Democratic_Primary_Case_Study_HW.ipynb

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        "#Predicting the 2020 Democratic Primary Election\n",
        "\n",
        "##Case Overview: \n",
        "\n",
        "\n",
        "\n",
        "Imagine it is Spring 2020 and you are analyzing who will win the Democratic Primary:\n",
        "\n",
        "The 2020 Democratic Primary Election will be a pivotal moment in American (and world) history. As such, this is an interesting and significant race to analyze and better understand. In this case study, we will explore the Democratic primary election from a variety of angles with the ultimate goal of predicting the nominee. Leveraging various data sources will enable us to understand what may influence the result. \n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P_RrZ_iAWe_h"
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      "source": [
        "## DISCUSSION 1\n",
        "\n",
        "In the past, the outcome of political campaigns was predicted by political analysts and pundits, using a combination of their experience, intuition, and personal biases and preferences. In recent decades there has been a shift to a more scientific approach, in which election results are predicted statistically using a poll. A small random sample of voters is asked how they will vote, and from that the result of the entire election is extrapolated. \n",
        "\n",
        "\n",
        "\n",
        "Discuss what factors could have lead to this shift to a more scienfitic approach in determining the outcome of political campaigns?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9UJHd0b2Wew_"
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      "source": [
        "## DISCUSSION 2\n",
        "In practice, a poll's prediction must be taken with a grain of salt, because the sample is only approximately representative of the voting population. For example, in late October 2012, the Gallup poll consistently gave Romney a 6-percentage-point lead in the popular vote, but in fact Obama won the popular vote by 2.6 percentage points. On the other hand, RAND Corporation was biased toward the Democrats and tended to overstate Obama's lead by 1.5 percentage points. \n",
        "\n",
        "**What could lead to a sample (in terms of polls) not representing the population?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B5a3va7VWetd"
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      "source": [
        "## DISCUSSION 3\n",
        "One approach to overcome this bias is by averaging together the different polls. This is better than trusting any one of them, but it is still rather crude. What if most of them are biased? \n",
        "\n",
        "Silver's approach is very sophisticated, but its key idea is to combine different polls using a weighted average. In a normal average, each data point contributes equally to the result. In a weighted average, some data points contribute more than others. Silver examined how well each polling organization had predicted previous elections, and then weighted their polls according to their accuracy: more biased pollsters had less effect on the weighted average.\n",
        "\n",
        "**Can you think of some things to consider when determining the weight of a poll (i.e. how accurate/reliable is this poll)?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZMFVURo7rrUm"
      },
      "source": [
        "#Bias in Survey Sampling\n",
        "\n",
        "In survey sampling, bias refers to the tendency of a sample statistic to systematically over- or under-estimate a population parameter. \n",
        "\n",
        "\n",
        "###Bias Due to Unrepresentative Samples\n",
        "\n",
        "\n",
        "- Undercoverage\n",
        "\n",
        "\n",
        "- Nonresponse bias. \n",
        "\n",
        "\n",
        "- Voluntary response bias.\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x98a2dAWrWcz"
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      "source": [
        "## DISCUSSION 4\n",
        "\n",
        "**What methods could potentially overcome selection bias?**\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "## Bias Due to Measurement Error\n",
        "\n",
        "A poor measurement process can also lead to bias. In survey research, the measurement process includes the environment in which the survey is conducted,the way that questions are asked, and the state of the survey respondent.\n",
        "\n",
        "## DISCUSSION 5 \n",
        "**Can you give some examples of response bias?**\n",
        "\n"
      ]
    },
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      "source": [
        "##Test Your Understanding\n",
        "\n",
        "\n",
        "Which of the following statements are true?\n",
        "\n",
        "\n",
        "\n",
        "1. Random sampling is a good way to reduce response bias. \n",
        "2. To guard against bias from undercoverage, use a convenience sample.\n",
        "3. Increasing the sample size tends to reduce survey bias. \n",
        "4. To guard against nonresponse bias, use a mail-in survey.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "* (A) 1 only \n",
        "* (B) 2 only \n",
        "* (C) 3 only \n",
        "* (D) 4 only \n",
        "* (E) None of the above.\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4nipPFTqvFyU"
      },
      "source": [
        "##Now to the fun stuff!"
