0ePHrC0tISlpSXMzc0FD1NTU0ilUn5QIqJOR2dF5b59+/DGG29g8eLFAIDY2FicP38eCQkJiI6O1tVq2u369euYM2eOaOtvqqGhAaamOj05rLHWboWi+nxju+m/jY+m/XK5XPloLCQ76rYr/v7+mDNnDhYtWoQePXp0yDqpa7OxsUF0dDTCwsIQHx+PU6dO4d69ey3OI5fLkZeXh7y8PK3WbWJiAlNTU5iYmAgewLNvrTd9NPY1/bep1grUzlTAavw6+mdhU5vhMGZmZsjOztZ4fiJjpZMKp66uDpmZmWrfqp0wYQJ++OEHXaxCYzKZDE+ePBE1BtIPiUQCHx8fBAcHY9asWXB1dRU7JOqi+vbti02bNiEqKgqZmZk4deoUEhMT8eDBA72tUy6XC27oT+2jzfsCr4AQNU8nRWVxcTFkMhns7e0F/fb29s/9w71z544uVt0qfb6oU8eRSqVwdHTE4MGD4e3tDS8vLwwdOlT5+8z19fUddkwZJ39Bi7nUnJWVFRYvXozFixejqKgIWVlZyMrKQnZ2NnJzc9s1lIM6J4VCYRB/I/4q7eZibuwTa3vaEqMm04qtI2ITKx/u7u7PfU6n12JVL5EoFIrnXjZpKShdqqys7JD1UMukUilMTU3RrVs3wfiwxnFjjY9evXrBzs5OOcbMyckJrq6u6N+/v9olrzt37nTYcdTVMK+64e7ujt69ewt+lae6uhoPHz7EvXv3kJ+fLxhH/PTpU+UY48Z/G8cgN/4rk8kgk8lE3CqSSCQG+TfSXMzu7u7ivpbeVo+nrTrrPuiwfGqRO33RSVFpZ2cHqVSqdlayqKhI7exlR/P29satW7dEjaFRTk4OBg0aJHYYSu0dR9Xc2CzVPqlUKhjf1TjmqzONySISU/fu3eHh4aHVmD6FQiEYs9x0HHPj882NeW5ufHN7x1eLTdPX0Rf/LbwNVGd5XyAyJjopKs3NzeHr64uUlBTMmjVL2Z+SkoIZM2boYhUaMzMzg4ODg6gxNCovL+80sRCR4ZJIJDA1Ne00X/zrSLp6HeVrMZHu6ewVadWqVQgPD8fIkSMRGBiIhIQE5OfnY+nSpbpaBRERERF1UjorKoODg1FSUoLY2FgUFBRgyJAhOH78OFxcXHS1CiIiIiLqpHR67SQ0NBShoaG6XCQRERERGQD+9jcRERERaY1FJRERERFpjUUlEREREWmNRSURERERaY1FJRERERFpjUUlEREREWmNRSURERERaY1FJRERERFpjUUlEREREWmNRSURERERaY1FJRERERFpjUUlEREREWmNRSURERERaU1SWlqqEDsIIiIiIjJsPFNJRERERFpjUUlEREREWmNRSURERERaY1FJRERERFpjUUlEREREWmNRqYHa2lpERERg0KBB6NevH+bPn49Hjx61Ot+hQ4cwbNgwODg44JVXXkFqaqrg+WnTpsHGxkbwWLZsmWCa0tJShIWFwcXFBS4uLggLC0NpaalOt08M+sjp06dPERERgVGjRsHR0RFeXl5Yu3YtSkpKBMvw8fFRy/vmzZt1vYl619rxperSpUt45ZVX4ODggOHDhyMhIaHdy9R0vxkKXef0b3/7G8aPHw9nZ2cMHjwY8+bNw08//SSYZuXKlWrH46RJk3S+bWLQdT5jYmLUcuXh4SGYRqFQICYmBp6ennB0dMS0adPw3//+V+fbJhZd57S510MbGxvMnTtXOU1b8m6o2pPP/Px8hIaGYtSoUbC1tcXKlSubne7zzz9HYGAg+vTpg8DAQCQlJQmeN6ZjlEWlBjZs2ICkpCTEx8cjOTkZFRUVmDdvHmQy2XPnOX36NCIjI/GXv/wF3333HQICAhASEoKHDx8KpvvjH/+IW7duKR+7du0SPB8aGorr16/jxIkTOHnyJK5fv47w8HC9bGdH0kdOHz9+jMePH2PLli1ITU1FXFwcUlNTsXz5crVlrVu3TpD3d999V2/bqg9tPb4a3b9/H3PnzkVAQAC+++47rF27FuvWrcPnn3/ermVqst8MhT5yeunSJSxfvhznzp1DYmIiTE1NMWvWLDx9+lSwrFdffVVwPJ44cUKv29oR9JFPAHB3dxfkSrUI2LNnD/bt24cdO3bg22+/hb29PV5//XVUVFTobVs7ij5ympKSIsjnxYsXIZFIMGvWLMGyWsu7IWpvPmtra2Fra4s1a9bA39+/2WnS09OxbNkyhISE4Pvvv0dISAiWLFmCq1evKqcxpmOU96lsp7KyMri5uWHfvn3KT255eXnw8fHByZMnMXHixGbnmzhxIry