historical_adjustment.ipynb

{
 "metadata": {
  "name": "historical_adjustment"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import datetime\n",
      "\n",
      "import numpy as np\n",
      "import statsmodels.api as sm\n",
      "import matplotlib.pyplot as plt\n",
      "import pandas\n",
      "from scipy import stats\n",
      "np.set_printoptions(precision=4, suppress=True)\n",
      "pandas.set_printoptions(notebook_repr_html=False,\n",
      "                        precision=4,\n",
      "                        max_columns=12, column_space=10,\n",
      "                        max_colwidth=25)\n",
      "from matplotlib import rcParams\n",
      "#rcParams['text.usetex'] = False\n",
      "#rcParams['text.latex.unicode'] = False"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We have a snapshot for what would happen if the election is held today (Don't go bet on intratrade based on this model). Historically, polls have narrowed as the election nears."
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Set up some globals for dates"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "today = datetime.datetime(2012, 10, 2)\n",
      "election = datetime.datetime(2012, 11, 6)\n",
      "days_before = election - today\n",
      "date2004 = datetime.datetime(2004, 11, 2)\n",
      "days_before2004 = date2004 - days_before\n",
      "date2008 = datetime.datetime(2008, 11, 4)\n",
      "days_before2008 = date2008 - days_before"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "TODO: Put a basemap map here"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Load that data and clean"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "national_2004 = pandas.read_table(\"/home/skipper/school/talks/538model/data/2004_poll_data.csv\")\n",
      "national_2004.rename(columns={\"Poll\" : \"Pollster\"}, inplace=True);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "state_data2004 = pandas.read_csv(\"/home/skipper/school/talks/538model/data/2004-pres-polls.csv\")\n",
      "state_data2008 = pandas.read_csv(\"/home/skipper/school/talks/538model/data/2008-pres-polls.csv\")\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "state_data2004"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 879 entries, 0 to 878\n",
        "Data columns:\n",
        "State       879  non-null values\n",
        "Kerry       879  non-null values\n",
        "Bush        879  non-null values\n",
        "Date        879  non-null values\n",
        "Pollster    879  non-null values\n",
        "dtypes: int64(2), object(3)"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "state_data2004.rename(columns={\"Kerry\" : \"challenger\", \n",
      "                               \"Bush\" : \"incumbent\"}, \n",
      "                      inplace=True);\n",
      "state_data2004[\"dem_spread\"] = (state_data2004[\"challenger\"] - \n",
      "                                      state_data2004[\"incumbent\"])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "state_data2004.Date.replace({\"Nov 00\" : \"Nov 01\", \"Oct 00\" : \"Oct 01\"}, \n",
      "                            inplace=True);\n",
      "state_data2004.Date = (state_data2004.Date + \", 2004\").apply(\n",
      "                                                pandas.datetools.parse)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def median_date(row, year=\"2008\"):\n",
      "    dt1 = pandas.datetools.parse(row[\"Start\"] + \", \" + year)\n",
      "    dt2 = pandas.datetools.parse(row[\"End\"] + \", \" + year)\n",
      "    dates = pandas.date_range(dt1, dt2)\n",
      "    median_idx = int(np.median(range(len(dates)))+.5)\n",
      "    return dates[median_idx]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "state_data2008[\"Date\"] = state_data2008.apply(median_date, axis=1)\n",
      "del state_data2008[\"Start\"]\n",
      "del state_data2008[\"End\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "actual = national_2004.head(1)\n",
      "national_2004 = national_2004.ix[national_2004.index[~national_2004.Pollster.isin([\"Final Results\", \"RCP Average\"])]]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def split_median_date(row):\n",
      "    dt = row[\"Date\"]\n",
      "    dt1, dt2 = dt.split(\" - \")\n",
      "    dates = pandas.date_range(dt1 + \", 2004\", dt2 + \", 2004\")\n",
      "    median_idx = int(np.median(range(len(dates)))+.5)\n",
      "    return dates[median_idx]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "national_2004[\"Date\"] = national_2004.apply(split_median_date, axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "national_2004[\"dem_spread\"] = national_2004[\"Kerry (D)\"] - national_2004[\"Bush (R)\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "state_data2008"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "Int64Index: 1189 entries, 0 to 1188\n",
        "Data columns:\n",
        "State       1189  non-null values\n",
        "Obama       1189  non-null values\n",
        "McCain      1189  non-null values\n",
        "Pollster    1189  non-null values\n",
        "Date        1189  non-null values\n",
        "dtypes: int64(2), object(3)"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "state_data2008.rename(columns={\"Obama\" : \"challenger\", \n",
      "                               \"McCain\" : \"incumbent\"}, \n",
      "                      inplace=True);\n",
      "state_data2008[\"dem_spread\"] = (state_data2008[\"challenger\"] - \n",
      "                                      state_data2008[\"incumbent\"])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Clean the Pollster names"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pickle\n",
      "pollster_map = pickle.load(open(\n",
      "                 \"/home/skipper/school/talks/538model/data/pollster_map.pkl\", \"rb\"))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "state_data2004.Pollster.replace(pollster_map, inplace=True);\n",
      "state_data2008.Pollster.replace(pollster_map, inplace=True);\n",
      "national_2004.Pollster.replace(pollster_map, inplace=True);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Get the Pollster weights"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "These are old weights obtained from the 538 web site. New weights are not published anywhere to my knowledge."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weights = pandas.read_table(\"/home/skipper/school/talks/538model/\"\n",
      "                            \"data/pollster_weights.csv\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "state_data2004 = state_data2004.merge(weights, on=\"Pollster\", how=\"inner\");\n",
      "state_data2008 = state_data2008.merge(weights, on=\"Pollster\", how=\"inner\");"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "What's the Assertion?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def edit_tick_label(tick_val, tick_pos):\n",
      "    if tick_val  < 0:\n",
      "        text = str(int(tick_val)).replace(\"-\", \"Republican+\")\n",
      "    else:\n",
      "        text = \"Democrat+\"+str(int(tick_val))\n",
      "    return text"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pandas import lib\n",
      "from matplotlib.ticker import FuncFormatter\n",
      "fig, axes = plt.subplots(figsize=(12,8))\n",
      "\n",
      "data = national_2004[[\"Date\", \"dem_spread\"]]\n",
      "#data = data.ix[data.Date >= days_before2004]\n",
      "#data = pandas.concat((data, national_data2012[[\"Date\", \"dem_spread\"]]))\n",
      "    \n",
      "data.sort(\"Date\", inplace=True)\n",
      "dates = pandas.DatetimeIndex(data.Date).asi8\n",
      "\n",
      "x = data.dem_spread.values.astype(float)\n",
      "lowess_res = sm.nonparametric.lowess(x, dates, \n",
      "                                    frac=.2, it=3)[:,1]\n",
      "\n",
      "dates_x = lib.ints_to_pydatetime(dates)\n",
      "axes.scatter(dates_x, data[\"dem_spread\"])\n",
      "axes.plot(dates_x, lowess_res, color='r', lw=4)\n",
      "axes.yaxis.get_major_locator().set_params(nbins=12)\n",
      "axes.yaxis.set_major_formatter(FuncFormatter(edit_tick_label))\n",
      "axes.grid(False, axis='x')\n",
      "axes.hlines(-1.21, dates_x[0], dates_x[-1], color='black', lw=3)\n",
      "axes.vlines(datetime.datetime(2004, 8, 5), -20, 15, lw=3)\n",
      "axes.margins(0, .00)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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kAWD1auVzIiLSuJWVwWefOaqn09PtFBUZIYxIRBqaEogQ6tTJIjHRB4BhWFx5pWofRESk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\nDhi4XNCuXfO45sjLM6isDJx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7lZbCzJkufve7cAzD4ve/L+PGGz3H3FSpuOSSGtP2b74J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      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Let's look at the State Polls"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pandas import lib\n",
      "from matplotlib.ticker import FuncFormatter\n",
      "fig, axes = plt.subplots(figsize=(12,8))\n",
      "\n",
      "data = state_data2004[[\"Date\", \"dem_spread\"]]\n",
      "#data = data.ix[data.Date >= days_before2004]\n",
      "data = data.ix[data.Date >= datetime.datetime(2004, 7, 15)]\n",
      "#data = pandas.concat((data, national_data2012[[\"Date\", \"dem_spread\"]]))\n",
      "    \n",
      "data.sort(\"Date\", inplace=True)\n",
      "dates = pandas.DatetimeIndex(data.Date).asi8\n",
      "\n",
      "x = data.dem_spread.values.astype(float)\n",
      "lowess_res = sm.nonparametric.lowess(x, dates, \n",
      "                                    frac=.2, it=3)[:,1]\n",
      "\n",
      "dates_x = lib.ints_to_pydatetime(dates)\n",
      "axes.scatter(dates_x, data[\"dem_spread\"])\n",
      "axes.plot(dates_x, lowess_res, color='r', lw=4)\n",
      "axes.yaxis.get_major_locator().set_params(nbins=12)\n",
      "axes.yaxis.set_major_formatter(FuncFormatter(edit_tick_label))\n",
      "axes.grid(False, axis='x')\n",
      "axes.hlines(-1.21, dates_x[0], dates_x[-1], color='black', lw=3)\n",
      "axes.margins(0, .05)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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FfxERERGRCKDgLyIiIiISAcI6qs/MmTPp378/aWlpjB8/nvj4+HC+fFht2rSJ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\n4geU+IuIiIiI+IH/B2CfaUMrzWkTAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pandas import lib\n",
      "from matplotlib.ticker import FuncFormatter\n",
      "fig, axes = plt.subplots(figsize=(12,8))\n",
      "\n",
      "data = state_data2008[[\"Date\", \"dem_spread\"]]\n",
      "data = data.ix[data.Date >= datetime.datetime(2008, 7, 15)]\n",
      "#data = data.ix[data.Date >= days_before2008]\n",
      "#data = pandas.concat((data, national_data2012[[\"Date\", \"dem_spread\"]]))\n",
      "    \n",
      "data.sort(\"Date\", inplace=True)\n",
      "dates = pandas.DatetimeIndex(data.Date).asi8\n",
      "\n",
      "x = data.dem_spread.values.astype(float)\n",
      "lowess_res = sm.nonparametric.lowess(x, dates, \n",
      "                                    frac=.2, it=3)[:,1]\n",
      "\n",
      "dates_x = lib.ints_to_pydatetime(dates)\n",
      "axes.scatter(dates_x, data[\"dem_spread\"])\n",
      "axes.plot(dates_x, lowess_res, color='r', lw=4)\n",
      "axes.yaxis.get_major_locator().set_params(nbins=12)\n",
      "axes.yaxis.set_major_formatter(FuncFormatter(edit_tick_label))\n",
      "axes.grid(False, axis='x')\n",
      "axes.hlines(3.65, dates_x[0], dates_x[-1], color='black', lw=3)\n",
      "axes.vlines(datetime.datetime(2008, 8, 29), -45, 70, lw=3)\n",
      "axes.vlines(datetime.datetime(2008, 9, 24), -45, 70, lw=3)\n",
      "axes.margins(0, .0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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/31HqU7trtM/X3vWorbbal27HxMQQToZ9/qnyv0NOTg65ubkAPProo+Tk5ODz+XjggQfo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\nYII=\n"
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Clean the data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#loadpy https://raw.github.com/gist/3912533/d958b515f602f6e73f7b16d8bc412bc8d1f433d9/state_abbrevs.py;"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "states_abbrev_dict = {\n",
      "        'AK': 'Alaska',\n",
      "        'AL': 'Alabama',\n",
      "        'AR': 'Arkansas',\n",
      "        'AS': 'American Samoa',\n",
      "        'AZ': 'Arizona',\n",
      "        'CA': 'California',\n",
      "        'CO': 'Colorado',\n",
      "        'CT': 'Connecticut',\n",
      "        'DC': 'District of Columbia',\n",
      "        'DE': 'Delaware',\n",
      "        'FL': 'Florida',\n",
      "        'GA': 'Georgia',\n",
      "        'GU': 'Guam',\n",
      "        'HI': 'Hawaii',\n",
      "        'IA': 'Iowa',\n",
      "        'ID': 'Idaho',\n",
      "        'IL': 'Illinois',\n",
      "        'IN': 'Indiana',\n",
      "        'KS': 'Kansas',\n",
      "        'KY': 'Kentucky',\n",
      "        'LA': 'Louisiana',\n",
      "        'MA': 'Massachusetts',\n",
      "        'MD': 'Maryland',\n",
      "        'ME': 'Maine',\n",
      "        'MI': 'Michigan',\n",
      "        'MN': 'Minnesota',\n",
      "        'MO': 'Missouri',\n",
      "        'MP': 'Northern Mariana Islands',\n",
      "        'MS': 'Mississippi',\n",
      "        'MT': 'Montana',\n",
      "        'NA': 'National',\n",
      "        'NC': 'North Carolina',\n",
      "        'ND': 'North Dakota',\n",
      "        'NE': 'Nebraska',\n",
      "        'NH': 'New Hampshire',\n",
      "        'NJ': 'New Jersey',\n",
      "        'NM': 'New Mexico',\n",
      "        'NV': 'Nevada',\n",
      "        'NY': 'New York',\n",
      "        'OH': 'Ohio',\n",
      "        'OK': 'Oklahoma',\n",
      "        'OR': 'Oregon',\n",
      "        'PA': 'Pennsylvania',\n",
      "        'PR': 'Puerto Rico',\n",
      "        'RI': 'Rhode Island',\n",
      "        'SC': 'South Carolina',\n",
      "        'SD': 'South Dakota',\n",
      "        'TN': 'Tennessee',\n",
      "        'TX': 'Texas',\n",
      "        'UT': 'Utah',\n",
      "        'VA': 'Virginia',\n",
      "        'VI': 'Virgin Islands',\n",
      "        'VT': 'Vermont',\n",
      "        'WA': 'Washington',\n",
      "        'WI': 'Wisconsin',\n",
      "        'WV': 'West Virginia',\n",
      "        'WY': 'Wyoming'\n",
      "}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "state_data2004.State.replace(states_abbrev_dict, inplace=True);\n",
      "state_data2008.State.replace(states_abbrev_dict, inplace=True);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "state_data2004[\"days_until\"] = date2004 - state_data2004.Date\n",
      "state_data2008[\"days_until\"] = date2008 - state_data2004.Date"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#state_data2004 = state_data2004.drop(\n",
      "#                    state_data2004.index[state_data2004.days_until > days_before])\n",
      "#state_data2008 = state_data2008.drop(\n",
      "#                    state_data2008.index[state_data2008.days_until > days_before])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def exp_decay(days):\n",
      "    # defensive coding, accepts timedeltas\n",
      "    days = getattr(days, \"days\", days)\n",
      "    return .5 ** (days/30.)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "state_data2004[\"time_weight_oct2\"] = (days_before2004 - \n",
      "                                      state_data2004[\"Date\"]).apply(exp_decay)\n",
      "state_data2004[\"time_weight_election\"] = (date2004 -\n",
      "                                      state_data2004[\"Date\"]).apply(exp_decay)\n",
      "state_data2008[\"time_weight_oct2\"] = (days_before2008 - \n",
      "                                      state_data2008[\"Date\"]).apply(exp_decay)\n",
      "state_data2008[\"time_weight_election\"] = (date2008 -\n",
      "                                      state_data2008[\"Date\"]).apply(exp_decay)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def weighted_mean(group, weights_name):\n",
      "    weights = group[weights_name]\n",
      "    return np.sum(weights*group[\"dem_spread\"]/np.sum(weights))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Get weighted average State-level polls for Oct 2 and Election Day"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_state_averages(dframe, time_weight_name):\n",
      "    dframe_pollsters = dframe.groupby([\"State\", \"Pollster\"])\n",
      "    dframe_result = dframe_pollsters.apply(weighted_mean, time_weight_name)\n",
      "    dframe_result.name = \"dem_spread\"\n",
      "    dframe_result = dframe_result.reset_index()\n",
      "    dframe_result = dframe_result.merge(dframe[[\"Pollster\", \"Weight\"]],\n",
      "                          on=\"Pollster\")\n",
      "    return dframe_result.groupby(\"State\").apply(weighted_mean, \"Weight\")\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "oct2 = state_data2004.Date <= days_before2004\n",
      "state_polls_oct2_2004 = get_state_averages(state_data2004.ix[oct2], \"time_weight_oct2\")\n",
      "state_polls_election_2004 = get_state_averages(state_data2004, \"time_weight_election\")\n",
      "updated2004 = state_data2004.ix[~oct2].State.unique()\n",
      "updated2004.sort()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "oct2 = state_data2008.Date <= days_before2008\n",
      "state_polls_oct2_2008 = get_state_averages(state_data2008.ix[oct2], \"time_weight_oct2\")\n",
      "state_polls_election_2008 = get_state_averages(state_data2008, \"time_weight_election\")\n",
      "updated2008 = state_data2008.ix[~oct2].State.unique()\n",
      "updated2008.sort()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 34
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Get Economic Data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<table>\n",
      "    <thead>\n",
      "        <tr style=\"background: black; color: white; text-align: center\">\n",
      "            <th style=\"padding: 15px; border-right-color: white; text-align: center\">FRED Variable</th>\n",
      "            <th style=\"padding: 15px; border-left-color: white; text-align: center\">Explanation</th>\n",
      "        </tr>\n",
      "    </thead>\n",
      "    <tbody>\n",
      "        <tr>\n",
      "            <td><b>PAYEMS</b></td>\n",
      "            <td>Nonfarm-Payrolls (Job Growth)</td>\n",
      "        </tr>\n",
      "        <tr>\n",
      "            <td><b>PI</b></td>\n",
      "            <td>Personal Income</td>\n",
      "        </tr>\n",
      "        <tr>\n",
      "            <td><b>INDPRO</b></td>\n",
      "            <td>Industrial Production</td>\n",
      "        </tr>\n",
      "        <tr>\n",
      "            <td><b>PCEC96</b></td>\n",
      "            <td>Consumption</td>\n",
      "        </tr>\n",
      "        <tr>\n",
      "            <td><b>CPIAUCSL</b></td>\n",
      "            <td>Inflation</td>\n",
      "        </tr>\n",
      "    </tbody>\n",
      "</table>"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pandas.io.data import DataReader"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "series = dict(jobs = \"PAYEMS\",\n",
      "              income = \"PI\",\n",
      "              prod = \"INDPRO\",\n",
      "              cons = \"PCEC96\",\n",
      "              prices = \"CPIAUCSL\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "try:\n",
      "    indicators = []\n",
      "    for variable in series:\n",
      "        data = DataReader(series[variable], \"fred\", start=\"2000-10-1\")\n",
      "        data.rename(columns={series[variable] : variable}, inplace=True)\n",
      "        indicators.append(data)\n",
      "    indicators = pandas.concat(indicators, axis=1)\n",
      "    indicators.to_csv(\"/home/skipper/school/talks/538model/tmp_indicators_full.csv\")\n",
      "except: # probably not online\n",
      "    indicators = pandas.read_csv(\"/home/skipper/school/talks/538model/tmp_indicators_full.csv\", \n",
      "                                 parse_dates=True)\n",
      "    indicators.set_index(\"DATE\", inplace=True)\n",
      "    # why doesn't it do this automaticall?\n",
      "    indicators.index = pandas.DatetimeIndex(indicators.index)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 37
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For stock variables, just compute annualized quarterly growth rates (end - beginning)/beginning * 400 and average."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "quarterly_growth = np.log(indicators.resample(\"Q\", \n",
      "                          how=\"mean\")).diff() * 400\n",
      "annualized = quarterly_growth.resample(\"A\", how=\"mean\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "quarterly_growth = quarterly_growth.dropna()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 39
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Try to be rigorous about what the voters know at the time of election."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "econ2004 = quarterly_growth.ix[:15].resample('A', 'mean').mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "econ2008 = quarterly_growth.ix[15:31].resample('A', 'mean').mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 41
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Leave out last quarter 2008 because that's on Bush? Do voters see it that way...?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "econ2012 = quarterly_growth.ix[32:].resample('A', 'mean').mean()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 42
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For flow variables, sum the quarters and get annualized quarter over quarter changes then average."