      ]
    },
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      "cell_type": "code",
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        "#@title Load modules\n",
        "## Load your libraries\n",
        "## Libraries, also called \"modules\", contain functions and methods already written \n",
        "## by someone else. All you have to do is pass your argument(s) and call them yourself!\n",
        "\n",
        "\n",
        "## Load the pandas module and give it the name 'pd'.\n",
        "## Pandas is a software library written for the Python programming language for data manipulation and analysis.\n",
        "import pandas as pd \n",
        "\n",
        "## Load the python plotting modules\n",
        "## We don't need to import the entire matplotlib. We can just import the plotting utilities.\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "##Load the numpy module. Numpy is \n",
        "import numpy as np\n",
        "\n",
        "## Load the datetime module\n",
        "import datetime\n",
        "\n",
        "##We might load other modules along the way."
      ],
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        "#@title Fetch Polling Data\n",
        "##Now we'll fetch our polling data from FiveThirtyEight. The data for this case study is hosted at the following link. \n",
        "##Pandas has a read_csv function which takes the data_link as an argument.\n",
        "\n",
        "data_link = \"https://projects.fivethirtyeight.com/polls-page/president_primary_polls.csv\"\n",
        "\n",
        "poll_df = pd.read_csv(data_link)\n",
        "\n",
        "##Display the first 10 rows.\n",
        "poll_df.head(n=10)"
      ],
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              "      <td>Sanders</td>\n",
              "      <td>13257</td>\n",
              "      <td>Bernard Sanders</td>\n",
              "      <td>28.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>121424</td>\n",
              "      <td>65687</td>\n",
              "      <td>2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1361</td>\n",
              "      <td>SSRS</td>\n",
              "      <td>143</td>\n",
              "      <td>CNN</td>\n",
              "      <td>CNN/SSRS</td>\n",
              "      <td>297.0</td>\n",
              "      <td>SSRS</td>\n",
              "      <td>B/C</td>\n",
              "      <td>462.0</td>\n",
              "      <td>rv</td>\n",
              "      <td>rv-d</td>\n",
              "      <td>Live Phone</td>\n",
              "      <td>U.S. President</td>\n",
              "      <td>4/3/20</td>\n",
              "      <td>4/6/20</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>4/9/20 11:03</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://cdn.cnn.com/cnn/2020/images/04/09/rel4...</td>\n",
              "      <td>primary</td>\n",
              "      <td>DEM</td>\n",
              "      <td>Biden</td>\n",
              "      <td>13256</td>\n",
              "      <td>Joseph R. Biden Jr.</td>\n",
              "      <td>65.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>121424</td>\n",
              "      <td>65687</td>\n",
              "      <td>2020</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1361</td>\n",
              "      <td>SSRS</td>\n",
              "      <td>143</td>\n",
              "      <td>CNN</td>\n",
              "      <td>CNN/SSRS</td>\n",
              "      <td>297.0</td>\n",
              "      <td>SSRS</td>\n",
              "      <td>B/C</td>\n",
              "      <td>462.0</td>\n",
              "      <td>rv</td>\n",
              "      <td>rv-d</td>\n",
              "      <td>Live Phone</td>\n",
              "      <td>U.S. President</td>\n",
              "      <td>4/3/20</td>\n",
              "      <td>4/6/20</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>4/9/20 11:03</td>\n",
              "      <td>NaN</td>\n",
