sliderule_dsi_inferential_statistics_exercise_3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hospital Readmissions Data Analysis and Recommendations for Reduction\n",
    "\n",
    "### Background\n",
    "In October 2012, the US government's Center for Medicare and Medicaid Services (CMS) began reducing Medicare payments for Inpatient Prospective Payment System hospitals with excess readmissions. Excess readmissions are measured by a ratio, by dividing a hospital’s number of “predicted” 30-day readmissions for heart attack, heart failure, and pneumonia by the number that would be “expected,” based on an average hospital with similar patients. A ratio greater than 1 indicates excess readmissions.\n",
    "\n",
    "### Exercise Directions\n",
    "\n",
    "In this exercise, you will:\n",
    "+ critique a preliminary analysis of readmissions data and recommendations (provided below) for reducing the readmissions rate\n",
    "+ construct a statistically sound analysis and make recommendations of your own \n",
    "\n",
    "More instructions provided below. Include your work **in this notebook and submit to your Github account**. \n",
    "\n",
    "### Resources\n",
    "+ Data source: https://data.medicare.gov/Hospital-Compare/Hospital-Readmission-Reduction/9n3s-kdb3\n",
    "+ More information: http://www.cms.gov/Medicare/medicare-fee-for-service-payment/acuteinpatientPPS/readmissions-reduction-program.html\n",
    "+ Markdown syntax: http://nestacms.com/docs/creating-content/markdown-cheat-sheet\n",
    "****"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "#import bokeh.plotting as bkp\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in readmissions data provided\n",
    "hospital_read_df = pd.read_csv('data/cms_hospital_readmissions.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "****\n",
    "## Preliminary Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# deal with missing and inconvenient portions of data \n",
    "clean_hospital_read_df = hospital_read_df[hospital_read_df['Number of Discharges'] != 'Not Available']\n",
    "clean_hospital_read_df.loc[:, 'Number of Discharges'] = clean_hospital_read_df['Number of Discharges'].astype(int)\n",
    "clean_hospital_read_df = clean_hospital_read_df.sort_values('Number of Discharges')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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zISIfA+4C9ojIgIj8ooi8QUTeABCF9/934AHgHuB9xpgHOz8NZS1SSMepNoIZ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\nBLWBt6/MsT3ICDyH9+71GOcSPemSaHP0SOhReNJC5ElQGuv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kpUIIgRGGuUpALy1sfIK2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SipWOsGJHlFXkBSlsS+y2LLo6rsLErLd9kepYA1Eaxqw7SouLASszuylgNJXuJFdiC5V9TsjVNe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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111fbffd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generate a scatterplot for number of discharges vs. excess rate of readmissions\n",
    "# lists work better with matplotlib scatterplot function\n",
    "x = [a for a in clean_hospital_read_df['Number of Discharges'][81:-3]]\n",
    "y = list(clean_hospital_read_df['Excess Readmission Ratio'][81:-3])\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8,5))\n",
    "ax.scatter(x, y,alpha=0.2)\n",
    "\n",
    "ax.fill_between([0,350], 1.15, 2, facecolor='red', alpha = .15, interpolate=True)\n",
    "ax.fill_between([800,2500], .5, .95, facecolor='green', alpha = .15, interpolate=True)\n",
    "\n",
    "ax.set_xlim([0, max(x)])\n",
    "ax.set_xlabel('Number of discharges', fontsize=12)\n",
    "ax.set_ylabel('Excess rate of readmissions', fontsize=12)\n",
    "ax.set_title('Scatterplot of number of discharges vs. excess rate of readmissions', fontsize=14)\n",
    "\n",
    "ax.grid(True)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "****\n",
    "\n",
    "## Preliminary Report\n",
    "\n",
    "Read the following results/report. While you are reading it, think about if the conclusions are correct, incorrect, misleading or unfounded. Think about what you would change or what additional analyses you would perform.\n",
    "\n",
    "**A. Initial observations based on the plot above**\n",
    "+ Overall, rate of readmissions is trending down with increasing number of discharges\n",
    "+ With lower number of discharges, there is a greater incidence of excess rate of readmissions (area shaded red)\n",
    "+ With higher number of discharges, there is a greater incidence of lower rates of readmissions (area shaded green) \n",