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Get Demographic Data"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Partisan voting index"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pvi = pandas.read_csv(\"/home/skipper/school/talks/538model/data/partisan_voting.csv\")\n",
      "pvi.set_index(\"State\", inplace=True);\n",
      "pvi.PVI = pvi.PVI.replace({\"EVEN\" : \"0\"})\n",
      "pvi.PVI = pvi.PVI.str.replace(\"R\\+\", \"-\")\n",
      "pvi.PVI = pvi.PVI.str.replace(\"D\\+\", \"\")\n",
      "pvi.PVI = pvi.PVI.astype(float)\n",
      "pvi.PVI"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "State\n",
        "Alabama                -13\n",
        "Alaska                 -13\n",
        "Arizona                 -6\n",
        "Arkansas                -9\n",
        "California               7\n",
        "Colorado                 0\n",
        "Connecticut              7\n",
        "Delaware                 7\n",
        "District of Columbia    39\n",
        "Florida                 -2\n",
        "Georgia                 -7\n",
        "Hawaii                  12\n",
        "Idaho                  -17\n",
        "Illinois                 8\n",
        "Indiana                 -6\n",
        "Iowa                     1\n",
        "Kansas                 -12\n",
        "Kentucky               -10\n",
        "Louisiana              -10\n",
        "Maine                    5\n",
        "Maryland                 9\n",
        "Massachusetts           12\n",
        "Michigan                 4\n",
        "Minnesota                2\n",
        "Mississippi            -10\n",
        "Missouri                -3\n",
        "Montana                 -7\n",
        "Nebraska               -13\n",
        "Nevada                   1\n",
        "New Hampshire            2\n",
        "New Jersey               4\n",
        "New Mexico               2\n",
        "New York                10\n",
        "North Carolina          -4\n",
        "North Dakota           -10\n",
        "Ohio                    -1\n",
        "Oklahoma               -17\n",
        "Oregon                   4\n",
        "Pennsylvania             2\n",
        "Rhode Island            11\n",
        "South Carolina          -8\n",
        "South Dakota            -9\n",
        "Tennessee               -9\n",
        "Texas                  -10\n",
        "Utah                   -20\n",
        "Vermont                 13\n",
        "Virginia                -2\n",
        "Washington               5\n",
        "West Virginia           -8\n",
        "Wisconsin                2\n",
        "Wyoming                -20\n",
        "Name: PVI, Length: 51"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Gallup party affiliation (Poll Jan.-Jun. 2012)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "party_affil = pandas.read_csv(\"/home/skipper/school/talks/538model/\"\n",
      "                              \"data/gallup_electorate.csv\")\n",
      "party_affil.Democrat = party_affil.Democrat.str.replace(\"%\", \"\").astype(float)\n",
      "party_affil.Republican = party_affil.Republican.str.replace(\"%\", \"\").astype(float)\n",
      "party_affil.set_index(\"State\", inplace=True);\n",
      "party_affil.rename(columns={\"Democrat Advantage\" : \"dem_adv\"}, inplace=True);\n",
      "party_affil[\"no_party\"] = 100 - party_affil.Democrat - party_affil.Republican\n",
      "party_affil[[\"dem_adv\", \"no_party\"]]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 44,
       "text": [
        "                      dem_adv  no_party\n",
        "State                                  \n",
        "District of Columbia    66.30       8.3\n",
        "Rhode Island            26.00      21.0\n",
        "Hawaii                  25.60      17.0\n",
        "New York                21.20      17.2\n",
        "Maryland                20.20      12.2\n",
        "Massachusetts           19.10      14.1\n",
        "Delaware                17.40      16.4\n",
        "Connecticut             15.40      15.8\n",
        "Vermont                 13.90      16.3\n",
        "California              13.70      17.1\n",
        "Illinois                12.60      15.8\n",
        "New Jersey              11.50      16.7\n",
        "Michigan                11.10      15.7\n",
        "Minnesota               10.20      13.4\n",
        "Washington               9.80      14.8\n",
        "Oregon                   8.10      13.7\n",
        "Pennsylvania             5.20      12.4\n",
        "Maine                    4.40      16.8\n",
        "New Mexico               3.60      14.2\n",
        "Ohio                     3.60      15.4\n",
        "West Virginia            3.40      12.8\n",
        "Wisconsin                2.80      12.8\n",
        "Iowa                     1.80      15.4\n",
        "Florida                  0.70      14.7\n",
        "Arkansas                 0.70      17.7\n",
        "Kentucky                 0.40      13.4\n",
        "North Carolina           0.20      13.4\n",
        "New Hampshire           -1.50      13.9\n",
        "Virginia                -3.00      14.6\n",
        "Missouri                -3.90      15.9\n",
        "Georgia                 -4.00      15.4\n",
        "Nevada                  -4.20      17.4\n",
        "Louisiana               -4.80      14.6\n",
        "Colorado                -5.20      15.0\n",
        "Texas                   -5.80      17.6\n",
        "South Dakota            -6.00      11.0\n",
        "Indiana                 -6.70      15.3\n",
        "Mississippi             -7.00      12.8\n",
        "Arizona                 -7.50      12.9\n",
        "Tennessee               -8.40      15.4\n",
        "Alaska                  -8.44      19.8\n",
        "Oklahoma                -9.40      13.4\n",
        "South Carolina         -11.90      14.3\n",
        "North Dakota           -13.20      15.2\n",
        "Alabama                -13.60      14.4\n",
        "Montana                -13.70      14.5\n",
        "Kansas                 -16.90      14.3\n",
        "Nebraska               -19.00      14.8\n",
        "Wyoming                -29.90      16.7\n",
        "Idaho                  -30.30      14.7\n",
        "Utah                   -39.30      11.7"
       ]
      }
     ],
     "prompt_number": 44
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Census data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "census_data_2012 = pandas.read_csv(\"/home/skipper/school/talks/\"\n",
      "                              \"538model/data/census_demographics.csv\")\n",
      "def capitalize(s):\n",
      "    s = s.title()\n",
      "    s = s.replace(\"Of\", \"of\")\n",
      "    return s\n",
      "census_data_2012[\"State\"] = census_data_2012.state.map(capitalize)\n",
      "del census_data_2012[\"state\"]\n",
      "census_data_2012.set_index(\"State\", inplace=True);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 45
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "census_data_2000 = pandas.read_csv(\"/home/skipper/school/talks/\"\n",
      "                                   \"538model/data/census_data_2000.csv\")\n",
      "census_data_2000.set_index(\"State\", inplace=True);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "census_data_2005 = (census_data_2000 + census_data_2012) / 2."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 47
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Model reversion to the \"mean\""
     ]
    },
    {
     "cell_type": "heading",
     "level": 5,
     "metadata": {},
     "source": [
      "A little more data preparation"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "changes_2004 = state_polls_election_2004.ix[updated2004].sub(\n",
      "                    state_polls_oct2_2004)\n",
      "changes_2004 = changes_2004.dropna()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 48
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "changes_2008 = state_polls_election_2008.ix[updated2008].sub(\n",
      "                    state_polls_oct2_2008)\n",
      "changes_2008 = changes_2008.dropna()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 49
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "changes_2004"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 50,
       "text": [
        "Alabama          -1.533\n",
        "Arizona          -2.196\n",
        "Arkansas         -0.278\n",
        "California       -0.022\n",
        "Colorado         -0.615\n",
        "Florida           0.170\n",
        "Georgia           2.119\n",
        "Illinois          3.262\n",
        "Indiana           2.566\n",
        "Iowa              0.154\n",
        "Kansas            1.640\n",
        "Kentucky         -3.251\n",
        "Maine             3.151\n",
        "Maryland          2.609\n",
        "Michigan         -0.471\n",
        "Minnesota         0.363\n",
        "Missouri         -1.095\n",
        "Montana           0.495\n",
        "Nevada           -1.676\n",
        "New Hampshire     0.689\n",
        "New Jersey        3.254\n",
        "New Mexico       -2.374\n",
        "New York          2.350\n",
        "North Carolina   -0.304\n",
        "Ohio              2.134\n",
        "Oklahoma         -1.377\n",
        "Oregon            3.295\n",
        "Pennsylvania      0.210\n",
        "Rhode Island     -2.322\n",
        "South Carolina   -0.051\n",
        "South Dakota      0.009\n",
        "Tennessee        -3.368\n",
        "Texas            -0.620\n",
        "Virginia         -1.851\n",
        "Washington       -0.155\n",
        "West Virginia    -0.254\n",
        "Wisconsin        -0.022"
       ]
      }
     ],
     "prompt_number": 50
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "changes_2008"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 51,
       "text": [
        "Alabama           1.409\n",
        "Alaska            1.032\n",
        "Arizona           4.619\n",
        "Arkansas          0.016\n",
        "California        6.518\n",
        "Colorado          1.487\n",
        "Connecticut       1.022\n",
        "Delaware          4.191\n",
        "Florida           2.063\n",
        "Georgia           2.469\n",
        "Illinois          1.660\n",
        "Indiana           1.171\n",
        "Iowa              1.709\n",
        "Kansas            0.727\n",
        "Kentucky          1.636\n",
        "Louisiana        -0.125\n",
        "Maine             3.708\n",
        "Massachusetts     4.180\n",
        "Michigan          1.890\n",
        "Minnesota         1.464\n",
        "Mississippi       0.339\n",
        "Missouri          5.364\n",
        "Montana           0.403\n",
        "Nevada            2.548\n",
        "New Hampshire     6.714\n",
        "New Jersey        0.812\n",
        "New Mexico        2.179\n",
        "New York          5.212\n",
        "North Carolina    4.124\n",
        "Ohio              3.271\n",
        "Oklahoma          1.392\n",
        "Oregon            5.742\n",
        "Pennsylvania      3.088\n",
        "South Carolina    1.726\n",
        "South Dakota      1.904\n",
        "Tennessee         1.873\n",
        "Texas            -0.120\n",
        "Utah              0.236\n",
        "Vermont           0.008\n",
        "Virginia          2.594\n",
        "Washington        3.398\n",
        "West Virginia     0.667\n",
        "Wisconsin         3.615\n",
        "Wyoming          -0.821"
       ]
      }
     ],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for name in econ2004.index:\n",
      "    census_data_2000[name] = econ2004.ix[name]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 52
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for name in econ2008.index:\n",
      "    census_data_2005[name] = econ2008.ix[name]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "census_data_2000[\"poll_change\"] = changes_2004\n",
      "census_data_2005[\"poll_change\"] = changes_2008\n",
      "#changes_2008 = changes_2008.join(census_data_2005)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#years = pandas.DataFrame([2004]*len(changes_2004), columns=[\"Year\"], index=changes_2004.index)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#years[\"poll_change\"] = changes_2004\n",
      "#changes_2004 = years"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#years = pandas.DataFrame([2008]*len(changes_2008), columns=[\"Year\"], index=changes_2008.index)\n",
      "#years[\"poll_change\"] = changes_2008\n",
      "#changes_2008 = years"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#changes_2004"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#changes_2004 = changes_2004.join(census_data_2000, how=\"left\")\n",
      "#changes_2008 = changes_2008.join(census_data_2000, how=\"left\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "census_data_2000[\"year\"] = 2004\n",
      "census_data_2005[\"year\"] = 2008"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 60
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "changes = pandas.concat((census_data_2000.reset_index(), census_data_2005.reset_index()))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 61
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "changes.reset_index(drop=True, inplace=True);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "changes = changes.dropna() # don't have polls for all the states"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "predict = census_data_2012.reset_index()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "predict[\"year\"] = 2012"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 65
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Add in Partisan information"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "changes = changes.merge(pvi.reset_index(), on=\"State\")\n",
      "predict = predict.merge(pvi.reset_index(), on=\"State\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 66
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Add in Party affiliation information"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "changes = changes.merge(party_affil[[\"dem_adv\", \"no_party\"]].reset_index(), on=\"State\")\n",
      "predict = predict.merge(party_affil[[\"dem_adv\", \"no_party\"]].reset_index(), on=\"State\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 67
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Do the K-means clustering for similar states"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.cluster import vq\n",
      "from sklearn import cluster"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clstr_dta = predict[[\"per_black\", \"per_hisp\", \"per_white\", \"educ_coll\", \"pop_density\", \"per_older\", \"PVI\", \"dem_adv\"]].values"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 69
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "clstr_dta = vq.whiten(clstr_dta) # might want to play with this to emphasize dimensions?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "kmeans = cluster.KMeans(n_clusters=7, n_init=100)\n",