              "      <td>https://cdn.cnn.com/cnn/2020/images/04/09/rel4...</td>\n",
              "      <td>primary</td>\n",
              "      <td>DEM</td>\n",
              "      <td>Sanders</td>\n",
              "      <td>13257</td>\n",
              "      <td>Bernard Sanders</td>\n",
              "      <td>30.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   question_id  poll_id  cycle  ... candidate_id       candidate_name    pct\n",
              "0       127126    67903   2020  ...        13256  Joseph R. Biden Jr.  54.00\n",
              "1       127126    67903   2020  ...        13257      Bernard Sanders  17.00\n",
              "2       122156    65938   2020  ...        13256  Joseph R. Biden Jr.  67.98\n",
              "3       122156    65938   2020  ...        13257      Bernard Sanders  23.66\n",
              "4       121552    65756   2020  ...        13256  Joseph R. Biden Jr.  49.00\n",
              "5       121552    65756   2020  ...        13257      Bernard Sanders  31.00\n",
              "6       121390    65691   2020  ...        13256  Joseph R. Biden Jr.  49.00\n",
              "7       121390    65691   2020  ...        13257      Bernard Sanders  28.00\n",
              "8       121424    65687   2020  ...        13256  Joseph R. Biden Jr.  65.00\n",
              "9       121424    65687   2020  ...        13257      Bernard Sanders  30.00\n",
              "\n",
              "[10 rows x 33 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OuJzODHJRted",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6016efee-7c86-4c5a-c7c1-0e8903d44485"
      },
      "source": [
        "## Notice that the poll_id is repeated several times.\n",
        "len(poll_df['poll_id'].unique())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1140"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JMzOyatcRtky",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "07738f92-097f-464e-b642-3a75063c63ab"
      },
      "source": [
        "#@title What features/metrics are available in this dataset? Let's take a look at the columns. \n",
        "## What features/metrics are available in this dataset? Let's take a look at the columns. \n",
        "list(poll_df.columns)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['question_id',\n",
              " 'poll_id',\n",
              " 'cycle',\n",
              " 'state',\n",
              " 'pollster_id',\n",
              " 'pollster',\n",
              " 'sponsor_ids',\n",
              " 'sponsors',\n",
              " 'display_name',\n",
              " 'pollster_rating_id',\n",
              " 'pollster_rating_name',\n",
              " 'fte_grade',\n",
              " 'sample_size',\n",
              " 'population',\n",
              " 'population_full',\n",
              " 'methodology',\n",
              " 'office_type',\n",
              " 'start_date',\n",
              " 'end_date',\n",
              " 'sponsor_candidate',\n",
              " 'internal',\n",
              " 'partisan',\n",
              " 'tracking',\n",
              " 'nationwide_batch',\n",
              " 'created_at',\n",
              " 'notes',\n",
              " 'url',\n",
              " 'stage',\n",
              " 'party',\n",
              " 'answer',\n",
              " 'candidate_id',\n",
              " 'candidate_name',\n",
              " 'pct']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XsHB_9WHSREF"
      },
      "source": [
        "## Discussion 6:\n",
        "\n",
        " >  ### Which metrics can provide us with the most useful insight?\n",
        " >  ### If you had to choose only one, which would it be? \n",
        " >  ### Do any of the columns above look unusual?\n",
        " \n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c-DhfIYgRtyq",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7e7d0537-63b8-4bc5-8a84-67e0012263d5"
      },
      "source": [
        "#@title Name of the Polls\n",