    "\n",
    "**B. Statistics**\n",
    "+ In hospitals/facilities with number of discharges < 100, mean excess readmission rate is 1.023 and 63% have excess readmission rate greater than 1 \n",
    "+ In hospitals/facilities with number of discharges > 1000, mean excess readmission rate is 0.978 and 44% have excess readmission rate greater than 1 \n",
    "\n",
    "**C. Conclusions**\n",
    "+ There is a significant correlation between hospital capacity (number of discharges) and readmission rates. \n",
    "+ Smaller hospitals/facilities may be lacking necessary resources to ensure quality care and prevent complications that lead to readmissions.\n",
    "\n",
    "**D. Regulatory policy recommendations**\n",
    "+ Hospitals/facilties with small capacity (< 300) should be required to demonstrate upgraded resource allocation for quality care to continue operation.\n",
    "+ Directives and incentives should be provided for consolidation of hospitals and facilities to have a smaller number of them with higher capacity and number of discharges."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "****\n",
    "### Exercise\n",
    "\n",
    "Include your work on the following **in this notebook and submit to your Github account**. \n",
    "\n",
    "A. Do you agree with the above analysis and recommendations? Why or why not?\n",
    "   \n",
    "B. Provide support for your arguments and your own recommendations with a statistically sound analysis:\n",
    "\n",
    "   1. Setup an appropriate hypothesis test.\n",
    "   2. Compute and report the observed significance value (or p-value).\n",
    "   3. Report statistical significance for $\\alpha$ = .01. \n",
    "   4. Discuss statistical significance and practical significance. Do they differ here? How does this change your recommendation to the client?\n",
    "   5. Look at the scatterplot above. \n",
    "      - What are the advantages and disadvantages of using this plot to convey information?\n",
    "      - Construct another plot that conveys the same information in a more direct manner.\n",
    "\n",
    "\n",
    "\n",
    "You can compose 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",
    "****"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A. Do you agree with the above analysis and recommendations? Why or why not?\n",
    "\n",
    "A: The above analysis only shows one graph. It's hard to tell the statistics it mentioned and the scatter plot itself does not show a strong correlation, nor to mention it's significance in p-value. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "B. Provide support for your arguments and your own recommendations with a statistically sound analysis:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Setup an appropriate hypothesis test.\n",
    "\n",
    "There's no significant correlation between number of discharges and excessive readmission ratio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Hospital Name</th>\n",
       "      <th>Provider Number</th>\n",
       "      <th>State</th>\n",
       "      <th>Measure Name</th>\n",
       "      <th>Number of Discharges</th>\n",
       "      <th>Footnote</th>\n",
       "      <th>Excess Readmission Ratio</th>\n",
       "      <th>Predicted Readmission Rate</th>\n",
       "      <th>Expected Readmission Rate</th>\n",
       "      <th>Number of Readmissions</th>\n",
       "      <th>Start Date</th>\n",
       "      <th>End Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>FROEDTERT MEMORIAL LUTHERAN HOSPITAL</td>\n",
       "      <td>520177</td>\n",
       "      <td>WI</td>\n",
       "      <td>READM-30-HIP-KNEE-HRRP</td>\n",
       "      <td>242</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.9095</td>\n",
       "      <td>10.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>38.0</td>\n",
       "      <td>07/01/2010</td>\n",
       "      <td>06/30/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PROVIDENCE HOSPITAL</td>\n",
       "      <td>90006</td>\n",
       "      <td>DC</td>\n",
       "      <td>READM-30-HIP-KNEE-HRRP</td>\n",
       "      <td>247</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.7521</td>\n",
       "      <td>9.2</td>\n",
       "      <td>5.3</td>\n",
       "      <td>33.0</td>\n",
       "      <td>07/01/2010</td>\n",
       "      <td>06/30/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BEAUFORT COUNTY MEMORIAL HOSPITAL</td>\n",
       "      <td>420067</td>\n",
       "      <td>SC</td>\n",