      "kmeans.fit(clstr_dta)\n",
      "values = kmeans.cluster_centers_\n",
      "labels = kmeans.labels_"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 71
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "predict[\"kmeans_groups\"] = labels"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 72
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for key, grp in predict.groupby(\"kmeans_groups\"): print key, grp.State.tolist()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0 ['Arizona', 'California', 'Nevada', 'New Mexico', 'Texas']\n",
        "1 ['Iowa', 'Maine', 'Michigan', 'Minnesota', 'New Hampshire', 'Ohio', 'Oregon', 'Pennsylvania', 'Vermont', 'Wisconsin']\n",
        "2 ['District of Columbia']\n",
        "3 ['Arkansas', 'Idaho', 'Indiana', 'Kansas', 'Kentucky', 'Missouri', 'Montana', 'Nebraska', 'North Dakota', 'Oklahoma', 'South Dakota', 'Tennessee', 'West Virginia', 'Wyoming']\n",
        "4 ['Alabama', 'Georgia', 'Louisiana', 'Mississippi', 'North Carolina', 'South Carolina']\n",
        "5 ['Colorado', 'Connecticut', 'Delaware', 'Florida', 'Hawaii', 'Illinois', 'Maryland', 'Massachusetts', 'New Jersey', 'New York', 'Rhode Island', 'Virginia', 'Washington']\n",
        "6 ['Alaska', 'Utah']\n"
       ]
      }
     ],
     "prompt_number": 73
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "changes = changes.merge(predict[[\"kmeans_groups\", \"State\"]], on=\"State\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 74
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Drop D.C. because it's not in the training data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "predict.set_index([\"State\", \"year\"], inplace=True);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 75
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "predict = predict.drop((\"District of Columbia\", 2012))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 76
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Let's explore some hypotheses"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "changes.set_index([\"State\", \"year\"], inplace=True);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 77
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from statsmodels.formula.api import ols"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 78
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "changes"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 79,
       "text": [
        "<class 'pandas.core.frame.DataFrame'>\n",
        "MultiIndex: 81 entries, ('Alabama', 2004) to ('Wyoming', 2008)\n",
        "Data columns:\n",
        "average_income    81  non-null values\n",
        "cons              81  non-null values\n",
        "educ_coll         81  non-null values\n",
        "educ_hs           81  non-null values\n",
        "income            81  non-null values\n",
        "jobs              81  non-null values\n",
        "median_income     81  non-null values\n",
        "older_pop         81  non-null values\n",
        "per_black         81  non-null values\n",
        "per_hisp          81  non-null values\n",
        "per_older         81  non-null values\n",
        "per_vote          81  non-null values\n",
        "per_white         81  non-null values\n",
        "poll_change       81  non-null values\n",
        "pop_density       81  non-null values\n",
        "prices            81  non-null values\n",
        "prod              81  non-null values\n",
        "vote_pop          81  non-null values\n",
        "PVI               81  non-null values\n",
        "dem_adv           81  non-null values\n",
        "no_party          81  non-null values\n",
        "kmeans_groups     81  non-null values\n",
        "dtypes: float64(21), int64(1)"
       ]
      }
     ],
     "prompt_number": 79
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "changes[[\"dem_adv\", \"PVI\"]].corr()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 80,
       "text": [
        "         dem_adv    PVI\n",
        "dem_adv    1.000  0.877\n",
        "PVI        0.877  1.000"
       ]
      }
     ],
     "prompt_number": 80
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "formula = (\"poll_change ~ C(kmeans_groups) + per_older*per_white + \"\n",
      "           \"per_hisp + no_party*np.log(median_income) + PVI\")\n",
      "mod = ols(formula, data=changes).fit()\n",
      "print mod.summary()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "                            OLS Regression Results                            \n",
        "==============================================================================\n",
        "Dep. Variable:            poll_change   R-squared:                       0.498\n",
        "Model:                            OLS   Adj. R-squared:                  0.400\n",
        "Method:                 Least Squares   F-statistic:                     5.103\n",
        "Date:                Sat, 27 Oct 2012   Prob (F-statistic):           3.47e-06\n",
        "Time:                        12:46:19   Log-Likelihood:                -147.99\n",
        "No. Observations:                  81   AIC:                             324.0\n",
        "Df Residuals:                      67   BIC:                             357.5\n",
        "Df Model:                          13                                         \n",
        "==================================================================================================\n",
        "                                     coef    std err          t      P>|t|      [95.0% Conf. Int.]\n",
        "--------------------------------------------------------------------------------------------------\n",
        "Intercept                       -129.4535     67.810     -1.909      0.061      -264.802     5.896\n",
        "C(kmeans_groups)[T.1]              2.1833      1.534      1.423      0.159        -0.879     5.246\n",
        "C(kmeans_groups)[T.3]              2.8481      1.575      1.808      0.075        -0.296     5.992\n",
        "C(kmeans_groups)[T.4]              2.2163      1.644      1.348      0.182        -1.065     5.498\n",
        "C(kmeans_groups)[T.5]              0.3561      1.197      0.297      0.767        -2.033     2.746\n",
        "C(kmeans_groups)[T.6]              1.7437      1.952      0.893      0.375        -2.152     5.639\n",
        "per_older                         -0.3697      0.380     -0.974      0.334        -1.127     0.388\n",
        "per_white                         -0.0950      0.055     -1.733      0.088        -0.204     0.014\n",
        "per_older:per_white                0.0064      0.004      1.425      0.159        -0.003     0.015\n",
        "per_hisp                           0.0276      0.056      0.490      0.626        -0.085     0.140\n",
        "no_party                           4.9148      4.124      1.192      0.238        -3.317    13.147\n",
        "np.log(median_income)             13.4102      6.651      2.016      0.048         0.136    26.685\n",
        "no_party:np.log(median_income)    -0.4960      0.402     -1.235      0.221        -1.298     0.306\n",
        "PVI                                0.1257      0.046      2.747      0.008         0.034     0.217\n",
        "==============================================================================\n",
        "Omnibus:                        1.558   Durbin-Watson:                   1.886\n",
        "Prob(Omnibus):                  0.459   Jarque-Bera (JB):                1.217\n",
        "Skew:                           0.054   Prob(JB):                        0.544\n",
        "Kurtosis:                       2.410   Cond. No.                     2.92e+05\n",
        "==============================================================================\n",
        "\n",
        "The condition number is large, 2.92e+05. This might indicate that there are\n",
        "strong multicollinearity or other numerical problems.\n"
       ]
      }
     ],
     "prompt_number": 107
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hyp = \", \".join(mod.model.exog_names[:5])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 82
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print hyp"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Intercept, C(kmeans_groups)[T.1], C(kmeans_groups)[T.3], C(kmeans_groups)[T.4], C(kmeans_groups)[T.5]\n"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print mod.f_test(hyp)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "<F test: F=array([[ 1.6831]]), p=[[ 0.1507]], df_denom=67, df_num=5>\n"
       ]
      }
     ],
     "prompt_number": 84
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "predicted2012 = pandas.read_csv(\"/home/skipper/school/talks/538model/2012-predicted.csv\")\n",
      "predicted2012[\"year\"] = 2012\n",
      "predicted2012 = predicted2012.set_index([\"State\", \"year\"])[\"poll\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "predicted_change = pandas.Series(mod.predict(predict), index=predict.index)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 86
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "predicted_change"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 87,
       "text": [
        "State           year\n",
        "Alabama         2012    2.408\n",
        "Alaska          2012    1.938\n",
        "Arizona         2012    4.496\n",
        "Arkansas        2012    1.388\n",
        "California      2012    7.027\n",
        "Colorado        2012    3.907\n",
        "Connecticut     2012    5.086\n",
        "Delaware        2012    4.265\n",
        "Florida         2012    3.977\n",
        "Georgia         2012    4.918\n",
        "Hawaii          2012    9.279\n",
        "Idaho           2012    1.652\n",
        "Illinois        2012    4.905\n",
        "Indiana         2012    3.014\n",
        "Iowa            2012    2.634\n",
        "Kansas          2012    3.370\n",
        "Kentucky        2012    1.863\n",
        "Louisiana       2012    3.549\n",
        "Maine           2012    1.658\n",
        "Maryland        2012    9.220\n",
        "Massachusetts   2012    5.800\n",
        "Michigan        2012    3.936\n",
        "Minnesota       2012    5.226\n",
        "Mississippi     2012    3.455\n",
        "Missouri        2012    2.945\n",
        "Montana         2012    2.046\n",
        "Nebraska        2012    2.599\n",
        "Nevada          2012    4.072\n",
        "New Hampshire   2012    4.682\n",
        "New Jersey      2012    5.568\n",
        "New Mexico      2012    6.087\n",
        "New York        2012    4.985\n",
        "North Carolina  2012    4.740\n",
        "North Dakota    2012    1.645\n",
        "Ohio            2012    2.841\n",
        "Oklahoma        2012    3.058\n",
        "Oregon          2012    4.968\n",
        "Pennsylvania    2012    5.191\n",
        "Rhode Island    2012    1.271\n",
        "South Carolina  2012    3.645\n",
        "South Dakota    2012    3.876\n",
        "Tennessee       2012    2.552\n",
        "Texas           2012    3.181\n",
        "Utah            2012    3.259\n",
        "Vermont         2012    3.415\n",
        "Virginia        2012    4.519\n",
        "Washington      2012    4.241\n",
        "West Virginia   2012    1.071\n",
        "Wisconsin       2012    4.811\n",
        "Wyoming         2012    0.955"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "results = predicted2012 + predicted_change\n",
      "results"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 88,
       "text": [
        "State           year\n",
        "Alabama         2012       NaN\n",
        "Alaska          2012       NaN\n",
        "Arizona         2012    -1.855\n",
        "Arkansas        2012       NaN\n",
        "California      2012    26.821\n",
        "Colorado        2012    10.854\n",
        "Connecticut     2012    19.053\n",
        "Delaware        2012       NaN\n",
        "Florida         2012     6.057\n",
        "Georgia         2012    -4.050\n",
        "Hawaii          2012    40.512\n",
        "Idaho           2012       NaN\n",
        "Illinois        2012    31.774\n",
        "Indiana         2012    -3.856\n",
        "Iowa            2012     4.959\n",
        "Kansas          2012    -6.171\n",
        "Kentucky        2012       NaN\n",
        "Louisiana       2012       NaN\n",
        "Maine           2012    14.393\n",
        "Maryland        2012    38.075\n",
        "Massachusetts   2012    27.616\n",
        "Michigan        2012    12.497\n",
        "Minnesota       2012    13.273\n",
        "Mississippi     2012    -5.182\n",
        "Missouri        2012     0.970\n",
        "Montana         2012    -4.989\n",
        "Nebraska        2012    -6.064\n",
        "Nevada          2012    13.094\n",
        "New Hampshire   2012     3.549\n",
        "New Jersey      2012    19.113\n",
        "New Mexico      2012    15.232\n",
        "New York        2012    28.191\n",
        "North Carolina  2012     4.150\n",
        "North Dakota    2012    -7.493\n",
        "Ohio            2012     7.224\n",
        "Oklahoma        2012       NaN\n",
        "Oregon          2012    14.085\n",
        "Pennsylvania    2012    10.883\n",
        "Rhode Island    2012    27.202\n",
        "South Carolina  2012    -3.122\n",
        "South Dakota    2012     2.739\n",
        "Tennessee       2012    -0.331\n",
        "Texas           2012     0.603\n",
        "Utah            2012   -25.883\n",
        "Vermont         2012    20.227\n",
        "Virginia        2012     9.504\n",
        "Washington      2012    20.359\n",
        "West Virginia   2012    -8.706\n",
        "Wisconsin       2012     9.720\n",
        "Wyoming         2012       NaN"
       ]
      }
     ],
     "prompt_number": 88
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "electoral_votes = pandas.read_csv(\"/home/skipper/school/seaboldgit/talks/pydata/data/electoral_votes.csv\")\n",
      "electoral_votes.sort(\"State\", inplace=True).reset_index(drop=True, inplace=True);\n",
      "red_states = [\"Alabama\", \"Alaska\", \"Arkansas\", \"Idaho\", \"Kentucky\", \"Louisiana\",\n",
      "              \"Oklahoma\", \"Wyoming\"]\n",
      "blue_states = [\"Delaware\"]#, \"District of Columbia\"]\n",
      "results.name = \"Poll\"\n",
      "results = results.reset_index()\n",
      "results = results.merge(electoral_votes, on=\"State\", how=\"left\").set_index(\"State\")\n",
      "results[\"obama\"] = 0\n",
      "results[\"romney\"] = 0\n",
      "results.ix[results[\"Poll\"] > 0, [\"obama\"]] = 1\n",
      "results.ix[results[\"Poll\"] < 0, [\"romney\"]] = 1\n",
      "results.ix[red_states, [\"romney\"]] = 1\n",
      "results.ix[blue_states, [\"obama\"]] = 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 89
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print results[\"Votes\"].mul(results[\"obama\"]).sum() + 3\n",