        "##Print out the names of the polls.\n",
        "\n",
        "list_polls = list(poll_df['pollster'].unique())\n",
        "\n",
        "print(list_polls)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Winston Group', 'Emerson College', 'YouGov', 'SSRS', 'Morning Consult', 'Sacred Heart University', 'IBD/TIPP', 'Harris Insights & Analytics', 'Marquette University Law School', 'ABC News/The Washington Post', 'Echelon Insights', 'Ipsos', 'Lord Ashcroft Polls', 'Swayable', 'AtlasIntel', 'Marist College', 'Monmouth University', 'University of Georgia', 'Point Blank Political', 'NBC News/The Wall Street Journal', 'Gravis Marketing', 'Latino Decisions', 'ROI Rocket', 'Hofstra University', 'Public Policy Polling', 'University of North Florida', 'Chism Strategies', 'Data for Progress', 'Optimus', 'Victory Research', 'Mitchell Research & Communications', 'Ogden & Fry', 'Target Insyght', 'Concord Public Opinion Partners', 'St. Pete Polls', 'Quinnipiac University', 'Florida Atlantic University', 'OurProgress (The Progress Campaign)', 'SurveyUSA', 'EPIC-MRA', 'Remington Research Group', 'OH Predictive Insights', 'Change Research', 'Spry Strategies', 'Glengariff Group', 'Elucd', 'Suffolk University', 'East Carolina University', 'Gonzales Research & Marketing Strategies Inc.', 'High Point University', 'Trafalgar Group', 'Starboard Communications', 'Fox News/Beacon Research/Shaw & Co. Research', 'MassINC Polling Group', 'RMG Research', 'Univision/University of Houston/Latino Decisions', 'University of Texas at Tyler', 'Magellan Strategies', 'University of California, Berkeley', 'Clemson University', 'Meredith College', 'Christopher Newport University', 'Saint Leo University', 'Americana Analytics', 'Florida Southern College', 'SoonerPoll.com', 'Mason-Dixon Polling & Strategy', 'Siena College', 'University of Massachusetts Lowell', 'Goucher College', 'Winthrop University', 'Evan Falchuk and Lou DiNatale', 'Elway Research', 'The Tyson Group', 'Southern Illinois University', 'Public Policy Institute of California', 'Fairleigh Dickinson University (PublicMind)', 'Beacon Research', 'Zogby Interactive/JZ Analytics', 'WPA Intelligence (WPAi)', 'Cole Hargrave Snodgrass & Associates', 'SocialSphere', 'Landmark Communications', 'McLaughlin & Associates', 'Braun Research', 'American Research Group', 'University of New Hampshire', 'Capitol Weekly', 'RKM Research and Communications Inc.', 'Hendrix College', 'Saint Anselm College', 'David Binder Research', 'Civiqs', 'Park Street Strategies', 'USC Dornsife/Los Angeles Times', 'YouGov Blue/Data for Progress', 'Franklin & Marshall College', 'Siena College/The New York Times Upshot', 'Morningside College', 'Baldwin Wallace University', 'Civis Analytics', 'Texas Lyceum', 'Pew Research Center', 'Neighborhood Research and Media', 'GQR Research (GQRR)', 'Tulchin Research', 'Myers Research/Strategic Services', 'Selzer & Co.', 'Patinkin Research Strategies', 'KG Polling', 'Climate Nexus', 'University of Iowa', 'Mellman Group', 'St. Cloud State University', \"Maine People's Resource Center\", 'Montana State University Billings', 'Kaiser Family Foundation', 'Public Religion Research Institute', 'Susquehanna Polling & Research Inc.', 'Avalanche Strategy', 'Denno Research', 'Show Me Victories', 'Research America Inc.', 'Bendixen & Amandi International', 'Univision/University of Houston/Latino Decisions/North Star Opinion Research', 'Benenson Strategy Group', 'Ragnar Research Partners', 'TargetPoint/GQR Research', 'Claster Consulting', 'SurveyMonkey', 'Hampton University', 'Crantford Research', 'GBAO', 'We Ask America', 'Muhlenberg College', 'D-CYFOR', 'Bold Blue Campaigns', 'Praecones Analytica']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pUMD_ZiZR1o4",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f83422d-14f0-4b3c-f74e-3dccb0387007"
      },
      "source": [
        "#@title Display the unique values from some columns.\n",
        "## Display the unique values in each of the following columns:\n",