       "      <td>READM-30-HIP-KNEE-HRRP</td>\n",
       "      <td>586</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.5836</td>\n",
       "      <td>7.6</td>\n",
       "      <td>4.8</td>\n",
       "      <td>53.0</td>\n",
       "      <td>07/01/2010</td>\n",
       "      <td>06/30/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ADVOCATE CHRIST HOSPITAL &amp; MEDICAL CENTER</td>\n",
       "      <td>140208</td>\n",
       "      <td>IL</td>\n",
       "      <td>READM-30-HIP-KNEE-HRRP</td>\n",
       "      <td>965</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.5760</td>\n",
       "      <td>9.0</td>\n",
       "      <td>5.7</td>\n",
       "      <td>95.0</td>\n",
       "      <td>07/01/2010</td>\n",
       "      <td>06/30/2013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BRAZOSPORT REGIONAL HEALTH SYSTEM</td>\n",
       "      <td>450072</td>\n",
       "      <td>TX</td>\n",
       "      <td>READM-30-HIP-KNEE-HRRP</td>\n",
       "      <td>149</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.5308</td>\n",
       "      <td>8.2</td>\n",
       "      <td>5.4</td>\n",
       "      <td>20.0</td>\n",
       "      <td>07/01/2010</td>\n",
       "      <td>06/30/2013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Hospital Name  Provider Number State  \\\n",
       "0       FROEDTERT MEMORIAL LUTHERAN HOSPITAL           520177    WI   \n",
       "1                        PROVIDENCE HOSPITAL            90006    DC   \n",
       "2          BEAUFORT COUNTY MEMORIAL HOSPITAL           420067    SC   \n",
       "3  ADVOCATE CHRIST HOSPITAL & MEDICAL CENTER           140208    IL   \n",
       "4          BRAZOSPORT REGIONAL HEALTH SYSTEM           450072    TX   \n",
       "\n",
       "             Measure Name Number of Discharges  Footnote  \\\n",
       "0  READM-30-HIP-KNEE-HRRP                  242       NaN   \n",
       "1  READM-30-HIP-KNEE-HRRP                  247       NaN   \n",
       "2  READM-30-HIP-KNEE-HRRP                  586       NaN   \n",
       "3  READM-30-HIP-KNEE-HRRP                  965       NaN   \n",
       "4  READM-30-HIP-KNEE-HRRP                  149       NaN   \n",
       "\n",
       "   Excess Readmission Ratio  Predicted Readmission Rate  \\\n",
       "0                    1.9095                        10.8   \n",
       "1                    1.7521                         9.2   \n",
       "2                    1.5836                         7.6   \n",
       "3                    1.5760                         9.0   \n",
       "4                    1.5308                         8.2   \n",
       "\n",
       "   Expected Readmission Rate  Number of Readmissions  Start Date    End Date  \n",
       "0                        5.6                    38.0  07/01/2010  06/30/2013  \n",
       "1                        5.3                    33.0  07/01/2010  06/30/2013  \n",
       "2                        4.8                    53.0  07/01/2010  06/30/2013  \n",
       "3                        5.7                    95.0  07/01/2010  06/30/2013  \n",
       "4                        5.4                    20.0  07/01/2010  06/30/2013  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualize data\n",
    "hospital_read_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hospital Name                  object\n",
      "Provider Number                 int64\n",
      "State                          object\n",
      "Measure Name                   object\n",
      "Number of Discharges           object\n",
      "Footnote                      float64\n",
      "Excess Readmission Ratio      float64\n",
      "Predicted Readmission Rate    float64\n",
      "Expected Readmission Rate     float64\n",
      "Number of Readmissions        float64\n",
      "Start Date                     object\n",
      "End Date                       object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "#data type\n",
    "print (hospital_read_df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# deal with missing and inconvenient portions of data \n",
    "clean_hospital_read_df = hospital_read_df[hospital_read_df['Number of Discharges'] != 'Not Available']\n",
    "clean_hospital_read_df.loc[:, 'Number of Discharges'] = clean_hospital_read_df['Number of Discharges'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hospital Name                  object\n",
      "Provider Number                 int64\n",
      "State                          object\n",
      "Measure Name                   object\n",