      "print results[\"Votes\"].mul(results[\"romney\"]).sum() "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "398\n",
        "140\n"
       ]
      }
     ],
     "prompt_number": 90
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "CCPR Plots"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Component-Component plus residual plots. Partial residual plots attempt to show the relationship between a given independent variable and the response variable given that other independent variables are also in the model."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from statsmodels.graphics.regressionplots import plot_ccpr_ax\n",
      "fig, ax = plt.subplots(figsize=(12,8))\n",
      "fig = plot_ccpr_ax(mod, 11, ax=ax)\n",
      "ax = fig.axes[0]\n",
      "ax.set_title(\"log(median_income)*B_11 + Resid vs log(median_income)\");"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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uu+8+fP755w5/P9u3b8fo0aOV4zZt2iAwMNDh15k2bZrS3SYrKwtxcXGq1xk/fjxuueUW\nhIeHY+rUqTCZTA6foyU1NTUwmUyIiopSxjp37oxrr70Wn3zyiTI2ePBgvPvuu6iqqsKmTZucek9q\n/N6CgoJUPyONZR2ff/45Tp48ieuuu07593v77bdV/37NY+/sexIA7Nu3DytXrsT8+fNhNptx4sQJ\n1eOdO3dW/jssLAznz5/3SJcRe+9933zzDbp162a369LmzZtV/34AEB0drSpnaYy9rd8HrfnZaeTv\n749+/fqp3vPIuOTfUku6JZrVijY/bt++Pd544w3ExcWha9euSEhIwPTp062+Vs+ePfHLL784NY/A\nwEDcfPPNFm2CGn8BdejQwW5da2v9+uuvGDVqFCIiInD77bdj6NChyMzMdPl13aH59+fn59diCyoh\nhMu/NAMDA3H33XfjySefVI1ffPHFFs89deoU/Pz8rN5w13jDX2hoKIYOHYqysjIsX75cE91Kmrr4\n4ouV7y0gIACXXHIJQkNDnXqtrl27YtSoUZg9ezbmzp2LvLw85bHAwED0798fn332meprmn4ACgoK\nwqeffopvv/0Wr776Km655RY8++yzmDdvXqvn0KFDB7ckTnfeeSeWLl2K6upqZGVlYenSparH/f39\nsX79eixduhSvv/467rrrLsyYMaPVNe2OsPa+1LTWfO3atRg2bBhWrVqFW265Bf/4xz+svo4r70kB\nAQEIDg7GV199pbpJMSAgQPlvd7wvCSEwdepUFBYWYtasWRg3bpxT9cne0vieY+/GzeYxqa+vd+he\ngdb87DR16tQpm/XhZCxcYSaPuPHGG1FbW6taLdy+fTuGDBmCSy+9FEDDG9/atWuxYcMGnDhxAq+8\n8orNVZTIyEgcPHjQqblMnjwZOTk56Nixo5J0hYaGKufq0aMHevXqhS+//FL5muarwk3ZenN+9913\n0bFjR3z88ceYO3cubrjhBqfm6wnN5zxkyBB8+umnFjcCNurRowcGDx6MnJwcZayx37MjN8bdeeed\n2LJliyruoaGhVn85HTx4ENdcc41qFcqWiy++WBM9Z1vijpv+li5div/973+qtnK33XYbfvjhB1RW\nVqriau2DSN++fZGeno5//vOfeP311x069+jRo1WJekVFhVP9oHv37o1BgwZh9erVOHbsGGJiYqw+\nr1evXnjsscfw1ltvYePGjQ6fx56AgACMHz9elSiVlZXhm2++Ua0iv/3227jrrrtw8uRJfPDBB7j6\n6qutvp4z70mNyd7111+PTp064ciRI6p/v6bX/ujRo1U3o9l7T7KlqKgIH3zwAXbv3o1HH30Ud9xx\nh1sWBzxh8ODBKCsrw9atW20+Z+LEiap/v/r6emzbtg1xcXHKWEs/c4787Jw9exY//fQTIiMjnfiO\nSG+YMJNb1NXVqVai/P39MXHiRMyaNQs7d+7E+++/jwULFqjujC8pKcGBAwdw6623ok2bNnYToMjI\nSBw4cMBivOmbv61fBI2/rCdNmoTdu3fjxIkTMJlMqKqqUp4zbtw4PPTQQygoKMCWLVvw2GOP2ZyL\nrfMMGDAAJ06cwHfffYcvvvgCcXFxCAgIQGVlpc3X8pbmc546dSquvPJK3Hrrrfjwww9x8uRJfPbZ\nZ6q5xsXF4ZFHHkF2djZycnJw7733qn4xtcbIkSNRU1ODadOmoaCgAEePHsWnn35q9bmHDh2y++fY\nuro6/Pzzz3j99dexfPlyh1t5VVZWorS0FL///jvMZjN+/fVXi3KYM2fOoLS0FGazGb/99htKS0st\nuiO0Vk5ODi6//HKnvrapLl26IC0tDSkpKfj1118BABdddBFuv/123Hnnnfjoo49w+vRp5OTkKI8D\nDWU1H3/8MU6fPo38/HysX78ed911l0PnHjduHN555x2sWbMGxcXFmDJlitPX89SpU7Fo0SJMnjwZ\nbdq0UT32+++/47333sOJEydQWFiI1atXOzzX5mpray268sTFxeHZZ5/FunXr8OWXX2Ly5MkYO3Ys\nOnbsqDznlVdewcyZM3HRRRehtrbW5s/7yJEjcfjw4RYT0MbHmz9vypQpuP/++/HGG2+gtLQUe/bs\nUXXnGTduHF588UX8+9//xqFDh5zaLKlXr14IDAxEXl4evv/+e8ycORPHjh1T/Rt6IoG29Zr2znX5\n5Zdjzpw5uP/++/HSSy/h9OnT2Lt3r6o1Y1xcHNauXYsXXngBhw4dUjoS9e3b1+Icts7Vmp+dRocO\nHcJVV12Fbt26ter7Jn1jwkwu8fPzw549e9CuXTuLN/Rly5Zh0KBBmDJlCh566CHEx8fjkUceUR6P\niIhAdHQ0QkJCEBAQgICAAPTo0QMrV660OE9sbCyKi4stdrVruppgb2Xh/fffR3BwMBISEjB8+HC8\n8sorqjfI9PR0hISEICoqCpmZmXj22Wdtvp6tdmGjR4/GlClTEBkZiUcffRTPP/88wsPDrbZRa+m1\nWsveHO2dp02bNti5cyf69euHBx54AIMGDcLLL7+sanv1t7/9DRMmTMD8+fMxbdo0XHfddXjuuecc\nmpO/vz9ycnJw7tw5REdHIyoqCuvXr1d9WAEaVnLWr1+PmTNnWn29n376Ce3atcMVV1yBf//733j5\n5ZcdbgGXnp6O0NBQPPLII9i7dy9CQkIwadIk1XOGDh2K0NBQ7Nu3D/PmzUNoaKgmyj7uuece9O3b\nFwsXLlTGMjIyMHToUPz5z3/GwIED8cILL+Cnn35SHj9+/DiWL1+Ofv36ITExEWPHjrVot9jI1nX4\nhz/8AY899hgWL16MP/7xj0hMTHS6xCQhIQFmsxnTpk2zeOzUqVN47bXXMGzYMCQkJCAiIsKihaSj\n2rdvj7CwMNXY9OnTMX/+fPz973/H+PHj0a1bN4u2cM888wyGDx+uvCc17i7ZXI8ePRAdHW2xq13T\nWNr6b6ChBVpcXBwWL16Mvn37YsmSJfjhhx+Ux6dNm4apU6ciMTERs2fPtvue1Pj6zXXs2BErV67E\nvHnzEBcXh3HjxiEhIUH1nmTt397Vv4w48r7U1KpVqzB79mysWrUKV155JRYtWqRqG/eHP/wBy5cv\nx6uvvoqRI0fi3Llzqn7szeNtS0s/O42ef/55zJkzp+VvmAzBT2j17zOke99++y0effRRrFmzBt27\ndwfQ0Fd0wYIFVpPM/Px83HfffdizZw+CgoK8PV3yoKlTp9pN6Ii8oaamBnFxcXjyySeVm4CLi4tx\n9dVX4+DBg6qVTKDhg97o0aPxxhtvYODAgTKmTB7yyiuvYOfOnXjrrbdkT4U0wu4K8+HDh5GSkmJR\n92Y2m/HnP/9Z9afVffv24ZlnnkFaWprXtyYl33Tw4EGUlpbi7NmzKC0txWeffYZly5bZrG8cNmwY\nFixY4JEbgbSgrq4OtbW1Nv/X/M/LerF792507drV6WTZXszs/UmdqLnTp0+joKAAtbW1KC8vx969\ne7Fo0SL07t0bV1xxhcXzO3fujHfeeQeLFy+WMFvPE0K0+POlR+fPn8dbb72FdevWyZ4KaYjdFeZv\nvvkGVVVVOHLkiGpns08++QSFhYUYOHAgYmNjAQCPPfYYnn76aZw/fx4vv/wyUlJSPD978nkpKSn4\n5JNPcOLECURGRiImJgaJiYkWNY5NVVVV2byj2ZeNHTvWYie2pnr27Gl1B0JfV19fj/r6eoud7Fqj\npKQEffr0sfucxx9/HE8//bSz0yOD2bhxIzIzM3HgwAEMGTIE0dHRmDNnjnKzsjV6fU9asmSJ3Z8d\nPz8/fPfddy3+DPqi6upqp1ookn61WJJRWFiI/fv3KwlzdXU1li9fjptuuglVVVVKwrxmzRqMGDEC\n5eXl+Pnnn5GQkGD19az1XyUiIiIi8oTo6GiXX8PhJZ0tW7YgNjYW5eXlqvEhQ4bggw8+gNlsxp13\n3mn3NYYNG+boaYmIiIiIHJKfn++W13GoS0ZFRQWKioowZMgQ1fjZs2dhMpmwaNEiLF68GO+8845H\ndgki12m5ab3eMfZyMf5yMf7yMPZyMf760OIKc9OKjaKiIpjNZqxYsQKnTp1CXV0dBg4ciM6dO7eq\nHy4RERERka+xW8OcnZ2NgoIClJeXo3///khMTFQe27FjB6qrqzF+/HgAwLZt21BQUAA/Pz+MGjXK\nZo/U7du3sySDiIiIiDwuPz/fLTXMXu/DzISZiIiIiLzBXQkzd/ozGNZSycPYy8X4y8X4y8PYy8X4\n6wMTZiIiIiIiO1iSQURERES6xJIMIiIiIiIvYMJsMKylkoexl4vxl4vxl4exl4vx1wcmzERERERE\ndrCGmYiIiIh0iTXMRERERERewITZYFhLJQ9jLxfjLxfjLw9jLxfjrw9MmImIiIiI7GANMxERERHp\nEmuYiYiIiIi8gAmzwbCWSh7GXi7GXy7GXx7GXi7GXx+YMBMRERER2cEaZiIiIiLSJdYwExERERF5\nARNmg2EtlTyMvVyMv1yMvzyMvVyMvz4wYSYiIiIisoM1zERERESkS6xhJiIiIiLyAibMBsNaKnkY\ne7kYf7kYf3kYe7kYf31gwkxEREREZAdrmImIiIhIl1jDTERERETkBUyYDYa1VPIw9nIx/nIx/vLo\nIfYmUx7i49MwYcILiI9Pg8mUJ3tKraaH+BPQVvYEiIiIiGwxmfKQkrIbxcXpylhJSQoAICZmhKxp\nkcGwhpmIiIg0Kz4+DTk56RbjUVHJyMpaKGFG5EtYw0xERES6V1PTzup4dTX/SE7ew4TZYFhLJQ9j\nLxfjLxfjL4+vxz4gwGx1PDCw1sszcY6vx58aMGEmIiIizUpKGoOIiBTVWHh4MhITR0uaERkRa5iJ\niIhI00ymPGRm7kJ1dVsEBtYiMXE0b/ijVnFXDTMLgIiIiEjTYmJGMEEmqViSYTCspZKHsZeL8ZeL\n8ZeHsZeL8dcHJsxERERERHawhpmIiIiIdIl9mImIiIiIvIAJs8Gwlkoexl4uxl8uxl8exl4uxl8f\nmDATEREREdnBGmYiIiIi0iXWMBMREREReQETZoNhLZU8jL1cjL9cjL88jL1cjL8+MGEmIiIiIrLD\nbg3z4cOHsWHDBvTv3x8zZsxQxs1mMx588EFMnDgRsbGxAICzZ89ixYoVqK6uRv/+/XHXXXdZfU3W\nMBMRERGRN7irhrmtvQfNZjMmTZqEI0eOqMa3bt2KPn36qMbee+89REVFITIy0uVJERERERFphd2E\nefDgwSgsLFSNVVdX45tvvsFNN92EqqoqZfzo0aO49957W3XS3NxcJbFurO3hsXeOV69ejUGDBmlm\nPkY6blrHpoX5GO2Y8Wf8jXrcOKaV+RjtuHFMK/Mx2nFQUBDcocW2coWFhdi/f79SkpGdnY3w8HCU\nl5ejqqoKsbGxqKiowKJFi3DVVVfh/PnziI2NxaBBg6y+Hksy5MrNvfBhhbyLsZeL8ZeL8ZeHsZeL\n8ZdLSlu5iooKFBUVYciQIarxoKAghIaGYtasWZg3bx6ysrJQX1/v8uTI/fhDKw9jLxfjLxfjLw9j\nLxfjrw9tW3pC0wXooqIimM1mrFixAqdOnUJdXR0GDhyInj17olu3bjhz5gx69OiBNm3aeHTSRERE\nRETeYjdhzs7ORkFBAcrLy1FZWYnExESlnGLHjh2orq5Gz549AQDTp0/H+++/j+LiYowbNw7+/uxY\np0X805A8jL1cjL9cjL88jL1cjL8+2E2Y4+LiEBcXZ/WxsWPHqo47duyIu+++220TIyIiIiLSghZv\n+nM33vRHRERERN4g5aY/IiIiIiKjYcJsME37QpJ3MfZyMf5yMf7yMPZyMf76wISZiIiIiMgO1jAT\nERERkS6xhpmIiIiIyAuYMBsMa6nkYezlYvzlYvzlYezlYvz1gQkzEREREZEdrGEmIiIiIl1iDTMR\nERERkRcwYTYY1lLJw9jLxfjLxfjLw9jLxfjrQ1vZEyAiIiJ9MpnykJGxEzU17RAQYEZS0hjExIyQ\nPS0ih7GGmYiIiNzOZMpDSspuFBenKmMRESlITR3FpJm8hjXMREREpFkZGTtVyTIAFBenIjNzl6QZ\nETmPCbPBsJZKHsZeLsZfLsZfHlmxr6lpZ3W8utpY1aC89vWBCTMRERG5XUCA2ep4YGCtl2dC5Dom\nzAYTGRl0b3BhAAAgAElEQVQpewqGxdjLxfjLxfjLIyv2SUljEBGRohoLD09GYuJoKfORpbXxz/4u\nG8Erg3Hs3DEPz4icYay/ixAREZFXNN7Yl5mZjOrqtggMrEVi4mje8NdMXX0dhq4fiuO/Hcd1l16H\nXp16yZ4SWcEVZoNhLZU8jL1cjL9cjL88MmMfEzMCWVkLsXnzo8jKWmjIZNle/P/7838RsioEx387\njqw7srB1ylb4+fl5cXbUWlxhJiIiIvIiIQSmbZ4GU4kJHdp1wPdzvkdg20DZ0yI72IeZiIiIyEuK\ny4tx3YbrAAAroldgxoAZkmekb+7qw8wVZiIiIiIveDL3SbyY/yIA4IfEH9C1fVfJM6LWYg2zwbCO\nUB7GXi7GXy7GXx7GXq7c3FyUVZYheGUwXsx/EQ9e9yDK5pcxWfYxXGEmIiIi8pBHih7B9/nfAwDy\nZ+YjvHO43AmRU1jDTERERORm56rPITwjXDkum18mbzIG5q4aZpZkEBEREbnRol2LlGT52dHPMlnW\nASbMBsNaNnkYe7kYf7kYf3kYe++pF/UIXhmM1QWrAQC//OUXDPx9oORZkTswYSYiIiJy0btF7+KS\nFy8BAMT3jUfZ/DIEtAmQPCtyF9YwExERka6ZTHnIyNiJmpp2CAgwIylpjFt3HQxeGaz895HZRxAS\nFOK21ybXsA8zERERUQtMpjykpOxGcXG6MlZSkgIALifN+SfzcfM7NwMAQi4KwZE5R1x6PdIulmQY\nDGvZ5GHs5WL85WL85TF67DMydqK4OFU1VlyciszMXS69bu9XeivJ8o6pO2wmy0aPv15whZmIiIh0\nq6amndXx6mrnUqBfzv+C/q/2V47ZAcMYuMJsMJGRkbKnYFiMvVyMv1yMvzxGj31AgNnqeGBgrcOv\ndffHdyvJ8mu3vtaqZNno8dcLrjATERGRbiUljUFJSYqqLCM8PBmJiaNb/RrVtdUIezlMOf513q/w\n8/Nz6zxJ27jCbDCspZKHsZeL8ZeL8ZenpdibTHmIj0/DhAkvID4+DSZTnlPP0aqYmBFITR2FqKhk\njBz5OKKikpGWNrrVN/yt3L9SSZYfuu4hlM0vcyhZ5rWvD1xhJiIiMqjWdJDwZJcJb4mJGeHwXIUQ\n6PZiN+X42P3H0DGgo7unRj6CK8wGw1oqeRh7uRh/uRh/eezFvjUdJDzVZULLtpVsU5Llm3rchLL5\nZU4ny7z29YErzERERAbVmg4S7u4yoXVNNyH5auZX6N25t8TZkFZwhdlgWEslD2MvF+MvF+Mvj73Y\nt6aDhDu7TGjZd2e+UyXLZfPL3JIs89rXBybMREREBpWUNAYRESmqseYdJFrzHF836s1RuPH1GwEA\nH/7xQ/ZWJgt+QgjhzRNu374dw4YN8+YpiYiIyAaTKQ+ZmbtQXd0WgYG1SEy07CDRmuf4onPV5xCe\nEa4cM1HWn/z8fERHR7v8OnYT5sOHD2PDhg3o378/ZsyYoYybzWY8+OCDmDhxImJjY1scb4oJMxER\nEcliMuUhI2Mn8m7IRFVQOQAgfUw65lw7R/LMyBPclTDbLckwm82YNGmSxfjWrVvRp08fiz6EtsZJ\nO1hLJQ9jLxfjLxfjLw9jf4HJlIfkx3YiZ8w/lGQ5fMMC9D450GPnZPz1wW7CPHjwYHTsqG6jUl1d\njW+++QbXX389mi5O2xonIiIi0oL78+ai5O5/NBxUXQwsESj53z903SKP3MPhnjBbtmxBbGwsysvL\nWzVuTW5urtKXsPGTF4+9c9w4ppX5GOk4MjJSU/Mx2jHjz/jz2NjHwSuDga5osOwYcK4XGlVXt5U+\nPx575jgoKAju0OJNf4WFhdi/fz9mzJiBiooKrFy5EsnJydixYweqqqoQGxtrc9wa1jATERGRt6z+\najUW7V50YWCJZdoTFZWMrKyFXpwVeYtXapgBqMorioqKYDabsWLFCmzduhU7duzAjz/+aHX8+PHj\nLk+O3K/xExd5H2MvF+MvF+Mvj5FjH7wyWEmWX4l5BW/3+9jrLfKMHH89aWvvwezsbBQUFKC8vByV\nlZVITExUVod37NiB6upq9OrVC7169bIY79mzp+dnT0RERNRM/sl83PzOzcqx0i6uX8P/ZWYm665F\nHnkW+zATERGRbjTdrS++bzzWxK6ROBuSzV0lGXZXmImIiIh8wdnqs4jIiFCOS+eVwt+PGxqTe/BK\nMhjWUsnD2MvF+MvF+MtjhNj3fLmnKlkum1+mmWTZCPE3Aq4wExERkU8SQqDbi92U4+/mfIduF3Wz\n8xVEzmENMxERkR2NWynX1LRDQIAZSUljeJOYBjzyn0fwr4P/Uo6VG/uImmANMxERkYeZTHlISdmN\n4uJ0ZaykpKEtGZNmeZre2Pdx/McYcRn/LciztFHgQ17DWip5GHu5GH+5fDX+GRk7UVycqhorLk71\nqa2UnYm9yZSH+Pg0TJjwAuLj02Ay5XlgZo7b/P1mVbJcNr9M88myr177pMYVZiIiIhtqatpZHa+u\n1u+vT62uqjdNlB8f8TgeueERaXMh49HvTzxZ1bjHOnkfYy8X4y+Xr8Y/IMBsdTwwsNbLM3Geo7Fv\nWFVPV401rKonuyVhdrQm/EjZEYzYeOFxX6tV9tVrn9SYMBMREdmQlDQGJSUpqrIMT2+lLJsnV9Ud\nXb1uuqoM+F6yTPrBGmaDYS2VPIy9XIy/XL4a/5iYEUhNHYWoqGSMHPk4oqKSkZbmW1spOxp7T66q\nt7YmvKq2SpUsH5973GeTZV+99kmNK8xERER2xMSM8KkE2VWeXFVvzep195e6o6auRjn21USZ9IUJ\ns8Gwlkoexl4uxl8uxl8eR2Pf+OEgMzMZ1dVtERhYi8RE96yqt7R63XRVOXd6Lvpf0t/lc8rGa18f\nmDATEZFmcdMQOTy1qm5r9TrgjkKLdnFEWsKE2WByc3P5aVcSxl4uxl8uZ+Kv1fZmvkZL17611ev/\njE5HyZmGx/8x9h+YPXi2xBm6n5biT85jwkxERJrk6fZmJEfj6vX2o9uR8EGCMs5VZdIyJswGw0+5\n8jD2cjH+cjkTfyNuGuIJWrz2m5ZfXN/9epjuNEmcjWdpMf7kOL7rEBGRJulh0xBSO/H7CQx8baBy\nXDqvFP5+7HBL2ser1GDYD1Iexl4uxl8uZ+KflDQGEREpqjG9bxriCVq59oNXBquS5bL5ZYZIlrUS\nf3INV5iJiEiTPNnejLynXtTjkhcvUY4P3XsIYR3DJM6IyHF+QgjhzRNu374dw4YN8+YpiYiIrGLb\nOs/i1tYkW35+PqKjo11+Ha4wExGRIbFtnWc1TZZXRK/AjAEzJM6GyDX6Lx4iFdZSycPYy8X4y6XF\n+De0rUtVjTW0rdslaUbuZzLlITp6ISZMeAHx8WkwmfI8fs5Hcx612ITEyMmyFq99chxXmImIyJD0\n3rbuwgp6pjLm6RX0ponytSHXImdajkfOQ+RtXGE2GPaDlIexl4vxl0uL8dd72zpvrqDvOb7HYlWZ\nyXIDLV775Dh9fIwmIiJyUFLSGJSUpKiSSj21rfPWCjpv7CMj4AqzwbCWSh7GXi7GXy4txj8mZgRS\nU0chKioZI0c+jqioZKSl6adtnadX0H+r+U2VLP8490cmy1Zo8donx3GFmYiIDCsmZoRuEuTmPLmC\nzlVlMhr2YSYiItIpkykPmZm73LrxS9Nk+dOETzE8bLir0yTyGPZhJiIiIrvcuYJ+0+s34dsz3yrH\nXFUmI2ENs8Gwlkoexl4uxl8uxl8ed8Q+eGWwkizPHTKXybIDeO3rA1eYiYiIyKo1X6/Bwp0LlWMm\nymRUrGEmIiIiC7yxj/SANcxERETkdoWlhYh888JmG7/O+xV+fn4SZ0QkH2uYDYa1VPIw9nIx/nIx\n/vI4EvvglcGqZLlsfhmTZRfx2tcHrjATEREZXG19LUJXhSrH++/ej4guERJnZCwmUx4yMnaipqYd\nAgLMSEoao9v+4L6KNcxEREQGxlplz2lNImwy5SElZbdqg5mIiBSkpo5i0uwGrGEmIiIilzRNlv96\nw1/x2IjHJM5GXy4kwunKWElJCgCoEuGMjJ2q5wBAcXEqMjOTmTBrCGuYDYa1VPIw9nIx/nIx/vJY\ni33IiyGqZLlsfhmTZTdrSIRTVWMNifAu1VhNTTurX19dzTVNLeG/BhERkYGwBMM7WpsIBwSYrT4v\nMLDW7XMi53GF2WAiIyNbfhJ5BGMvF+MvF+MvT2Ps13y9xmJVmcmy57Q2EU5KGoOIiBTVWHh4MhIT\nR3tsbuQ4rjATEZHmsYuAa7iq7H1JSWNQUpKiKsuwlgg3XseZmcmorm6LwMBaJCaO5vWtMUyYDSY3\nN5crPZIw9nIx/nK5Ev/W3jxFlk78fgIDXxuoHB+9/yg6BXSSOCPjaLw209OTEBQUYjcRjokZwWtZ\n4+wmzIcPH8aGDRvQv39/zJgxQxk3m8148MEHMXHiRMTGxgIA1q1bh2PHjqFDhw6477770LVrV8/O\nnIiIDIFdBJzDVWX5YmJGICiojh/WdcBuDbPZbMakSZMsxrdu3Yo+ffqodv+555578OSTT2L06NHY\ntm2b+2dKbsEfWnkYe7kYf7lciT+7CDiuabK8ZvwaJssS8b1HH+wmzIMHD0bHjh1VY9XV1fjmm29w\n/fXXw9qeJx07dkRtrf07O5u2uMnNzeUxj3nMYx7z2OaxrZunKipOa2J+WjoOXhmsSpY/HPYh4q+O\n18z8eMxjWceuanGnv8LCQuzfv18pycjOzkZ4eDjKy8tRVVWllGQ0WrNmDW677TZcdtllVl+PO/3J\nlZvLOk5ZGHu5GH+5XIm/tZ3QwsOTkZbGG6Oaapooh3UIw6H7DgHgtS8b4y+XlJ3+KioqUFRUhLi4\nOOzYscPi8S+//BKXXXaZzWSZiIjIUewiYN+tm27FFz9/oRyz/ILI/VpcYT506BDy8/MxY8YM5Ofn\n4+OPP8bFF1+MU6dOoa6uDg888AB69uyJH374AZ9//jnuuusuuyfkCjMREWmBHlrVuXpjnx5iQGSP\nV1aYs7OzUVBQgPLyclRWViIxMVFJdnfs2IHq6mr07NkTALB8+XJ069YNTz31FC6//HLcc889Lk+O\niIjIE3y9Vd2OYzvwx+w/Kse/zvtVdSN+a/h6DIi8qcUVZnfjCrNcrKWSh7GXi/GXS2vxj49PQ05O\nusV4VFQysrIWSphR6zm6qmwr9r4cA1+itWvfaKTUMBMREemBL7aqq66tRtjLYcrx53/6HH2D+zr9\ner4YAyJZ+FNhMPyUKw9jLxfjL5fW4m+rVV1goP22qLK4UqtsK/a+FgNfpbVrn5xjtw8zERGRHiUl\njUFERIpqLDw8GYmJoyXNyLamyXLStUlu64LhSzEgko0rzAbDWip5GHu5GH+5tBZ/X2hV566trW3F\n3hdioAdau/bJOUyYiYjIkGJiRmg2OXRXstwSLceASEvYJYOIiMhDHO1z/MTuJ/DSVy8px9yEhMg1\n7JJBRESkYY72OfbWqjIROY43/RlMbm6u7CkYFmMvF+MvlxHjn5GxE8XFqaqx4uJUZGbuUo2VnC1R\nJcvH7j/m1mTZiLHXEsZfH7jCTERE5AGt6XPMVWUi38AVZoPhnbryMPZyMf5yGTH+LfU5bposv3br\nax5Llo0Yey1h/PWBCTMREZEH2Opz/J/R6apkuWx+GeKuivP29IjIAUyYDYa1VPIw9nIx/nI5E3+T\nKQ/x8WmYMOEFxMenwWTK88DMPCcmZgRSU0chKioZI0c+jqioZJTMunADYGhQqFdKMHjty8X46wNr\nmImISHMc7TChVY19jnu/0hu/1fymjLNWmci3sA8zERFpTnx8GnJy0i3Go6KSkZW1UMKMnMcb+4jk\nYR9mIiLSrdZ0mNC6rCNZSPwsUTn+dd6v8PPzs/l8Rzc5ISLvYQ2zwbCWSh7GXi7GXy5H499Shwmt\nC14ZrEqWy+aXtZgsp6TsRk5OOvbsWYqcnHSkpOx2S902r325GH99YMJMRESaY6vDRGLiaEkzap3z\n5vOqEozP//R5q0owWrvJCRHJ4Tt/2yK3YD9IeRh7uRh/uRyNf2MpQmZmMqqr2yIwsBaJiaM1XaLg\nSq2yJ0tQeO3LxfjrAxNmIiLSpMYOE76gabL8p/5/wsqbVzr09b5egkKkdyzJMBjWUsnD2MvF+Mul\n1/gHrwy22ITE0WQZ8GwJil5j7ysYf33gCjMREZET3NkuTkYJCrtyELUe+zATERE54Pb3bsfen/Yq\nx77YV/nCxjAXbjSMiEhBauooJs2kK+7qw8ySDCIiolYKXhns88kywK4cRI5iwmwwrKWSh7GXi/GX\ny9fjv/+X/aoSjB/n/ugzybK12OthYxhf4evXPjXgTwYREZEdetzaml05iBzDFWaDYT9IeRh7uRh/\nuXwx/kIIVbL8wrgXfDJZthZ7X90Yxhf54rVPlrjCTERE1IweV5Wb8sWNYYhkYsJsMLm5ufy0Kwlj\nLxfjL5cvxb+lZNnX2rHZir0vbQzjy3zp2ifbmDATEemEryVynuBKDFqzqnyhHVu6MlZS0lDaYLRY\nExkJE2aD4adceRh7ufQef60nct6IvysxaG0JRkM7tnTVWEM7tmRNxNkavV/7Wsf46wNv+iMi0gH2\n1XUuBo/tesxia2t79cpsx0ZkTEyYDYb9IOVh7OXSe/y1nsh5I/6OxiB4ZTBeKXhFOW7NjX2+2I5N\n79e+1jH++sCEmYhIB3wxkXO31sbgdMVp1aryzmk7W90Fg+3YiIzJTwghvHnC7du3Y9iwYd48JRGR\n7l2o371QkhAenoy0NOO0CmtNDNzRLs5kykNm5i6lHdv114dg375Thr7Zkkir8vPzER0d7fLraONv\ndURE5BL21W05Bk2T5fDO4cifme/SeTIyduKXXyqxcuUXqKp6AEDDKrOWbrYkIvfgCrPBsB+kPIy9\nXIy/XDLj7+5NSKytZAOLAIxHY9IcFZWMrKyFLp3HXZyJPVsUug/fe+TiCjMREVELPLFjn7XWcsDf\nATyBxoRZKzdbOkPrLQqJZPDdn2hyCj/lysPYy8X4y+Xt+Htya2tb3TiANsp/aelmS0dj74u9prWM\n7z36wC4ZRESkK55MlgHb3TiAOgC+3zVD6y0KiWRgwmww7AcpD2MvF+Mvlzfin/1dtipZ/nHuj25P\nlgHrreUuuigJ11zzM6KitNeZxNHYs0Whe/G9Rx/4cZGIiHyerVVlT9y8Zr0bxx2aSpJdkZQ0BiUl\nKRbt+Xx51ZzIVeySQUREPqte1OOSFy9RjlNuSsGC4QsAWO9mERGRgtTUUbpJbj2lea9po7UoJP3w\nSpeMw4cPY8OGDejfvz9mzJihjJvNZjz44IOYOHEiYmNjAQDHjx/Hpk2bAAAJCQno2bOny5MjIiKy\npaVaZd685ryYmBGMEVETdmuYzWYzJk2aZDG+detW9OnTRzW2fv16zJo1C7NmzcKbb77p3lmS27CW\nSh7GXi7GXy53x781N/bx5rUGvPblYvz1we67xuDBg1FYWKgaq66uxjfffIObbroJVVVVAICqqiq0\nbdsWXbt2VZ5XU1ODgIAAq6/btIl344XEY+8cHzhwQFPz4TGPecxjR44n5k9EUx8O+9Dm8ysqTsGa\nxpvXXJ3P8uVr8dFH3yEoKBQBAWZERl6CG27op6l4NaWV+RjtuJFW5mO046CgILhDizXMhYWF2L9/\nv1KSkZ2djfDwcJSXl6OqqgqxsbEoLi7Gf/7zH7Rt2xZAw