        "\n",
        "## fte_grade\n",
        "## methodology\n",
        "## office_type\n",
        "## stage\n",
        "\n",
        "\n",
        "for column_name in ['fte_grade','methodology','office_type','stage']:\n",
        "  list_returned = list(poll_df[column_name].unique())\n",
        "  print(\"Column Name: \", column_name)\n",
        "  print(list_returned)\n",
        "  print(\"---------------\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Column Name:  fte_grade\n",
            "['B/C', 'A-', 'B', 'A/B', 'C', 'A+', nan, 'B-', 'D-', 'C/D', 'C-', 'B+', 'F', 'A', 'C+']\n",
            "---------------\n",
            "Column Name:  methodology\n",
            "['Live Phone', 'IVR/Online', 'Online', 'Live Phone/Online', 'Online/Text', 'Live Phone/Online/Text', 'IVR/Text', 'Text', 'Live Phone/Text', 'Automated Phone', 'IVR/Live Phone', nan, 'IVR/Online/Text', 'IVR/Live Phone/Text', 'Online/IVR']\n",
            "---------------\n",
            "Column Name:  office_type\n",
            "['U.S. President']\n",
            "---------------\n",
            "Column Name:  stage\n",
            "['primary', 'caucus']\n",
            "---------------\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c17HEmHmSov7"
      },
      "source": [
        "## Discussion 7 \n",
        "PCT is the percentage of participants that chose that answer for a given question and poll id. \n",
        "\n",
        "**What should pct sum up to? Why?**\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MktFynU6PcHe"
      },
      "source": [
        "\n",
        "\n",
        "### Our main columns of interest? \n",
        "\n",
        "1. Candidate name\n",
        "2. PCT\n",
        "3. Pollster Rating\n",
        "\n",
        "Let's begin by doing some initial visualization to get a feel for the data. Our goal is to create a function that takes a poll id and graphs the pct for each candidate.\n",
        "\n",
        "We'll do this step by step"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f3pXt-H_Qxdy",
        "cellView": "form"
      },
      "source": [
        "#@title Code hidden.\n",
        "## Now let's take everything we learned and put it into a function that\n",
        "## we can call instead of having to replicate those same lines of code everytime\n",
        "## we'd like to graph the polling percentage.\n",
        "import pdb\n",
        "\n",
        "def graph_poll_id(argument_poll_id):\n",
        "  #pdb.set_trace()\n",
        "  #Only get the rows where the column poll_id matches the poll_id passed in as an argument.\n",
        "  series_poll = poll_df[poll_df[\"poll_id\"]==argument_poll_id]\n",
        "  \n",
        "  #Sort\n",
        "  sorted_series_poll = series_poll.sort_values(by=\"pct\")\n",
        "\n",
        "  \n",
        "  ## Second we will remove candidates who have zero percent from the graph.\n",
        "  ## We'll do this by filtering the data such that we only consider rows where\n",
        "  ## the percentage is greater than 0.\n",
        "\n",
        "  ## Let's also give each candidate a different color.\n",
        "  filtered_series_poll = sorted_series_poll[sorted_series_poll['pct'] > 0]\n",
        "\n",
        "  bars = filtered_series_poll['candidate_name']\n",
        "  y_pos = np.arange(len(bars))\n",
        "  performance = filtered_series_poll['pct'] \n",
        "\n",
        "  ## try changing the orders of the colors yourself!\n",
        "  \n",
        "  colors = 'rgbkymc'  #red, green, blue, black, etc.\n",
        "  plt.barh(y_pos, performance, color=colors,align='center', alpha=0.5)\n",
        "  plt.yticks(y_pos, bars)\n",
        "  plt.xlabel('PCT')\n",
        "  \n",
        "  \n",
        "  ## Third we will we will add some metadata (poll name, sample size, date) to give more context.\n",
        "  date_of_poll = list(filtered_series_poll['start_date'])[0]\n",