      "Number of Discharges          float64\n",
      "Footnote                      float64\n",
      "Excess Readmission Ratio      float64\n",
      "Predicted Readmission Rate    float64\n",
      "Expected Readmission Rate     float64\n",
      "Number of Readmissions        float64\n",
      "Start Date                     object\n",
      "End Date                       object\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# now check data type again, Number of Discharges has turned into floating data type\n",
    "print (clean_hospital_read_df.dtypes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hospital Name                     0\n",
       "Provider Number                   0\n",
       "State                             0\n",
       "Measure Name                      0\n",
       "Number of Discharges              0\n",
       "Footnote                      11497\n",
       "Excess Readmission Ratio         81\n",
       "Predicted Readmission Rate       81\n",
       "Expected Readmission Rate        81\n",
       "Number of Readmissions           81\n",
       "Start Date                        0\n",
       "End Date                          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check missing value \n",
    "clean_hospital_read_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop missing value in Number of Discharges and Excess Readmission Ratio columns\n",
    "clean_hospital_read_df = clean_hospital_read_df.dropna(subset=['Number of Discharges', 'Excess Readmission Ratio'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Hospital Name                     0\n",
       "Provider Number                   0\n",
       "State                             0\n",
       "Measure Name                      0\n",
       "Number of Discharges              0\n",
       "Footnote                      11497\n",
       "Excess Readmission Ratio          0\n",
       "Predicted Readmission Rate        0\n",
       "Expected Readmission Rate         0\n",
       "Number of Readmissions            0\n",
       "Start Date                        0\n",
       "End Date                          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now there's no missing values in these two columns, we can start our analysis. \n",
    "clean_hospital_read_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_discharge = np.array(clean_hospital_read_df['Number of Discharges'])\n",
    "readmit_rate = np.array(clean_hospital_read_df['Excess Readmission Ratio'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('p-val =', 1)\n"
     ]
    }
   ],
   "source": [
    "def pearson_r(x, y):\n",
    "    \"\"\"Compute Pearson correlation coefficient between two arrays.\"\"\"\n",
    "    # Compute correlation matrix: corr_mat\n",
    "    corr_mat = np.corrcoef(x, y)\n",
    "\n",
    "    # Return entry [0,1]\n",
    "    return corr_mat[0,1]\n",
    "\n",
    "# Compute observed correlation: r_obs\n",
    "r_obs = pearson_r(n_discharge, readmit_rate)\n",
    "\n",
    "# Initialize permutation replicates: perm_replicates\n",
    "perm_replicates = np.empty(100000)\n",
    "\n",
    "# Draw replicates\n",
    "for i in range(10000):\n",
    "    # Permute illiteracy measurments: illiteracy_permuted\n",
    "    discharge_permuted = np.random.permutation(n_discharge)\n",
    "\n",
    "    # Compute Pearson correlation\n",
    "    perm_replicates[i] = pearson_r(discharge_permuted, readmit_rate)\n",
    "\n",
    "# Compute p-value: p\n",
    "p = np.sum(perm_replicates >= r_obs) / len(perm_replicates)\n",
    "print('p-val =', p)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#2. Compute and report the observed significance value (or p-value)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I got a p-value of one. In hacker statistics, this means that my p-value is very large. I got every single replicate in the 100000 I took that had a Pearson correlation greater than the observed one. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_bs_pairs_linreg(x, y, size=1):\n",
    "    \"\"\"Perform pairs bootstrap for linear regression.\"\"\"\n",
    "\n",
    "    # Set up array of indices to sample from: inds\n",
    "    inds = np.arange(len(x))\n",
    "\n",
    "    # Initialize replicates: bs_slope_reps, bs_intercept_reps\n",
    "    bs_slope_reps = np.empty(size)\n",
    "    bs_intercept_reps = np.empty(size)\n",
    "\n",
    "    # Generate replicates\n",
    "    for i in range(size):\n",
    "        bs_inds = np.random.choice(inds, size=len(inds))\n",