8r0zTffjPDwcIvXYw0zERE5w9F2cdZq\nmMPD3dPFgvXRRL5Byk5/FRUVKCoqQlxcHHbs2KGM9+jRA6WlpXj44YchhMA///lP9OjRw+XJERER\nxW6KxX9//q9y3NpWcda7Wbjn5jXWRxMZS4sJc9MF6KKiIpjNZqxYsQKnTp1CXV0dBg4ciJ49e6K+\nvh4VFRWor69HXV2dzXIMkis3l3vay8LYy8X4e569Fm7Oxt/VTUg8dfOaL9VH89qXi/HXB7s/2dnZ\n2SgoKEB5eTkqKyuRmJiolFPs2LED1dXVSjeM6dOn49VXX4W/vz/uvvtuz8+ciIg040KJwoVV15KS\nhs09nElYi8uLcd2G65Tj7VO2Y+ilQ12fqJtwcw8iY2EfZiIicll8fBpyctItxqOikpGVtdCh1/L0\n1tbu4Mn6aCJyHyk1zERERNa4q0ShabLczr8dTj5w0qV5Neeunf88WR9NRNrDhNlgWEslD2MvF+Pv\nWS2VKLQUf2+sKtsrGwHgcCLtK5t78NqXi/HXBybMRETksqSkMSgpSbEoUUhMHN3i13qrBMNWZ4tn\nn70Xv/12qdvqr4lIf1jDTERkYLZKFJwpXTCZ8pCZuavVJQrerlWeMOEF7Nmz1GK8c+cpOHv2HYtx\nZ+qviUhbWMNMREQusVWikJ9/GJs2lTq84upIiYKMG/tslY34+QVaHddiizgiksNf9gTIu5pv1Une\nw9jLxfhbaihRSFWNFRenYu3aL6yOZ2bucvpcjfF/peAVVbL8859/9loXjKSkMYiISFGNhYcn4/LL\nL7L6fL20iOO1Lxfjrw/8+ExEZFC2OlvU1lpPIF1dcZXdLs5WZ4v8/MP47rskVFZmKM9tbf01ERkD\nE2aD4Z268jD2cjH+lmyVKLRtW2l13NkV19r6WkzMn6gcp9yUggXDFzj1Wq5qXjZiMuVh06ZSVFbe\nBeAJAG1w0UWFuPPOG3Vzwx+vfbkYf31gwkxEZFC2OlvceeeN2LTJuY4XzcleVW6JunNGw/dXWQl8\n+WWyvEkRkeYwYTYY9oOUh7GXi/G3ZG/zjWHD8lzelEPryTLgvg1XtIzXvlyMvz7o5x2BiIgcZquz\nhSubcjRPlMft/BsiIy9x6rU8raUNV4iIAHbJMBx+ypWHsZeL8feO5skylgjk5KRj48YymEx5ciZl\nh63OGXq64Y/XvlyMvz5whZmIiFwWuioUtfVNVmWXCAC7ADwOoC2Ki9vg2WezNHcjnb2yFCKiRkyY\nDYa1VPIw9nIx/i1zZnc/wHJVeeTWRdiDXQA+A/B3Zfzbb++HyZSnuWTUlfITX8BrXy7GXx+YMBMR\nkc1d/wDbu/sdOH0AY94aoxzvnLYTg0IGIX5nGgATmibLAFBV9QoyM5N1nZwSkT6xhtlg+ClXHsZe\nLsbfPlu7/tna3S94ZbAqWS6bX4ZBIYMANNQFt29/zOrX6an7hK/gtS8X468PTJiJiMih9mpNSzDa\n+re1aBcXEzMCffvqe7tpIjIWJswGwz3t5WHs5WL87WtNe7XglcGqZLlsfhlOPXDK6tc99tgduu8+\n4St47cvF+OsD/zZGREQ2d/1rTHAd3YSkefeJiorTWLjwDtYvE5FP8hNCCG+ecPv27Rg2bJg3T0lE\nRK1gMuUhM3OXqr3a1KL/Uz1Hi7v1ERHZkp+fj+joaJdfhyvMREQEwLK9mi9sbU1E5A2sYTYY1lLJ\nw9jLxfi33t92/E2VLJ/8y0mXk2XGXx7GXi7GXx+4wkxERAquKhMRWWINMxFpkrO7zpFzKmsrcdnL\nlynHD133EBaPXCxxRkRErmMNMxHpljO7zpHzuKpMRGQfa5gNhrVU8jD2refornOtwfhb561kmfGX\nh7GXi/HXB64wE5HmOLLrHDmHq8pERK3HFWaD4Z728jD2rdeaXeccxfhfICNZZvzlYezlYvz1gcs1\nRKQ5Le06R87hqjIRkXO4wmwwrKWSh7FvvZiYEUhNHYWoqGSMHPk4oqKSkZY22qUb/owef9nJstHj\nLxNjLxfjrw9cYSYiTWq+6xw5Z/fx3bjj/TuU47137UW/bv0kzoiIyPewDzMRkU7JXlUmIpKNfZiJ\niHTAUxu0NE2W2/q3xakHTrn8mkRERsUaZoNhLZU8jL1cWox/4wYtOTnp2LNnKXJy0pGSshsmU57T\nrxm8MliVLJfNL9NEsqzF+BsFYy8X468PTJiJiCRx9wYtLMEgIvIMlmQYDPtBysPYy6XF+LtrgxZf\nSJS1GH+jYOzlYvz1gSvMRESSuGODFl9IlomIfB0TZoNhLZU8jL1cWox/UtIYRESkqMZau0HLPZ/c\no0qWS+eVajpZ1mL8jYKxl4vx1weWZBARSdLYDSMzMxnV1W0RGFiLxMSWN2jhqjIRkXexDzMRkY84\nV30O4RnhyvFTI5/CvOvmyZsQEZHGsQ8zEZGBcFWZiEgeuzXMhw8fRkpKCl5//XVlbNOmTXjqqaew\nfPlylJeXK+P79u3DM888g7S0NHz//feemzG5hLVU8jD2cvly/L2dLJtMeYiPT8OECS8gPj7Npb7Q\njXw5/k15IjaeppfY+yrGXx/srjCbzWZMmjQJR44cUcYSEhIAAAUFBfj0008xdepUAMC///1vPP30\n0zh//jxefvllpKSkWH1NIiJqHWcTZVd2D2zcTKW4OF0ZKylpeD93xw6EvoyxITIuuwnz4MGDUVhY\naDFeW1uLQ4cOISwsTBmLiIhAUVERysvLceWVV9o9aW5urtKXsPGTF4+9c9w4ppX5GOk4MjJSU/Mx\n2rGvxd9astyar9+3rwgbN5ZZTeqCgupa/Pr09M0oLs5UnbthM5XkVn29rWNfi7+14/T0bJuxiYkZ\nIX1+POYxjy2Pg4KC4A4t3vRXWFiI/fv3Y8aMGcrYggULEBAQgCeeeALt27cH0FCSYTKZYDabceed\nd6J///5WX483/RER2eZq+UV8fBpyctItxqOikpGVtbDFr58w4QXs2bPUYnzkyMexefOjDs1Fbxgb\nIt/jrpv+nOrD/Nxzz2HSpElYvXo1AODs2bMwmUxYtGgRFi9ejHfeeQf19fUuT47cr/ETF3kfYy+X\nL8TfHbXKru4e6I7NVKzxhfi3xFOx8TQ9xN6XMf760GLCbGsBOjQ0VFnm9vPzUz3Py53qiIh82pb/\nbVEly/kz852+sc/VpM6VzVT0jrEhMi67JRnZ2dkoKChAeXk5+vfvj8TERKxatQpnzpxBly5dMH36\ndI+dwOAAACAASURBVHTr1g0AsG3bNhQUFMDPzw+jRo3C8OHDrb4mSzKIiC5wdweMCzempSpj4eHJ\nSEtreUOUpq+RmbnLoc1UjIKxIfIt7irJ4MYlREQeZK9jRdNkOaxDGA7dd8ht52RSR0TEjUvISbm5\nFzpkkHcx9nLJiL+tNmRTi/5P9Tx391WOiRmhuQSZ1788jL1cjL8+MGEmIp/iSo9hb8vI2KlKlgGg\neGaa6pg79hERaR8TZoPhp1x5GHvXubJxhIz4qzpWLPFTPWa0RJnXvzyMvVyMvz441VaOiEiGhhXb\nVNVYw8YRuyTNyD6lY0WzZDlqV8v9kImISDuYMBsM+0HKw9i7zpUewzLi/1PsZnWyvKQe4f9aaMg2\nZLz+5WHs5WL89YElGUTkM3xp44jm7eJGbl2EwKgUdqwgIvJBbCtHRD7DHT2GPa20ohR91/ZVjpeN\nW4ZZg2a16mt96YZGIiJfwLZyRGQ4jcljZmayJnsMu7IJiSs3NBIRkWexhtlgWEslD2PvHjExI5CV\ntRCbNz+KrKyFrU4mPR1/V3fs84UbGk2mPMTHp2HChBcQH58Gkymv1V/L618exl4uxl8fuMJMROQC\nd21t7coNjd7AFXAiMjKuMBsM+0HKw9jL5Yn4uytZBrR/Q6OrK+C8/uVh7OVi/PVBG0sXREQ+xJ2J\ncqOkpDEoKUmxuKFRKy3otL4CTkTkSVxhNhjWUsnjK7F3pU5Vy9wVf08ky0BDWUNq6ihERSVj5MjH\nERWlre4frq6A+8r1r0eMvVyMvz5waYCIFKxTte2tw2/hL1v/ohwX3leI7h26u/UcMTEjNBtnra+A\nExF5EvswE5EiPj4NOTnpFuNRUcnIyjLuds6eWlX2NSZTHjIzd2mypR8RkTXsw0xEbsc6VTUhBLq9\n2E05vjr4auT9SR8lKs7Q8go4EZEnsYbZYFhLJY8vxF7rnRpc4Wj8g1cGq5Llsvllhk6WXeUL179e\nMfZyMf76wISZiBRJSWMQEZGiGjNinSpLMIiIqCnWMBORipHrVJkoExHpC2uYiUjFZMpDRsZO1NS0\nQ0CAGUlJY5xKdI1ap8pkmYiIbGFJhsGwlkoeT8a+sR1cTk469uxZipycdKSk7NZND2V3sBX/If8a\nokqWy+aXMVn2AL73yMPYy8X46wMTZiIdcHXbYqMKXhmMY+eOKcdMlImIyBomzAbDPe3l8WTs2Q6u\nZU3j/9NvP6lWldeMX8Nk2cP43iMPYy8X468P/G1KpAN6bgfnbqxVJiIiR3GF2WBYSyWPJ2PPdnAt\ny83NZbIsEd975GHs5WL89YErzEQ60NjVIjMz2ZDt4FoiO1F2VwcTIiKSg32YiUjXCZ0WkuWUlN2q\nmzIjIlKQmjpKNzEmItIq9mEmIre4kNClK2MlJQ3lHb6c0MlOlBs1dDBJV401dDBJ9un4EhEZCWuY\nDYa1VPJoNfZ6bElnLVmWFX92MGmg1evfCBh7uRh/fWDCTGRwekroMgoyVMnyD4k/SL+xjx1MiIh8\nn+/9RiSXsB+kPFqNvV4SupZKMGTFPylpDEpKUlSr+EbsYKLV698IGHu5GH99YMJMZHC+ntAJIdDt\nxW7KceRlkfgw/kOJM1JjBxMiIt/HhNlgcnNz+WlXksbYa60jhS8ndI7c2Cfz2o+JGeET8fQkvvfI\nw9jLxfjrAxNmIi/SakcKX0zotNIFg4iI9I99mIm8KD4+DTk56RbjUVHJyMpaKGFGvoeJMhERtZa7\n+jCzSwaRF+mpI4UMTJaJiEgGJswGw36Q8uTm5uqmI4W3hb8SrkqWy+aXOZws89qXi/GXh7GXi/HX\nBybMRF6UlDQGEREpqjFf6kghQ/DKYJyrOaccc1WZiIi8jTXMRF5mMuUhM3OXz3Wk8LaSsyUYtv7C\ne8W7E9/FzeE3S5wRERH5GnfVMLNwksjLfLEjhbexVpmIiLSEJRkGw1oqeXJzc2Ey5SE+Pg0TJryA\n+Pg0mEx5sqelOZ5Klnnty8X4y8PYy8X46wNXmIm8ZN++ImzcWKa5HsxawVVlIiLSKrs1zIcPH8aG\nDRvQv39/zJgxAwCwadMmFBYW4uKLL8Y999yDLl26AADOnj2LFStWoLq6Gv3798ddd91l9TVZw0xG\nxR7MtjFZJiIiT/BKDbPZbMakSZNw5MgRZSwhIQEAUFBQgE8//RRTp04FALz33nuIiori9o9ENrAH\nsyUmykRE5Avs/qYePHgwCgsLLcZra2tx6NAhhIWFKWNHjx7Fvffe26qTNt1XvbG2h8feOV69ejUG\nDRqkmfn4wvG+fUXIzS1FTU07VFScwu23X4WHH57t8OtVVJyCNRUVp5X/1sL3661ja8myJ8/XtI5Q\nC9+/0Y4Zf3nHjWNamY/RjhvHtDIfox0HBQXBHVpsK1dYWIj9+/crJRkAsGDBAgQEBOCJJ55A+/bt\nUVFRgUWLFuGqq67C+fPnERsbi0GDBll9PZZkyJWbe+HDCrXMZMpDSspuFBenKmMRESlITR3lcN3x\n8uVr/38N84XXCg9PRlqasdrKvX7odTy4/UHl+KbtC3BRWz8kJY3xaBx47cvF+MvD2MvF+Mslta3c\nc889hy+//BKrV6/Gww8/jKCgIISGhmLWrFnw9/dHamoqBgwYAH9/NuHQGv7QOiYjY6fqJj0AKC5O\nRWZmssPJ3cMPz8aAAXnIzEw2bA/m5qvKWCLw+f//T0/fAMlrXy7GXx7GXi7GXx9aTJhtLUCHhoaq\nlrm7deuGM2fOoEePHmjTpo37Zkgkkbvrjo3ag7muvg4hq0IuDOSMAnbuUj3H2Q8iREREnmZ3CTg7\nOxubNm3C/v37kZmZCQBYtWoVnnnmGXzwwQeYPHmy8tzp06dj+/btePrppzF27FiuLmtU05oqallA\ngNnqeGBgrcOvZdTYB68MVifLSwSwM8rqcz15A6RR468VjL88jL1cjL8+2P3tFBcXh7i4ONXYAw88\nYPW5HTt2xN133+2+mRFpQFLSGJSUpFjUHScmjpY4K99hrQSjgfUPHM58ECEiIvK0Fm/6czfe9Ee+\nxmTKQ2bmLsPWHTvDWgcMdR/qXQA+A/B35TnNb4A0mfKQkbETNTXtEBBg9vhNgUREpD9Sb/ojMhKj\n1h07y1ZvZfVqfcMKffv2d6JPn+7o3j1I9UHkQncS7opIRETysdDYYFhLJY/eYx/zbowqWS6bX6ba\niCQmZgRSU0chKioZI0c+jqioT/Cvf81Cbu5TyMpaqEqEG7qTpKpev+GmQPWNgo7Qe/y1jvGXh7GX\ni/HXB64wE5HLWrtjX2tX67krIhERaQlXmA2G/SDl0WPsS86WqJLl3Om5btne2p3dSRrpMf6+hPGX\nh7GXi/HXBy7XEJFTWruq7Ax2JyEiIi1hwmww3KJTntzcXFRUtNFF54emyXK/4H7Y+6e9bn39xpi4\nc1dEXvtyMf7yMPZyMf76wISZyEv27SvCxo1lPt35wZOrys2xOwkREWkFa5gNhp9y5cnNLXV75wdv\n8may7Am89uVi/OVh7OVi/PWBK8xEXuKrnR98PVEmIiJyFVeYDYb9IOWpqDhldVzL20HrKVnmtS8X\n4y8PYy8X468PTJiJvOT2269CRESKakyrnR82HtqoSpZP/uWkTyfLRERErvATQghvnnD79u0YNmyY\nN09JpBkmUx4yM3e5rfODJ+hpVZmIiIwtPz8f0dHRLr+OtosniXTCZMpr0k5OYN68P2guUa6tr0Xo\nqlDleEX0CswYMEPijIiIiLSBCbPBsB+k95lMeUhJ2a3pdnJGWFXmtS8X4y8PYy8X468PrGEm8rCM\njJ2abidnhGSZiIjIFVxhNhh+yvU+rbaTM1qizGtfLsZfHsZeLsZfH5gwE3lYQIDZ6rjMdnJaSZbV\ntd2+u1U4ERHpG0syDIb9IL0vKWmMZtrJzfpklipZLptfJjVZTknZjZycdOzZsxQ5OelISdkNkynP\nI+fjtS8X4y8PYy8X468PXGEm8rDGFdP09CQEBYVIayentVXlr776GeXllwPYBaDhw0NDbXcyV5mJ\niEhT2IeZSOf+V/4/XL/heuX4wD0HcFmny6TM5ULHkKY3QS4CMB6NSfPIkY9j8+ZHZUyPiIh0hn2Y\niahFWllVbtTQMSS92ejfATyBxoRZy1uFExGRMbGG2WBYSyWPt2PfNFmefs106ckyYLtjCNAGgGdr\nu3nty8X4y8PYy8X46wNXmIl0RiurytY6YNjqGNKly2EMG5asya3CiYiIWMNMpCNaSpab1ypHRKQg\nIeESbNpUqhoPD09GWhoTZSIicj/WMBORQiuJciNrtcrFxan48stkpKaORmZmMqqr20rrGEJEROQI\n1jAbDGup5PFU7LWWLAP2dzeMiRmBrKyF2Lz5UWRlLfRassxrXy7GXx7GXi7GXx+4wkzko94tehf3\nm+5XjkvnlcLfz3ufge3t0qfF3Q2JiIicxYTZYLinvTzujL3sVeULNcoXyi5KShp2M4yJGYGkpDEo\nKUmxqFWWsbthI177cjH+8jD2cjH++sCEmciH1NTVoPtL3ZXjTXdsQnRv129mcJStGuXGXfoaV5pZ\nq0xERHrAhNlgcnNz+WlXEldjL3tVuSl7NcqNmibOWsBrXy7GXx7GXi7GXx940x+RD2iaLHds11H6\njX2sUSYiIiNhH2YiDdPSqnJT1voss58yERFpDfswE0lmr0uEO2g1WQbAGmUiIjIUJswGw1oq92ip\nS4Q1rY39lA+nYGvJVuVYS4lyU1qrUW4Jr325GH95GHu5GH99YA0zkRMaukSkqsYaukTscul1g1cG\n+0SyTEREZCRcYTYYfsp1j9Z0iWjOXuy/P/M9hr8+XDk+MvsI/l97dx8dVX3ncfwTiEmMaQyJRp5L\nUESpRkRWy/IkaTelajzSiFJYtqQPSakFj8eDJNUWe1xL4kNdkYpABcGtrSUg6u6RThQQ0PgQOQFL\nElqQ0IMCCgliDUkmIftHNkPGTCbzcGd+M3fer7+8NzczPz6J537zm+/9/S5OvjjwAaIHfvfNIn9z\nyN4s8rcHCmYgAFauEhHJvcoAAICWjJjDnvbWKCqaqqysErdzfe1k5yn77sXyousXUSyHEL/7ZpG/\nOWRvFvnbAzPMQACCXSWCWWUAAKIH6zADYUaxDABAeIRlHeba2lqtX79eY8aM0dy5cyVJGzZsUE1N\njVJTU1VQUKC0tDTX9U6nU3fffbduvfVWTZ8+PejBAXby7Re/rd3Hd7uOKZQBAIgOXnuYnU6nZsyY\n4XZu5syZWrJkiaZNm6YtW7a4fa2iokIjR45UXFyc9SOFJeilMiN9WTrFsmH87ptF/uaQvVnkbw9e\nC+bs7GylpKT0ON/W1qZ9+/YpMzPTda6lpUV79+7V+PHjFeYuDyBivXH4DbcWjJMLTlIsAwAQZQJ6\n6K+kpEQJCQnKz893nXvttdc0ffp0nTp1qs/v777rTddfXhyH57jrXKSMx87HnnqVI2l84T52OCpV\nVrZZTmeiLr44WUVFU5Wc3B629580aVJE5RFrx+TPMcccmzhOTk6WFfp86K+mpkYffPCBq4e5S1VV\nlXbu3Kl77rlHTU1NWrZsmYqLi7V9+3Y1Nzf32sPMQ3+wu5a2Fg16epDr+O05b+uKjCsMjsi8c1uJ\nn9sdMSurREuXTo6q7bUBANHFqof++lyHubd6OjMz01W119XVyel06sknn1RFRYW2b9+uI0eOBD04\nWK/rLy6ERvqydLdiuWFhg6tYNp29w1Gp/PxS5eU9rvz8UjkclWF771BtJe4P0/nHOvI3h+zNIn97\niPf2xc2bN6u6ulqnTp3SmTNnVFhYqOXLl6uxsVFpaWmaPXu2JGncuHGuWePt27erpaVFQ4cODf3o\ngQjSvQXjrmvv0kOTH/J6vcNRqZUr31Rr63lKSHCqqGhqyGZbz83wlrnO1dd3brwSjhneQLYSBwAg\nUrAOMxCkMc+O0bEvj7mOfXmoL9wtCvn5pdq2razH+ZycYpWXL7b8/SLt/QEAsSlsLRkAepe+LN3v\nYlkKf4uC6RneQLYSBwAgUlAwxxh6qayx7q/r3FowGhY29Fksd8++twK2qupoSPqLExKcHs8nJrZZ\n+j69yc2doKVLJysnp1gTJz6gnJxilZb6vpW4FfjdN4v8zSF7s8jfHmggBPxkxdbWvRWwp08P17Zt\nD1neX1xUNFX19SVus9rhnuHNzZ3AihgAgKhEDzPgo8+aPtPo3492HX/ys0+UFJ8U0Gt56mGWfiFp\nuqTOItbq/l6Ho1KrVu3QsWNNOnbsmC65JFUDB2aE9GFDAABMsqqHmRlmwAdWzCp311WgrlpVrKqq\nozp9eri6F8uS9f3FXe9ZUrJTDQ1/VkODVFsb3tUyAACIRvQwxxh6qfzXvVjeOmtrwMXyV7PPzZ2g\n8vLFuu66gZIeUvdiWQpNf3EkrIdsCr/7ZpG/OWRvFvnbAzPMQC8uXXWpGpsbXcfBzir3Jpz9xaZX\nywAAIBpxl4wxXXusw7vus8qLb1isxTcE30vcW/bd2zNaWuKVmNimwsLQrCBherUMk/jdN4v8zSF7\ns8jfHiiYgW6eqHpCD719boe+aW/ep10V8XovoTSkD8eFawWJSFgtAwCAaEPBHGN27drFX7u96D6r\nnBiXqMHP3aNt3QrLYB+Oi4TswzmbHWkiIf9YRv7mkL1Z5G8PFMyIeQcaD+j65693HZ9ccFK3317m\nVixLXQ/HFUd9ccl6yAAA+IeCOcbwV6673paL6+3huKNHTyg/v1StrecpIcHpV5sG2ZtF/maRvzlk\nbxb52wMFM2KSs92pS353iev4YOFBDUga4Dr2/HDcDtXXx6u2tsx1hjWMAQCwP9ZhjjGsB9k5q9y9\nWG5Y2OBWLEudD8dlZZW4nUtKWq4zZ55xO+fPGsZkbxb5m0X+5pC9WeRvD8wwIywcjkqtXPlmQK0M\nVuregrHptk26cfiNHq/z9HDcsWMDVVPT81rWMAYAwN6408cYE71UDkelSkp26tAhc60M92y9R+v+\nus517MsmJF99OC4/v9RjwezrGsb0sZlF/maRvzlkbxb52wMtGQg509sxpy9LdxXLBVcXBLxjn6c2\nDdYwBgDA/iiYY4yJXipT2zFvPbzVrQWjYWGDHp/2eMCvl5s7QUuXTlZOTrEmTnxAOTnFKi31fQ1j\n+tjMIn+zyN8csjeL/O2BlgyEnIntmHtbLi5YrGEMAEDsievo6OgI5xu+8cYbGjduXDjfEoad62F2\n347Zn9lZXzWcadBlqy9zHR+/67jO6+95hhsAANjb7t279a1vfSvo12GGGSFnxXbMvqyyEapZZQAA\nENsomGOMqT3tg2ll6GuVjY6ODmU8leH62vv/8b4uTbs0uAGHgKns0Yn8zSJ/c8jeLPK3BwpmGNfX\n7HHnKhtlbt/TucpGsR789F7VNdS5zjOrDAAArEbBHGMi7a9cX9Zo7m2Vja1TyqT/r4+fyHlCP7jq\nB6EdbJAiLftYQ/5mkb85ZG8W+dsDy8rBKF/WaO6xysY3/0t6MM512LCwIeKLZQAAEL0omCOMw1Gp\n/PxS5eU9rvz8UjkclZa+fqStB+nLGs1uG4Y8GCdNv0eSdFnS6KhqwYi07GMN+ZtF/uaQvVnkbw+0\nZESQSNhCOtx8WaM5N3eCjrce1d0fnZtV/tMV/2vbTAAAQGRhHeYIkp9fqm3bynqcz8kpVnn5YgMj\nCj1f1mhmubjw8GXpPgAAognrMNuQqS2kTfK2RrOz3alLfneJ69rDPz2sryV8zdRQbS0WP90AAMBX\n9DBHkHBsIR2JvVS5uRNUXr5Yr756r8rLFys3d4IuX325W7HcsLAh6ovlSMy+iy8PX0a7SM4/FpC/\nOWRvFvnbg32nLqNQUdFU1deX9GhPKCycYnBUgQv0I/7uLRhv3PmGrr3k2lAOE4rNTzcAAPAVd8MI\nYsUW0n0J13qQgXzEv3rPai1+81yvtt16lSN5Lc5wfLphWiTnHwvI3xyyN4v87YGCOcIEs4V0JPG2\nO5+nf1/3WeUl/7pEd4+/O+RjxDl2+3QDAAAr0cMcY8LVS+XrR/x7Pt3jViw3LGywbbEcyX1subkT\ntHTpZOXkFGvixAeUk+O+UokdRHL+sYD8zSF7s8jfHphhRkj48hF/90L5tlG3ac1314R8XOidXT7d\nAADAaqzDjJDwtr7yxGnZGrZimOv8iQUn1C+ODzsAAIC1WIcZEa23BxjXND+pWSsckqTxA8fLcYfD\n5DAtxcYfAADYEwVzjNm1a1fYntjt/hF/R0eHMp7KcH3to8KPlJaUFpZxhIMvq4KEM3v0RP5mkb85\nZG8W+dsDn4Mj5CrqK1zF8oTBE9SwsMFWxbIUGxt/AAAQq5hhjjHh/iv3/h33a0X1CklS9bxqDU8d\nHtb3DxdfVgVhhsEs8jeL/M0he7PI3x4omBESJ8+c1KjVoyRJ9/7Lvbp/wv2GRxRasbDxBwAAsYqW\njBgTjvUg1+xd4yqWq+dV275Yljo3/sjKKnE799WNP1iL0yzyN4v8zSF7s8jfHrzOMNfW1mr9+vUa\nM2aM5s6dK0nasGGDampqlJqaqoKCAqWldfairl27Vv/4xz90wQUX6Ec/+pEGDBgQ+tEjojS3NWvY\nimFq72hX3qV5WnfzOtNDCptwbGsOAADM8LoO8969e9Xc3Kz9+/e7CuYu1dXVqqur06xZs9zOv/fe\nezp8+LBmzpzp8TVZh9meKuordOcrd3b+9x0Vum7gdYZHBAAAYp1V6zB7bcnIzs5WSkpKj/NtbW3a\nt2+fMjMze3wtJSVFbW3e+za7fzyxa9cujqP4eMfOHRq/erzufOVOjRowSi9d+5LOHDgTMePjmGOO\nOeaYY445DlafO/3V1NTogw8+cJthXrRokRISEvTLX/5SSUlJbtevXr1aN910k4YMGeLx9ZhhNmvX\nLuvWg9z76V7d+KcbJUnP3/y8br70Zkte166szB7+I3+zyN8csjeL/M0Kywxzbx599FHNmDFDK1as\ncDtfVVWlIUOG9Foswz6K/lLkKpaPzD9CsQwAAGwrvq8LepuAzszMVHJysuv44MGD2r9/v+bMmWPd\n6GC5YP/K/fiLj3X12qslSQ9Pfljzr51vxbBiAjMMZpG/WeRvDtmbRf724LVg3rx5s6qrq3Xq1Cmd\nOXNGhYWFWr58uRobG5WWlqbZs2e7rn3iiSeUkZGhX//61xo+fLgKCgpCPniE12PvPabfvPMbSVLd\nj+uUmdyzhx0AAMBu+uxhtho9zGYF0kv1ResX+vozX5ck/fDqH+qxaY+FYmi2Rx+bWeRvFvmbQ/Zm\nkb9ZVvUw99mSgdhWvr9chX8plCRV/nulRqePNjwiAACA8GKGGR45253KXput403HNXHIRL3yvVcU\nFxdnelgAAAA+Y4YZIfP2x2/rlo23SJJe/t7Lmjx0suERAQAAmBPQsnKIXt4W8e7o6NCMl2bolo23\nKD0pXcfvOk6xbCErF1CH/8jfLPI3h+zNIn97YIYZkqS/N/5dNzx/gyTp6X97WrOunNXHdwAAAMQG\nepihX+z4hZ6pfkaSVF9Ur9TEVLMDAgAAsAA9zAjaiaYTuvz3l0uS7rv+PhV/s9jwiAAAACIPPcwx\npquXatWeVa5iec+8PRTLYUAfm1nkbxb5m0P2ZpG/PTDDHGNazrYofVm6JOm2UbdpzXfXGB4RAABA\nZKOHOYZs+WiLZv9P53bmW2dt1djMsYZHBAAAEDr0MMNnZzvOavILk1V7slZXpF+hXXN2qV8c3TgA\nAAC+oGqyuT2f7tFFT12k2pO1+sMtf9AjIx6hWDaEPjazyN8s8jeH7M0if3tghtnGfrLlJ9r4t42S\npI9/9rHOjz9fuz7hf1wAAAB/0MNsQ0e+OKLstdmSpKVTlqpobJHhEQEAAIQfPczwqOzdMpW9WyZJ\n+tuP/6aLki8yPCIAAIDoRjOrTZxuOa30Zekqe7dMhdcUqmFhg8dimV4qc8jeLPI3i/zNIXuzyN8e\nmGG2gT/X/Vk/dfxUkvTu3Hc1asAowyMCAACwD3qYo5iz3alvrPmGTpw5oanDpmrTbZsUFxdnelgA\nAAARgR7mGPfWkbeUtylPkvTq917VxKETDY8IAADAnuhhjjIdHR26deOtytuUp8zkTB2/67hfxTK9\nVOaQvVnkbxb5m0P2ZpG/PTDDHEX2N+zXhP+eIElambtSM6+YaXhE9uFwVGrlyjfV2nqeEhKcKiqa\nqtzcCaaHBQAAIgA9zFFi0bZFevbDZyVJ9UX1Sk1MNTsgG3E4KlVSslOHDi11ncvKKtHSpZMpmgEA\niGL0MMeIz5o+0+jfj5YkFd9QrPtuuM/wiOxn5co3dehQmdu5Q4eWatWqYp8LZmaoAQCwL3qYI9jK\n6pWuYvnDgg8tKZbppeqptfU8j+dbWnz7e7JrhnrbtjK99dZ/atu2MpWU7JTDUel2HdmbRf5mkb85\nZG8W+dsDBXOEchxyqGRHiW4ffbsaFjZoyNeGmB6SZRyOSuXnlyov73Hl55f2KCzDLSHB6fF8YmKb\nT9/fOUO91O1c5wz1jqDHBgAAzKMlI0JNGTZFB35yQOnnp1v6upMmTbL09fx1rl/4XAtEfX2JJBlr\nYSgqmqr6+hK3onfEiGIVFk7x6ft9naE2nX2sI3+zyN8csjeL/O2BgjlCJcUnKSk+yfQwLGdFv7DV\nut531apitbTEKzGxTYWFU3weT7Az1AAAILLRkhFjTPdSBdsvHCq5uRNUXr5Yr756r8rLF/tVvBcV\nTVVWVonbOU8z1Kazj3Xkbxb5m0P2ZpG/PTDDjLCy42xssDPUAAAgsrEOM8LK05rHI0YUq7SUAhMA\nAFiLdZgRlZiNBQAA0YYe5hgTCb1UwfQLR7NIyD6Wkb9Z5G8O2ZtF/vZAwQwAAAB4QQ8zAAAAbMmq\nHmZmmAEAAAAvKJhjDL1U5pC9WeRvFvmbQ/Zmkb89UDADAAAAXtDDDAAAAFuihxkAAAAIAwrmFQmC\n5wAABeJJREFUGEMvlTlkbxb5m0X+5pC9WeRvDxTMAAAAgBf0MAMAAMCWrOphjvf2xdraWq1fv15j\nxozR3LlzJUkbNmxQTU2NUlNTVVBQoLS0NEnSkSNHtGHDBknSzJkzNXTo0KAHBwAAAJjmtSXD6XRq\nxowZbudmzpypJUuWaNq0adqyZYvr/Lp16zRv3jzNmzdPL7zwQmhGi6DRS2UO2ZtF/maRvzlkbxb5\n24PXGebs7GzV1NT0ON/W1qZ9+/Zp0KBBkqTm5mbFx8drwIABrmtaW1uVkJDg8XV3794dzJgRhOTk\nZPI3hOzNIn+zyN8csjeL/O3Ba8Hcm5KSEiUkJCg/P1+SdPToUV100UVat26dJCk9PV2ffPKJRowY\n0eN7regjAQAAAMIloFUyHn30Uc2YMUMrVqyQJA0aNEgnTpzQ97//fc2aNUsnT57U4MGDLR0oAAAA\nYEKfM8y9LaKRmZmp5ORkSVJSUpLOnj2rpqYmnT17Vu3t7b22YwAAAADRxOuycps3b1Z1dbVOnTql\nMWPGqLCwUMuXL1djY6PS0tI0e/ZsZWRkSJIOHz6s8vJy9evXj1UyAAAAYBthX4cZAAAAiCbs9AcA\nAAB40f/BBx98MBQvXFlZqeeee04HDhzQyJEjlZSU1Ou1n3/+uR555BE5HA4dO3ZM2dnZoRhSzPAn\n+/fff19r1qxRZWWlBg4cqPT09DCO1B5qa2v129/+VkePHtU111wjqXMjn2effVbvvPOOhg0bptTU\nVK+v4e/1OMeK/NeuXatXXnlFVVVVuvLKK3X++eeHY+hRz4rspc41/xcsWKD+/fvrsssuC/WwbcOK\n/Ln/Bs6K/LkHB8ZT9p7OeePvzyokM8zt7e3asmWLHnjgAeXl5emPf/yj1+s3btyonJwcPfzww5oz\nZ04ohhQz/M3+pZdeUklJiebPn+/aqRH+8bTBj78b+bDxT+CsyL+goEBLlizRlClT9Prrr4dqqLZj\nRfaSVFFRoZEjRyouLi4Uw7QtK/Ln/hs4K/LnHhwYT9l7OueNvz+rkBTMHR0dOnv2rFpaWpSSkqJT\np055vf7w4cOaNGlSKIYSc/zNPisrS3V1dfrwww+Z2QlQdna2UlJSXMfdN/Lp2syntbW11+/393q4\nCzb/7lJSUtTW1haScdqRFdm3tLRo7969Gj9+fK+rMsEzK/Ln/hs4K/LnHhyYr2bf27neBPKzCmjj\nkr7Ex8fr9ttv11NPPaULLrhAx48fV3Nzs8fWgKamJp0+fVpPP/20vvzyS02fPl1XX311KIYVE/zJ\nXpLGjh2rl19+WU6nU3fccUeYR2tP/mzkE8j18C6YPN966y3ddNNNIR6hfQWS/Wuvvabp06f3+cc9\n+uZv/tx/rRXI7z/3YDMC+VmF7KG/a665RosWLdL8+fN14YUX9lqwJScnKzMzU/PmzdOCBQtUXl6u\ns2fPhmpYMcHX7D///HM5HA7df//9+tWvfqUXX3yR7C0wePBgvzby8fd6eBdonlVVVRoyZIiGDBkS\nhlHak7/ZNzU1qa6uTmPHjg3jKO3L3/y5/1rL3/y5B5sTyH0iJDPM3VVUVOjyyy/3ek1GRoYaGxs1\nePBg9e/fP9RDihl9ZR8XF+f2ESgfhwaue3aJiYl+beTj7/XoKZj8JengwYPav38/PZwBCCb7uro6\nOZ1OPfnkk/r000/V3t6uq666inX8/RDs7z733+AEkz/34OB4ysvXDAP5fyVk6zCvWrVKhw4dUlpa\nmhYsWODaFbCyslKJiYkaN26c69p//vOf2rRpkw4dOqRp06ZpypQpoRhSzPAn+9dff13V1dWKi4vT\n5MmTdf3115sadtTytMGPt418PP0c2PgncFbk//Of/1wZGRnq16+fhg8froKCAhP/lKhjRfZdtm/f\nrpaWFn3nO98J5z8hqlmRP/ffwFmRP/fgwHjK3tO5Llbcd9m4BAAAAPCCjUsAAAAALyiYAQAAAC8o\nmAEAAAAvKJgBAAAALyiYAQAAAC8omAEAAAAv/g+uvNNapWnOOgAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 91