8vLB3715ln5+fH2bOnIno6GgAz85UDh06FLGxsc0u49atWwgMDMTZs2cRFBQEAEhLS8OUKVOQkZEBd3d3XW5qh9FnTlV9/fXXmDdvHnJzc9GzZ08Azz6Zh4WFYfXq1Treso7T3lxER0cjKSkJ165dU/atXr0aN2/exDfffNOmZWq63wyFPnKqqrKyEi4uLjhy5AimTJkC4NmZypKSEhw7dkzHWyQufeQzJiYGiYmJSEtLa3adCoUCnp6eWLFihfKDYnV1Ndzd3fHee+9h6dKlutzEDtcRx+jOnTuxd+9e3Lx5Ez169ADQet4NlSbvKY3mzZsHW1tb/P3vfxf0L126FE+fPsWZM2eUfTNnzkTv3r0RHx9vdMcoz1S2U2ZmJurr6zFhwgRln5OTE1588UX88MMPzc5TV1eHzMxMwTwAMGHCBLV5Tp06hUGDBiEoKAibNm0SfFJJT0+HlZUVAgMDlX1BQUGwtLR87roNgb5z2lRFRQW6deumfHFs9OGHH2LgwIF46aWXsHPnTtTV1WmxRR1Lk1ykp6erTT9x4kT85z//QX19fZuWqcl+MxT6yGlzKisrIZfLYWNjI+hPS0uDm5sbRo4cibfeeguFhYVabI349JnP+/fvY8iQIRg2bBiWLVuG+/fvK5/Lzc1FQUGBYDndu3fHmDFjeIz+pqVjVKFQ4F//+hfmzZun9prZUt4NkabvKa3JyMhoNueNyzS2Y9RU7AAMzZMnTyCVSmFnZyfot7e3x5MnT5qdp7i4GDKZDPb29i3OExISAmdnZzg6OuLmzZvYsmULsrKylJ9wnjx5Ajs7O0gkEuU8EokEvXv3fu66DYE+c9pUaWkp/vrXv2LRokUwNf3foR8eHo5hw4bB1tYW165dw+bNm5Gbm4sPP/xQyy3rGJrk4smTJ3j11VfVpm9oaEBxcTEUCkWry9RkvxkKfeTU0dFRbZ7IyEj4+PggICBA2Tdp0iRMnz4dAwYMwIMHD7Bt2zbMmDEDFy5cQLdu3bTfOBHoK5/+/v74+OOP4e7ujqKiIsTGxuK1117DlStXYGtri4KCAuV8qst5/Pix7jZQBB1xjKakpCA3NxcLFy4U9LeWd0OkST7boqCgoMVlGtsxyqLyN9u2bcPOnTtbnEZ1cG1TCoVCUOw1R/V51XmWLFmi/L+XlxdcXV0xceJEZGZmwtfXt9lltHXdYugMOW1UVVWFBQsWoG/fvti6davguTfffFP5f29vb1hbW2Pp0qXYsmWLQb1AtjUXLU3f2N/0/+1ZZlunMRS6zKmqjRs34sqVKzh79iykUqmyf/bs2cr/e3l5wdfXFz4+Pjh37hxmzJih0XZ0FrrO5+9+9zvB8/7+/vD19cWnn34q+LvW5Dg2FPo8Rg8fPgw/Pz8MGzZM0N/WvBsifRwrbVmmsRyjLCp/s3LlSsG325rj5OSEjIwMyGQyFBcXo3fv3srnioqKMGbMmGbns7Ozg1QqVfu0U1RUpPbppKkRI0ZAKpUiJycHvr6+6NOnD4qKigQHm0KhQHFxcYvLEUtnyWllZSVCQkIAAMeOHYOFhUWLMY0cORIAkJOTYxBFpSbHV58+fZqd3tTUFLa2tlAoFK0us0+fPu3eb4ZCHzltasOGDTh9+jSSkpLg6uraYix9+/ZFv379kJOT0/4N6ST0nc9GVlZW8PT0VObKwcEBwLMzdE5OTm1ar6HQd04LCwuRnJzc6okBQD3vhkjT9+nWODg4tLhMYztGOabyN3Z2dvDw8Gjx0aNHD/j6+sLMzAwpKSnKeR89eqT8Ek1zzM3N4evrK5gHeHZp4XnzAEB2djZkMpnyoAsICEBlZSXS09OV06Snp6OqqqrF5YilM+S0oqICc+bMgVwux/Hjx2FlZdVq3Ddu3ADwvz/2zk6T4ysgIAAXLlxQm37EiBEwMzNr0zI12W+GQh85bbR+/XqcPHkSiYmJbboNS3FxMR4/fmwwx2Nz9JnPpmpqanDnzh1lrgYMGAAHBwfBemtqapCWlsZjtMn0zeX0008/Rbdu3RAcHNxqLKp5N0Savk+3ZtSoUS0u09iOUWlkZORmsYMwJBYWFsjPz8fBgwfh7e2NsrIyvPPOO+jZsye2bNkCE5NndfqoUaMA/O+sl7W1NWJiYuDo6AgLCwvExsYiNTUVH330EV544QXcu3cPBw4cgKWlJerq6pCeno41a9agf//+2LRpE0xMTNC7d29cvXoVJ0+exLBhw/Do0SO888478PPzM+jbCukrpxUVFQgODkZ5eTkSEhIgkUhQVVWFqqoqmJubQyqVIj09