        "  poll_name = list(filtered_series_poll['pollster'])[0]\n",
        "  \n",
        "  sample_size =  list(filtered_series_poll['sample_size'])[0]\n",
        "  \n",
        "  title_of_graph = \"Polling Percentage \\n Poll Name: {} \\n Date of Poll: {} \\n Sample Size: {}\\n \".format(poll_name,date_of_poll,sample_size)\n",
        "  plt.title(title_of_graph)\n",
        "\n",
        "  plt.show()\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TPNkjwoBQxnY",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "5346e21c-7451-4fe1-8237-9ea0f799ae67"
      },
      "source": [
        "#@title Show results for 5 A polls.\n",
        "\n",
        "list_A_minus_poll_ids = list(poll_df[poll_df['fte_grade'] == \"A-\"][\"poll_id\"].unique())\n",
        "\n",
        "print(list_A_minus_poll_ids)\n",
        "\n",
        "## To get the first 5 elements in a list: use splicing list[:5]\n",
        "for poll_id in list_A_minus_poll_ids[:5]:\n",
        "  graph_poll_id(poll_id)\n",
        "  \n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[65938, 64893, 64834, 64816, 64817, 64818, 64732, 64645, 64646, 64590, 64591, 64481, 64503, 64350, 64347, 63488, 63489, 63483, 63487, 63479, 63471, 63454, 63448, 63437, 63429, 63426, 63427, 63407, 63355, 63360, 63338, 63350, 63298, 63291, 63263, 63244, 63245, 63246, 63224, 63139, 63151, 63115, 63060, 62978, 62715, 62685, 62628, 62629, 62538, 62539, 62536, 62537, 62508, 62233, 60106, 60065, 60082, 59938, 59820, 59857, 59858, 59642, 59611, 59591, 59593, 59583, 59541, 59522, 59443, 58930, 58906, 58821, 58759, 58731, 58648, 58646, 58623, 58535, 58467, 58399, 58187, 58085, 58058, 57962, 57884, 57834, 57799, 57788, 57742, 57741, 57712, 57697, 57656, 57612, 57574, 57532, 57445, 57374, 57075]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:30: MatplotlibDeprecationWarning: Using a string of single character colors as a color sequence is deprecated. Use an explicit list instead.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:30: MatplotlibDeprecationWarning: Using a string of single character colors as a color sequence is deprecated. Use an explicit list instead.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:30: MatplotlibDeprecationWarning: Using a string of single character colors as a color sequence is deprecated. Use an explicit list instead.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:30: MatplotlibDeprecationWarning: Using a string of single character colors as a color sequence is deprecated. Use an explicit list instead.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:30: MatplotlibDeprecationWarning: Using a string of single character colors as a color sequence is deprecated. Use an explicit list instead.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F_YCRtRtRLfm"
      },
      "source": [
        "## Reminder: Polls are approximate! \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BcSOVakFRb54"
      },
      "source": [
        "## First, let's look at who the candidates are more closely."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nwflCmnnRYLH",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "435df9e5-d0b1-462f-c11c-dc2e5a63a4d3"
      },
      "source": [
        "#@title Who are the candidates? How many candidates are there?\n",
        "## Who are the candidates? \n",
        "candidates_object = poll_df[\"candidate_name\"].unique()\n",
        "print(candidates_object)\n",
        "\n",
        "## How many candidates are there? \n",
        "len(candidates_object)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Joseph R. Biden Jr.' 'Bernard Sanders' 'Tulsi Gabbard'\n",
            " 'Elizabeth Warren' 'Michael Bloomberg' 'Pete Buttigieg' 'Amy Klobuchar'\n",
            " 'Donald Trump' 'William F. Weld' 'Tom Steyer' 'Andrew Yang'\n",
            " 'Roque De La Fuente' 'Michael F. Bennet' 'Cory A. Booker' 'Julián Castro'\n",
            " 'John K. Delaney' 'Joe Sestak' 'Marianne Williamson' 'Joe Walsh'\n",