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from statsmodels.graphics.regressionplots import plot_ccpr_ax\n",
      "fig, ax = plt.subplots(figsize=(12,8))\n",
      "fig = plot_ccpr_ax(mod, 9, ax=ax)\n",
      "ax = fig.axes[0]\n",
      "ax.set_title(\"per_hisp*B_9 + resid vs per_hisp\");"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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LRn6IHfLrm5f/VpCdbeTnLxTB8Iz29iGu59va+MACwAH8rQAQCxTBcJWMvqiMjA7X85mZ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\nz1SK6fsr5AAAAABJRU5ErkJggg==\n"
      }
     ],
     "prompt_number": 106
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X = mod.model.data.orig_exog"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 93
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X[X.columns[:6]]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 94,
       "text": [
        "                     Intercept  C(kmeans_groups)[T.1]  C(kmeans_groups)[T.3]  C(kmeans_groups)[T.4]  C(kmeans_groups)[T.5]  C(kmeans_groups)[T.6]\n",
        "State          year                                                                                                                              \n",
        "Alabama        2004          1                      0                      0                      1                      0                      0\n",
        "               2008          1                      0                      0                      1                      0                      0\n",
        "Alaska         2008          1                      0                      0                      0                      0                      1\n",
        "Arizona        2004          1                      0                      0                      0                      0                      0\n",
        "               2008          1                      0                      0                      0                      0                      0\n",
        "Arkansas       2004          1                      0                      1                      0                      0                      0\n",
        "               2008          1                      0                      1                      0                      0                      0\n",
        "California     2004          1                      0                      0                      0                      0                      0\n",
        "               2008          1                      0                      0                      0                      0                      0\n",
        "Colorado       2004          1                      0                      0                      0                      1                      0\n",
        "               2008          1                      0                      0                      0                      1                      0\n",
        "Connecticut    2008          1                      0                      0                      0                      1                      0\n",
        "Delaware       2008          1                      0                      0                      0                      1                      0\n",
        "Florida        2004          1                      0                      0                      0                      1                      0\n",
        "               2008          1                      0                      0                      0                      1                      0\n",
        "Georgia        2004          1                      0                      0                      1                      0                      0\n",
        "               2008          1                      0                      0                      1                      0                      0\n",
        "Illinois       2004          1                      0                      0                      0                      1                      0\n",
        "               2008          1                      0                      0                      0                      1                      0\n",
        "Indiana        2004          1                      0                      1                      0                      0                      0\n",
        "               2008          1                      0                      1                      0                      0                      0\n",
        "Iowa           2004          1                      1                      0                      0                      0                      0\n",
        "               2008          1                      1                      0                      0                      0                      0\n",
        "Kansas         2004          1                      0                      1                      0                      0                      0\n",
        "               2008          1                      0                      1                      0                      0                      0\n",
        "Kentucky       2004          1                      0                      1                      0                      0                      0\n",
        "               2008          1                      0                      1                      0                      0                      0\n",
        "Louisiana      2008          1                      0                      0                      1                      0                      0\n",
        "Maine          2004          1                      1                      0                      0                      0                      0\n",
        "               2008          1                      1                      0                      0                      0                      0\n",
        "Maryland       2004          1                      0                      0                      0                      1                      0\n",
        "Massachusetts  2008          1                      0                      0                      0                      1                      0\n",
        "Michigan       2004          1                      1                      0                      0                      0                      0\n",
        "               2008          1                      1                      0                      0                      0                      0\n",
        "Minnesota      2004          1                      1                      0                      0                      0                      0\n",
        "               2008          1                      1                      0                      0                      0                      0\n",
        "Mississippi    2008          1                      0                      0                      1                      0                      0\n",
        "Missouri       2004          1                      0                      1                      0                      0                      0\n",
        "               2008          1                      0                      1                      0                      0                      0\n",
        "Montana        2004          1                      0                      1                      0                      0                      0\n",
        "               2008          1                      0                      1                      0                      0                      0\n",
        "Nevada         2004          1                      0                      0                      0                      0                      0\n",
        "               2008          1                      0                      0                      0                      0                      0\n",
        "New Hampshire  2004          1                      1                      0                      0                      0                      0\n",
        "               2008          1                      1                      0                      0                      0                      0\n",
        "New Jersey     2004          1                      0                      0                      0                      1                      0\n",
        "               2008          1                      0                      0                      0                      1                      0\n",
        "New Mexico     2004          1                      0                      0                      0                      0                      0\n",
        "               2008          1                      0                      0                      0                      0                      0\n",
        "New York       2004          1                      0                      0                      0                      1                      0\n",
        "               2008          1                      0                      0                      0                      1                      0\n",
        "North Carolina 2004          1                      0                      0                      1                      0                      0\n",
        "               2008          1                      0                      0                      1                      0                      0\n",
        "Ohio           2004          1                      1                      0                      0                      0                      0\n",
        "               2008          1                      1                      0                      0                      0                      0\n",
        "Oklahoma       2004          1                      0                      1                      0                      0                      0\n",
        "               2008          1                      0                      1                      0                      0                      0\n",
        "Oregon         2004          1                      1                      0                      0                      0                      0\n",
        "               2008          1                      1                      0                      0                      0                      0\n",
        "Pennsylvania   2004          1                      1                      0                      0                      0                      0\n",
        "               2008          1                      1                      0                      0                      0                      0\n",
        "Rhode Island   2004          1                      0                      0                      0                      1                      0\n",
        "South Carolina 2004          1                      0                      0                      1                      0                      0\n",
        "               2008          1                      0                      0                      1                      0                      0\n",
        "South Dakota   2004          1                      0                      1                      0                      0                      0\n",
        "               2008          1                      0                      1                      0                      0                      0\n",
        "Tennessee      2004          1                      0                      1                      0                      0                      0\n",
        "               2008          1                      0                      1                      0                      0                      0\n",
        "Texas          2004          1                      0                      0                      0                      0                      0\n",
        "               2008          1                      0                      0                      0                      0                      0\n",
        "Utah           2008          1                      0                      0                      0                      0                      1\n",
        "Vermont        2008          1                      1                      0                      0                      0                      0\n",
        "Virginia       2004          1                      0                      0                      0                      1                      0\n",
        "               2008          1                      0                      0                      0                      1                      0\n",
        "Washington     2004          1                      0                      0                      0                      1                      0\n",
        "               2008          1                      0                      0                      0                      1                      0\n",
        "West Virginia  2004          1                      0                      1                      0                      0                      0\n",
        "               2008          1                      0                      1                      0                      0                      0\n",