    "        bs_x, bs_y = x[bs_inds], y[bs_inds]\n",
    "        bs_slope_reps[i], bs_intercept_reps[i] = np.polyfit(bs_x, bs_y, 1)\n",
    "\n",
    "    return bs_slope_reps, bs_intercept_reps\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ -3.54542430e-05  -2.20654584e-05]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e5335d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate replicates of slope and intercept using pairs bootstrap\n",
    "bs_slope_reps, bs_intercept_reps = draw_bs_pairs_linreg(\n",
    "                    n_discharge, readmit_rate, size=1000)\n",
    "\n",
    "# Compute and print 95% CI for slope\n",
    "print(np.percentile(bs_slope_reps, [2.5, 97.5]))\n",
    "\n",
    "# Plot the histogram\n",
    "_ = plt.hist(bs_slope_reps, bins=50, normed=True)\n",
    "_ = plt.xlabel('slope')\n",
    "_ = plt.ylabel('PDF')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oHcJEH70G3A3co6pv51+k7nFYeZT9KYrMlSrpsrO37T7IL59cTyodKDg9Fs4/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\nw8eUgmEYhuFjSsEwDMPw+f+JoLR/CjiIGwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111e3a290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Generate array of x-values for bootstrap lines: x\n",
    "x = np.array([0, 100])\n",
    "\n",
    "# Plot the bootstrap lines\n",
    "for i in range(100):\n",
    "    _ = plt.plot(x, bs_slope_reps[i] * x + bs_intercept_reps[i],\n",
    "                 linewidth=0.5, alpha=0.01, color='red')\n",
    "\n",
    "# Plot the data\n",
    "_ = plt.plot(n_discharge, readmit_rate, marker='.', linestyle='none')\n",
    "\n",
    "# Label axes, set the margins, and show the plot\n",
    "_ = plt.xlabel('number of discharges')\n",
    "_ = plt.ylabel('escessive readmission rate')\n",
    "plt.margins(0.02)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I got a p-value of one. In hacker statistics, this means that my p-value is very large. I got every single replicate in the 100000 I took that had a Pearson correlation greater than the observed one. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A. Do you agree with the above analysis and recommendations? Why or why not?\n",
    "B. Provide support for your arguments and your own recommendations with a statistically sound analysis:\n",
    "Setup an appropriate hypothesis test.\n",
    "Compute and report the observed significance value (or p-value).\n",
    "Report statistical significance for  α = .01.\n",
    "Discuss statistical significance and practical significance. Do they differ here? How does this change your recommendation to the client?\n",
    "Look at the scatterplot above.\n",
    "What are the advantages and disadvantages of using this plot to convey information?\n",
    "Construct another plot that conveys the same information in a more direct manner.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_discharge_norm = n_discharge / n_discharge.max(axis=0)\n",
    "readmit_rate_norm = readmit_rate / readmit_rate.max(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t = 126.568942363\n",
      "p = 0.0\n"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "# two-way t-test\n",
    "t2, p2 = stats.ttest_ind(n_discharge,readmit_rate, equal_var=False)\n",
    "print(\"t = \" + str(t2))\n",
    "print(\"p = \" + str(2*p2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result from t-test shows that there's statsitical significance between number of discharges and excessive readmission ratio. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ecdf(data):\n",
    "    \"\"\"Compute ECDF for a one-dimensional array of measurements.\"\"\"\n",
    "    # Number of data points: n\n",
    "    n = len(data)\n",
    "\n",
    "    # x-data for the ECDF: x\n",
    "    x = np.sort(data)\n",
    "\n",
    "    # y-data for the ECDF: y\n",
    "    y = np.arange(1, n+1) / n\n",
    "\n",
    "    return x, y\n",
    "\n",
    "\"\"\"\n",
    "# Generate CDFs\n",
    "x_std1, y_std1 = ecdf(samples_std1)\n",
    "x_std3, y_std3 = ecdf(samples_std3)\n",
    "x_std10, y_std10 = ecdf(samples_std10)\n",
    "\n",
    "# Plot CDFs\n",
    "_ = plt.plot(x_std1, y_std1, marker='.', linestyle='none')\n",
    "_ = plt.plot(x_std3, y_std3, marker='.', linestyle='none')\n",
    "_ = plt.plot(x_std10, y_std10, marker='.', linestyle='none')\n",
    "\n",
    "# Make a legend and show the plot\n",
    "_ = plt.legend(('std = 1', 'std = 3', 'std = 10'), loc='lower right')\n",
    "plt.show()\n",
    "\"\"\""
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}