HWfOnIGFhQWqq6uRkpKC9evXY9y4cWr3CO3MWstFeHg4esQoPAAAAsJJREFUvvjiC0yfPh0AMHDgQOzevRuFhYVwdnZGcnIyPvjgA2zbtg2enp5tWmZb95uh0kdO3333XRw9ehT/+Mc/4OTkpDwegWdvapWVldi6dSusrKzQ0NCAGzduYPXq1ZDJZIiNjTXYMZWAfvK5adMmmJubQy6X4+7du4iIiEBOTg527dqFF154ARKJBDKZDLt27YKbmxtkMhmioqJQUFCA3bt3G3Q+Af3kFHh2BWzVqlWYPHmy2q2EgNbzbqjam08AuH79OgoKCvDll19CoVDAw8MDT58+VV696du3L7Zv3w4zMzPY2dnh8OHDOHLkCPbs2YN+/foZ3THKy98a2L59O6RSKZYuXYqamhq8/PLL2L9/v2Bc1J07d1BcXKxsBwcHo6SkBLGxsSgoKMCQIUNw/PhxuLi4AADMzMxw8eJF7N+/H1VVVejfvz9ee+01REZGCpZ78OBBrF+/XvnpccqUKXj//fc7aMv1Rx85zczMREZGBoD/FaKNkpKSMG7cOJibm+Ozzz7Djh07UFdXB2dnZyxatAhvv/12B2y17rSWi7y8PMH0rq6uOH78ODZu3IiEhAQ4Ojpix44dmDlzZpuXCbRtvxkqfeT00KFDACDoA56dvdywYQOkUil++uknHD16FGVlZXBwcMC4cePwySefwNraWs9brF/6yOcvv/yC0NBQ5RAMf39/fPPNN4Jj9O2330Z1dTUiIiJQWlqKkSNH4vTp0wafT0A/OQWA77//Hj///DMOHDjQ7HrbkndD1N58AsDLL78saJ89exbOzs7KK16BgYFISEjAtm3bEBMTg4EDByIhIUFwX0tjOkZ5n0oiIiIi0pphX58iIiIiok6BRSURERERaY1FJRERERFpjUUlEREREWmNRSURERERaY1FJRERERFpjUUlEREREWmNRSURERERaY1FJRERERFp7f8BYpP1DLEeJ48AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1bac4f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use('fivethirtyeight')\n",
    "plt.rcParams[\"figure.figsize\"] = (10, 8)\n",
    "\n",
    "# Graph the Frequentist results\n",
    "x = np.linspace(- 0.06, 0.06, 100, endpoint=True)\n",
    "pdf = [stats.norm.pdf(_, loc=0, scale=moe_N) for _ in x]\n",
    "plt.plot(x, pdf, 'k-')\n",
    "plt.figsize = (10, 8)\n",
    "\n",
    "_ = plt.axvline(ci_high, color='red')\n",
    "_ = plt.axvline(ci_low, color='red')\n",
    "_ = plt.axvline(ci_high_b, color ='green')\n",
    "_ = plt.axvline(ci_low_b, color ='green')\n",
    "_ = plt.axvline(ci_high_w, color ='orange')\n",
    "_ = plt.axvline(ci_low_w, color ='orange')\n",
    "_ = plt.axvline(0, color='blue')\n",
    "_ = plt.text(0.022, 23, '95% Confidence \\n     Interval')\n",
    "_ = plt.title('Frequentist Approach');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "obs diff: -0.032032854209445585\n",
      "\n",
      "BOOTSTRAP RESULTS\n",
      "p-value: 1.0\n",
      "95% conf. int.: [-0.0137577   0.01396304]\n"
     ]
    }
   ],
   "source": [
    "#Bootstrap\n",
    "# Construct arrays of data: white-sounding names, black-sounding names\n",
    "all_callb = np.array([True] * int(b_yes) + [False] * int(w_yes))\n",