        "Wisconsin      2004          1                      1                      0                      0                      0                      0\n",
        "               2008          1                      1                      0                      0                      0                      0\n",
        "Wyoming        2008          1                      0                      1                      0                      0                      0"
       ]
      }
     ],
     "prompt_number": 94
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "X[X.columns[6:]]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 95,
       "text": [
        "                     per_older  per_white  per_older:per_white  per_hisp  no_party  np.log(median_income)  no_party:np.log(median_income)  PVI\n",
        "State          year                                                                                                                           \n",
        "Alabama        2004     13.038     70.289              916.404     1.705      14.4                  9.961                   143.441        -13\n",
        "               2008      6.589     68.544              451.628     2.853      14.4                 10.362                   149.213        -13\n",
        "Alaska         2008      2.888     65.649              189.568     4.962      19.8                 10.740                   212.644        -13\n",
        "Arizona        2004     13.017     63.818              830.698    25.253      12.9                 10.018                   129.233         -6\n",
        "               2008      6.579     60.609              398.767    27.676      12.9                 10.503                   135.493         -6\n",
        "Arkansas       2004     13.990     78.557             1099.039     3.249      17.7                  9.883                   174.925         -9\n",
        "               2008      7.068     76.378              539.857     4.925      17.7                 10.290                   182.128         -9\n",
        "California     2004     10.616     46.696              495.706    32.377      17.1                 10.128                   173.183          7\n",
        "               2008      5.366     43.198              231.813    35.238      17.1                 10.668                   182.421          7\n",
        "Colorado       2004      9.673     74.464              720.309    17.102      15.0                 10.139                   152.089          0\n",
        "               2008      4.893     72.082              352.707    19.001      15.0                 10.619                   159.279          0\n",
        "Connecticut    2008      6.975     74.193              517.509    11.603      15.8                 10.801                   170.657          7\n",
        "Delaware       2008      6.564     68.791              451.576     6.579      16.4                 10.640                   174.489          7\n",
        "Florida        2004     17.567     65.438             1149.534    16.785      14.7                 10.001                   147.016         -2\n",
        "               2008      8.871     61.469              545.316    19.843      14.7                 10.459                   153.747         -2\n",
        "Georgia        2004      9.592     62.648              600.944     5.316      15.4                 10.090                   155.393         -7\n",
        "               2008      4.851     59.074              286.579     7.208      15.4                 10.511                   161.874         -7\n",
        "Illinois       2004     12.078     67.831              819.276    12.322      15.8                 10.162                   160.553          8\n",
        "               2008      6.103     65.566              400.120    14.261      15.8                 10.617                   167.745          8\n",
        "Indiana        2004     12.381     85.838             1062.770     3.528      15.3                 10.053                   153.813         -6\n",
        "               2008      6.257     83.569              522.854     4.864      15.3                 10.476                   160.287         -6\n",
        "Iowa           2004     14.907     92.619             1380.633     2.818      15.4                  9.971                   153.560          1\n",
        "               2008      7.528     90.510              681.335     4.009      15.4                 10.467                   161.193          1\n",
        "Kansas         2004     13.251     83.097             1101.078     7.002      14.3                 10.006                   143.079        -12\n",
        "               2008      6.692     80.449              538.342     8.901      14.3                 10.485                   149.940        -12\n",
        "Kentucky       2004     12.489     89.268             1114.906     1.483      13.4                  9.950                   133.329        -10\n",
        "               2008      6.312     87.684              553.480     2.341      13.4                 10.350                   138.693        -10\n",
        "Louisiana      2008      5.846     61.314              358.445     3.405      14.6                 10.373                   151.445        -10\n",
        "Maine          2004     14.385     96.500             1388.181     0.734      16.8                  9.966                   167.425          5\n",
        "               2008      7.274     95.400              693.955     1.067      16.8                 10.437                   175.347          5\n",
        "Maryland       2004     11.315     62.051              702.124     4.303      12.2                 10.284                   125.465          9\n",
        "Massachusetts  2008      6.844     79.138              541.610     8.326      14.1                 10.746                   151.525         12\n",
        "Michigan       2004     12.266     78.550              963.475     3.259      15.7                 10.137                   159.157          4\n",
        "               2008      6.203     77.475              480.605     3.879      15.7                 10.515                   165.080          4\n",
        "Minnesota      2004     12.080     88.163             1064.992     2.915      13.4                 10.147                   135.965          2\n",
        "               2008      6.105     85.481              521.900     3.907      13.4                 10.630                   142.447          2\n",
        "Mississippi    2008      6.103     59.221              361.429     2.145      12.8                 10.268                   131.431        -10\n",
        "Missouri       2004     13.500     83.759             1130.780     2.120      15.9                  9.987                   158.800         -3\n",
        "               2008      6.821     82.279              561.246     2.910      15.9                 10.434                   165.905         -3\n",
        "Montana        2004     13.406     89.540             1200.377     2.004      14.5                  9.755                   141.441         -7\n",
        "               2008      6.779     88.520              600.080     2.552      14.5                 10.327                   149.744         -7\n",
        "Nevada         2004     10.956     65.207              714.406    19.716      17.4                 10.111                   175.933          1\n",
        "               2008      5.540     59.403              329.125    23.408      17.4                 10.601                   184.455          1\n",
        "New Hampshire  2004     11.974     95.102             1138.723     1.658      13.9                 10.162                   141.254          2\n",
        "               2008      6.057     93.651              567.231     2.279      13.9                 10.705                   148.803          2\n",
        "New Jersey     2004     13.229     66.044              873.703    13.277      16.7                 10.323                   172.402          4\n",
        "               2008      6.683     62.472              417.502    15.689      16.7                 10.822                   180.732          4\n",
        "New Mexico     2004     11.667     44.721              521.752    42.076      14.2                  9.874                   140.217          2\n",
        "               2008      5.901     42.460              250.577    44.388      14.2                 10.362                   147.136          2\n",
        "New York       2004     12.902     61.977              799.626    15.111      17.2                 10.175                   175.015         10\n",
        "               2008      6.520     59.988              391.096    16.556      17.2                 10.619                   182.655         10\n",
        "North Carolina 2004     12.039     70.157              844.612     4.708      13.4                 10.011                   134.151         -4\n",
        "               2008      6.085     67.578              411.245     6.654      13.4                 10.432                   139.787         -4\n",
        "Ohio           2004     13.281     84.013             1115.737     1.912      15.4                 10.084                   155.289         -1\n",
        "               2008      6.712     82.506              553.764     2.556      15.4                 10.482                   161.417         -1\n",
        "Oklahoma       2004     13.213     74.084              978.899     5.196      13.4                  9.901                   132.680        -17\n",
        "               2008      6.675     71.142              474.887     7.198      13.4                 10.357                   138.781        -17\n",
        "Oregon         2004     12.807     83.522             1069.661     8.047      13.7                 10.008                   137.108          4\n",
        "               2008      6.475     80.811              523.249    10.023      13.7                 10.484                   143.627          4\n",
        "Pennsylvania   2004     15.627     84.052             1313.482     3.209      12.4                 10.074                   124.915          2\n",
        "               2008      7.892     81.626              644.153     4.554      12.4                 10.520                   130.450          2\n",
        "Rhode Island   2004     14.538     81.887             1190.447     8.663      21.0                 10.086                   211.808         11\n",
        "South Carolina 2004     12.097     66.109              799.717     2.370      14.3                  9.979                   142.701         -8\n",
        "               2008      6.119     65.054              398.068     3.835      14.3                 10.397                   148.674         -8\n",
        "South Dakota   2004     14.325     88.043             1261.207     1.444      11.0                  9.867                   108.533         -9\n",
        "               2008      7.234     86.221              623.766     2.172      11.0                 10.399                   114.388         -9\n",
        "Tennessee      2004     12.362     79.200              979.077     2.177      15.4                  9.985                   153.770         -9\n",
        "               2008      6.250     77.300              483.089     3.438      15.4                 10.389                   159.994         -9\n",
        "Texas          2004      9.939     52.433              521.153    31.986      17.6                 10.005                   176.092        -10\n",
        "               2008      5.022     48.617              244.161    35.043      17.6                 10.488                   184.595        -10\n",
        "Utah           2008      4.305     82.686              355.964    11.113      11.7                 10.557                   123.520        -20\n",
        "Vermont        2008      6.441     95.179              613.000     1.252      16.3                 10.510                   171.308         13\n",
        "Virginia       2004     11.193     70.151              785.233     4.655      14.6                 10.141                   148.056         -2\n",
        "               2008      5.659     67.325              381.011     6.428      14.6                 10.678                   155.896         -2\n",
        "Washington     2004     11.234     78.934              886.753     7.491      14.8                 10.146                   150.166          5\n",
        "               2008      5.681     75.517              428.977     9.545      14.8                 10.630                   157.329          5\n",
        "West Virginia  2004     15.312     94.560             1447.906     0.679      12.8                  9.861                   126.215         -8\n",
        "               2008      7.737     93.780              725.579     0.990      12.8                 10.267                   131.419         -8\n",
        "Wisconsin      2004     13.098     87.284             1143.276     3.597      12.8                 10.069                   128.884          2\n",
        "               2008      6.619     85.192              563.858     4.848      12.8                 10.535                   134.845          2\n",
        "Wyoming        2008      5.905     87.182              514.852     7.757      16.7                 10.513                   175.563        -20"
       ]
      }
     ],
     "prompt_number": 95
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "false_disc = mod.outlier_test(\"fdr_bh\")\n",
      "false_disc.sort(\"unadj_p\", inplace=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 96,
       "text": [
        "                     student_resid  unadj_p  fdr_bh(p)\n",
        "State          year                                   \n",
        "Missouri       2008          2.125    0.037      0.748\n",
        "New Hampshire  2008          2.099    0.040      0.748\n",
        "Vermont        2008         -2.080    0.041      0.748\n",
        "Rhode Island   2004         -2.063    0.043      0.748\n",
        "Tennessee      2004         -2.032    0.046      0.748\n",
        "New Jersey     2008         -1.934    0.057      0.774\n",
        "Arizona        2008          1.861    0.067      0.778\n",
        "Connecticut    2008         -1.727    0.089      0.784\n",
        "Kentucky       2004         -1.657    0.102      0.784\n",
        "Maine          2004          1.553    0.125      0.784\n",
        "Illinois       2004          1.548    0.126      0.784\n",
        "California     2008          1.521    0.133      0.784\n",
        "Minnesota      2008         -1.500    0.138      0.784\n",
        "Oregon         2008          1.480    0.144      0.784\n",
        "               2004          1.400    0.166      0.784\n",
        "Maine          2008          1.392    0.169      0.784\n",
        "New Jersey     2004          1.371    0.175      0.784\n",