    "prop_diff = b_p - w_p\n",
    "\n",
    "size = 10000\n",
    "\n",
    "bs_reps_diff = np.empty(size)\n",
    "\n",
    "for i in range(size):\n",
    "    w_bs_replicates = np.sum(np.random.choice(all_callb, size=w_n2))\n",
    "    b_bs_replicates = np.sum(np.random.choice(all_callb, size=b_n1))\n",
    "    \n",
    "    bs_reps_diff[i] = (b_bs_replicates - w_bs_replicates)/total_cases\n",
    "    \n",
    "bs_p_value = np.sum(bs_reps_diff >= prop_diff) / len(bs_reps_diff)\n",
    "\n",
    "bs_ci = np.percentile(bs_reps_diff, [2.5, 97.5])\n",
    "bs_mean_diff = np.mean(bs_reps_diff)\n",
    "\n",
    "\n",
    "print('obs diff: {}\\n'.format(prop_diff))\n",
    "print('BOOTSTRAP RESULTS\\np-value: {}\\n95% conf. int.: {}'.format(bs_p_value, bs_ci))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1f0383c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.hist(bs_reps_diff, bins=20)\n",
    "_ = plt.axvline(prop_diff, color='red')\n",
    "_ = plt.axvline(np.max(bs_mean_diff), color='blue')\n",
    "_ = plt.xlabel('% Diff')\n",
    "_ = plt.ylabel('Frequency')\n",
    "_ = plt.title('Empirical v Bootstrap Proportional Differences')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"span5 alert alert-success\">\n",
    "<p> Your answers to Q4 and Q5 here </p>\n",
    "</div>\n",
    "\n",
    "From my analysis, I concluded that my null hypothesis must be rejected with a 95% confidence level in favor of my alternative hypothesis. It proves that there is a preference from employers to callback resumes with white-sounding names than black-sounding names. The calculated margins of the errors add extra credibility to this analysis since they are within 1%. Even after doing a bootstrap replications for 10,000 samples, shows that the differences of the  empirical sample proportions and bootstraped sample proportions are within range. Thus, the empirical sample proportions could be used as good estimators of the actual population proportion.\n",
    "\n",
    "At this point, given that the researchers took the necessary steps to make this a RSS, will be interesting to see if other variables (education/experience) could play a role in not rejecting our null hypothesis.\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}