        "Michigan       2004         -1.345    0.183      0.784\n",
        "New Mexico     2004         -1.339    0.185      0.784\n",
        "Indiana        2004          1.313    0.194      0.784\n",
        "New York       2008          1.221    0.226      0.818\n",
        "Montana        2004          1.198    0.235      0.818\n",
        "Georgia        2004          1.188    0.239      0.818\n",
        "Kansas         2004          1.180    0.242      0.818\n",
        "Louisiana      2008         -1.142    0.258      0.831\n",
        "Missouri       2004         -1.120    0.267      0.831\n",
        "Pennsylvania   2004         -1.024    0.310      0.873\n",
        "Delaware       2008          1.014    0.314      0.873\n",
        "Illinois       2008         -0.966    0.338      0.873\n",
        "Ohio           2004          0.876    0.384      0.873\n",
        "North Carolina 2008          0.861    0.392      0.873\n",
        "Texas          2004          0.848    0.399      0.873\n",
        "Wisconsin      2004         -0.797    0.429      0.873\n",
        "Kansas         2008         -0.789    0.433      0.873\n",
        "Ohio           2008          0.768    0.445      0.873\n",
        "Virginia       2004         -0.764    0.447      0.873\n",
        "New York       2004          0.758    0.451      0.873\n",
        "California     2004         -0.742    0.461      0.873\n",
        "Michigan       2008         -0.730    0.468      0.873\n",
        "Texas          2008         -0.728    0.469      0.873\n",
        "New Mexico     2008         -0.724    0.472      0.873\n",
        "Florida        2004          0.723    0.472      0.873\n",
        "Nevada         2004         -0.721    0.473      0.873\n",
        "Mississippi    2008         -0.707    0.482      0.873\n",
        "Minnesota      2004         -0.703    0.485      0.873\n",
        "Washington     2008          0.563    0.576      0.951\n",
        "South Dakota   2004          0.550    0.584      0.951\n",
        "Indiana        2008         -0.542    0.590      0.951\n",
        "Alaska         2008          0.536    0.593      0.951\n",
        "Utah           2008         -0.536    0.593      0.951\n",
        "Arizona        2004         -0.520    0.605      0.951\n",
        "Wyoming        2008         -0.499    0.619      0.951\n",
        "Pennsylvania   2008         -0.492    0.624      0.951\n",
        "Kentucky       2008          0.447    0.656      0.951\n",
        "Nevada         2008          0.437    0.664      0.951\n",
        "Colorado       2008         -0.426    0.672      0.951\n",
        "New Hampshire  2004         -0.414    0.680      0.951\n",
        "Arkansas       2008         -0.380    0.705      0.951\n",
        "Alabama        2008          0.358    0.722      0.951\n",
        "Montana        2008         -0.357    0.722      0.951\n",
        "North Carolina 2004         -0.348    0.729      0.951\n",
        "Iowa           2004         -0.325    0.746      0.951\n",
        "Maryland       2004          0.304    0.762      0.951\n",
        "South Carolina 2004          0.302    0.764      0.951\n",
        "Tennessee      2008          0.278    0.782      0.951\n",
        "Georgia        2008         -0.251    0.802      0.951\n",
        "Iowa           2008         -0.197    0.844      0.951\n",
        "South Dakota   2008         -0.196    0.845      0.951\n",
        "Florida        2008          0.173    0.863      0.951\n",
        "Arkansas       2004          0.170    0.865      0.951\n",
        "Alabama        2004         -0.166    0.869      0.951\n",
        "West Virginia  2004          0.153    0.879      0.951\n",
        "Oklahoma       2008          0.150    0.881      0.951\n",
        "Virginia       2008          0.137    0.891      0.951\n",
        "Washington     2004         -0.133    0.895      0.951\n",
        "Colorado       2004         -0.121    0.904      0.951\n",
        "South Carolina 2008         -0.121    0.904      0.951\n",
        "Wisconsin      2008          0.096    0.924      0.960\n",
        "Massachusetts  2008         -0.075    0.940      0.964\n",
        "Oklahoma       2004          0.055    0.957      0.969\n",
        "West Virginia  2008         -0.005    0.996      0.996"
       ]
      }
     ],
     "prompt_number": 96
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bonf = mod.outlier_test(\"sidak\")\n",
      "bonf.sort(\"unadj_p\", inplace=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 97,
       "text": [
        "                     student_resid  unadj_p  sidak(p)\n",
        "State          year                                  \n",
        "Missouri       2008          2.125    0.037     0.954\n",
        "New Hampshire  2008          2.099    0.040     0.962\n",
        "Vermont        2008         -2.080    0.041     0.967\n",
        "Rhode Island   2004         -2.063    0.043     0.972\n",
        "Tennessee      2004         -2.032    0.046     0.978\n",
        "New Jersey     2008         -1.934    0.057     0.992\n",
        "Arizona        2008          1.861    0.067     0.996\n",
        "Connecticut    2008         -1.727    0.089     0.999\n",
        "Kentucky       2004         -1.657    0.102     1.000\n",
        "Maine          2004          1.553    0.125     1.000\n",
        "Illinois       2004          1.548    0.126     1.000\n",
        "California     2008          1.521    0.133     1.000\n",
        "Minnesota      2008         -1.500    0.138     1.000\n",
        "Oregon         2008          1.480    0.144     1.000\n",
        "               2004          1.400    0.166     1.000\n",
        "Maine          2008          1.392    0.169     1.000\n",
        "New Jersey     2004          1.371    0.175     1.000\n",
        "Michigan       2004         -1.345    0.183     1.000\n",
        "New Mexico     2004         -1.339    0.185     1.000\n",
        "Indiana        2004          1.313    0.194     1.000\n",
        "New York       2008          1.221    0.226     1.000\n",
        "Montana        2004          1.198    0.235     1.000\n",
        "Georgia        2004          1.188    0.239     1.000\n",
        "Kansas         2004          1.180    0.242     1.000\n",
        "Louisiana      2008         -1.142    0.258     1.000\n",
        "Missouri       2004         -1.120    0.267     1.000\n",
        "Pennsylvania   2004         -1.024    0.310     1.000\n",
        "Delaware       2008          1.014    0.314     1.000\n",
        "Illinois       2008         -0.966    0.338     1.000\n",
        "Ohio           2004          0.876    0.384     1.000\n",
        "North Carolina 2008          0.861    0.392     1.000\n",
        "Texas          2004          0.848    0.399     1.000\n",
        "Wisconsin      2004         -0.797    0.429     1.000\n",
        "Kansas         2008         -0.789    0.433     1.000\n",
        "Ohio           2008          0.768    0.445     1.000\n",
        "Virginia       2004         -0.764    0.447     1.000\n",
        "New York       2004          0.758    0.451     1.000\n",
        "California     2004         -0.742    0.461     1.000\n",
        "Michigan       2008         -0.730    0.468     1.000\n",
        "Texas          2008         -0.728    0.469     1.000\n",
        "New Mexico     2008         -0.724    0.472     1.000\n",
        "Florida        2004          0.723    0.472     1.000\n",
        "Nevada         2004         -0.721    0.473     1.000\n",
        "Mississippi    2008         -0.707    0.482     1.000\n",
        "Minnesota      2004         -0.703    0.485     1.000\n",
        "Washington     2008          0.563    0.576     1.000\n",
        "South Dakota   2004          0.550    0.584     1.000\n",
        "Indiana        2008         -0.542    0.590     1.000\n",
        "Alaska         2008          0.536    0.593     1.000\n",
        "Utah           2008         -0.536    0.593     1.000\n",
        "Arizona        2004         -0.520    0.605     1.000\n",
        "Wyoming        2008         -0.499    0.619     1.000\n",
        "Pennsylvania   2008         -0.492    0.624     1.000\n",
        "Kentucky       2008          0.447    0.656     1.000\n",
        "Nevada         2008          0.437    0.664     1.000\n",
        "Colorado       2008         -0.426    0.672     1.000\n",
        "New Hampshire  2004         -0.414    0.680     1.000\n",
        "Arkansas       2008         -0.380    0.705     1.000\n",
        "Alabama        2008          0.358    0.722     1.000\n",
        "Montana        2008         -0.357    0.722     1.000\n",
        "North Carolina 2004         -0.348    0.729     1.000\n",
        "Iowa           2004         -0.325    0.746     1.000\n",
        "Maryland       2004          0.304    0.762     1.000\n",
        "South Carolina 2004          0.302    0.764     1.000\n",
        "Tennessee      2008          0.278    0.782     1.000\n",
        "Georgia        2008         -0.251    0.802     1.000\n",
        "Iowa           2008         -0.197    0.844     1.000\n",
        "South Dakota   2008         -0.196    0.845     1.000\n",
        "Florida        2008          0.173    0.863     1.000\n",
        "Arkansas       2004          0.170    0.865     1.000\n",
        "Alabama        2004         -0.166    0.869     1.000\n",
        "West Virginia  2004          0.153    0.879     1.000\n",
        "Oklahoma       2008          0.150    0.881     1.000\n",
        "Virginia       2008          0.137    0.891     1.000\n",
        "Washington     2004         -0.133    0.895     1.000\n",
        "Colorado       2004         -0.121    0.904     1.000\n",
        "South Carolina 2008         -0.121    0.904     1.000\n",
        "Wisconsin      2008          0.096    0.924     1.000\n",
        "Massachusetts  2008         -0.075    0.940     1.000\n",
        "Oklahoma       2004          0.055    0.957     1.000\n",
        "West Virginia  2008         -0.005    0.996     1.000"
       ]
      }
     ],
     "prompt_number": 97
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "infl = mod.get_influence()\n",
      "table = infl.summary_frame()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 98
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for stat in table.columns:\n",
      "    print stat"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "dfb_Intercept\n",
        "dfb_C(kmeans_groups)[T.1]\n",
        "dfb_C(kmeans_groups)[T.3]\n",
        "dfb_C(kmeans_groups)[T.4]\n",
        "dfb_C(kmeans_groups)[T.5]\n",
        "dfb_C(kmeans_groups)[T.6]\n",
        "dfb_per_older\n",
        "dfb_per_white\n",
        "dfb_per_older:per_white\n",
        "dfb_per_hisp\n",
        "dfb_no_party\n",
        "dfb_np.log(median_income)\n",
        "dfb_no_party:np.log(median_income)\n",
        "dfb_PVI\n",
        "cooks_d\n",
        "dffits\n",
        "dffits_internal\n",
        "hat_diag\n",
        "standard_resid\n",
        "student_resid\n"
       ]
      }
     ],
     "prompt_number": 99
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Measure the influence of points on prediction\n",
      "\n",
      "$$\\text{DFFITS}=\\frac{\\hat{y}-\\hat{y}_{i}}{s_i\\sqrt{h_{ii}} }$$\n",
      "\n",
      "points greater than\n",
      "\n",
      "$$2\\left\\(\\frac{p}{\\text{nobs}} \\right\\)^{1/2}$$\n",
      "\n",
      "might be cause for concern"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print 2*np.sqrt(mod.df_model/mod.nobs)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.80123361677\n"
       ]
      }
     ],
     "prompt_number": 100
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dffits = np.abs(table['dffits'].copy())\n",
      "dffits.sort()\n",
      "dffits[::-1][:15]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 101,
       "text": [
        "State          year\n",
        "Rhode Island   2004    1.526\n",
        "Vermont        2008    1.133\n",
        "New Mexico     2004    1.022\n",
        "Arizona        2008    0.989\n",
        "California     2008    0.945\n",
        "New Jersey     2008    0.909\n",
        "New Hampshire  2008    0.872\n",
        "Alaska         2008    0.844\n",
        "Utah           2008    0.844\n",
        "Maine          2004    0.810\n",
        "Connecticut    2008    0.719\n",
        "Missouri       2008    0.704\n",
        "Maine          2008    0.611\n",
        "Tennessee      2004    0.605\n",
        "New Jersey     2004    0.565\n",
        "Name: dffits"
       ]
      }
     ],
     "prompt_number": 101
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Indicate influential observations, where you might want more data. \n",
      "\n",
      "Overall fit change with deleted observation.\n",
      "\n",
      "$$\\text{Cook's D}=\\frac{e_i^2}{p\\text{MSE}\\frac{h_{ii}}{(1-h_{ii})^2}}$$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print 4/mod.nobs"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0.0493827160494\n"
       ]
      }
     ],
     "prompt_number": 102
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cooks_d = table[\"cooks_d\"].copy()\n",
      "cooks_d.sort()\n",
      "print cooks_d[::-1][:15]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "State          year\n",
        "Rhode Island   2004    0.159\n",
        "Vermont        2008    0.087\n",
        "New Mexico     2004    0.074\n",
        "Arizona        2008    0.067\n",
        "California     2008    0.063\n",
        "New Jersey     2008    0.057\n",
        "New Hampshire  2008    0.052\n",
        "Alaska         2008    0.051\n",
        "Utah           2008    0.051\n",
        "Maine          2004    0.046\n",
        "Connecticut    2008    0.036\n",
        "Missouri       2008    0.034\n",
        "Maine          2008    0.026\n",
        "Tennessee      2004    0.025\n",
        "New Jersey     2004    0.023\n",
        "Name: cooks_d\n"
       ]
      }
     ],
     "prompt_number": 103
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "student_resid = np.abs(table.student_resid.copy())\n",
      "student_resid.sort()\n",
      "student_resid[::-1][:15]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 104,
       "text": [
        "State          year\n",
        "Missouri       2008    2.125\n",
        "New Hampshire  2008    2.099\n",
        "Vermont        2008    2.080\n",
        "Rhode Island   2004    2.063\n",
        "Tennessee      2004    2.032\n",
        "New Jersey     2008    1.934\n",
        "Arizona        2008    1.861\n",
        "Connecticut    2008    1.727\n",
        "Kentucky       2004    1.657\n",
        "Maine          2004    1.553\n",
        "Illinois       2004    1.548\n",
        "California     2008    1.521\n",
        "Minnesota      2008    1.500\n",
        "Oregon         2008    1.480\n",
        "               2004    1.400\n",
        "Name: student_resid"
       ]
      }
     ],
     "prompt_number": 104
    }
   ],
   "metadata": {}
  }
 ]
}