            " 'Deval Patrick' 'Mosie Boyd' 'Michael Ellinger' 'Mark Stewart Greenstein'\n",
            " 'Mike Pence' 'Kamala D. Harris' 'Robert Wells' 'Nathan Bloxham'\n",
            " \"Beto O'Rourke\" 'Steve Bullock' 'Wayne Messam' 'Kirsten E. Gillibrand'\n",
            " 'Mike Gravel' 'Eric Swalwell' 'Tim Ryan' 'Hillary Rodham Clinton'\n",
            " 'Bill de Blasio' 'Jay Robert Inslee' 'John Kerry' 'Mark Sanford'\n",
            " 'John Hickenlooper' 'Mitt Romney' 'John Richard Kasich' 'Michelle Obama'\n",
            " 'Seth Moulton' 'Terry R. McAuliffe' 'Nimrata R. Haley'\n",
            " 'Stacey Yvonne Abrams' 'Bob Corker' 'Marco Rubio' 'Larry Hogan'\n",
            " 'Howard Schultz' 'Barack Obama' 'Jeff Flake' 'Jamie Dimon'\n",
            " 'Dwayne Johnson' 'Margaret Wood Hassan' 'Michael Avenatti' 'Andrew Cuomo'\n",
            " 'Ben Sasse' 'Eric H. Holder' 'Sherrod Brown' 'Oprah Winfrey'\n",
            " 'Mark Zuckerberg' 'Tim Kaine' 'Eric Garcetti' 'Gavin Newsom' 'Ted Cruz'\n",
            " 'Nancy Pelosi' 'Gary Johnson' 'Joseph Kennedy III' 'Dennis J. Kucinich'\n",
            " 'Jia Lee' 'Robert C. Scott' 'Kyrsten Sinema' 'Mark R. Warner'\n",
            " 'Richard Neece Ojeda' 'Jeb Bush' 'Paul Ryan' 'Charles D. Baker']\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "79"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Mg9rO4CTzll"
      },
      "source": [
        "## DISCUSSION 8\n",
        "\n",
        "###1.   Any unusual names there?\n",
        "###2.   Any names you haven't heard of?\n",
        "###3.   Any names that dropped out?\n",
        "###4.   Any names that do not belong to the Democratic party?\n",
        "\n",
        "Since the group of those that are not running is much larger than those that are still running, we will create a list of those candidates that are still running.\n",
        "We will only consider those candidates. \n",
        "\n",
        "As of March 6, 2020, there are only two candidates left in the primary. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qB7n36zqUjtN",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cc88fc24-1878-466e-c92f-f31e47b94fc4"
      },
      "source": [
        "#@title Candidates still left in the primary\n",
        "## \n",
        "list_candidates = list(candidates_object[:2])\n",
        "print(list_candidates)\n",
        "print(len(list_candidates))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Joseph R. Biden Jr.', 'Bernard Sanders']\n",
            "2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oB-V6RxzZ8MR"
      },
      "source": [
        "# Let's get a polling average.\n",
        "\n",
        "Since there's a lot of polling data, let's focus on the polls from the first half of 2019. This means we will only consider polls conducted between Jan. 1 and June 30, 2019. \n",
        "\n",
        "Our goal is to match the following numbers [in this 538 chart](https://fivethirtyeight.com/features/what-weve-gleaned-about-the-democratic-primary-from-6-months-of-polls/). See the chart with the title: \"How the 2020 Democratic primary field looks six months in.\"\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f1Pi-oVzbvgy",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "outputId": "c2042d08-ef95-4377-fde6-9f87481662a5"
      },
      "source": [
        "#@title Get Polling Average from November 1, 2019 to March 5, 2020\n",
        "## First look at the columns we'll use to filter the data. \n",
        "## Columns: 'start_date' and 'end_date'\n",
        "#print(poll_df['start_date'])\n",
        "\n",
        "#print(\"-------------------this is just to seperate the two print statements----------------\")\n",
        "\n",
        "## DIY: Print the end_date column \n",
        "## Remove next line\n",
        "#print(poll_df['end_date'])\n",
        "## Now for us to be able to do manipulations (comparisons) on this data\n",
        "## we need to format it first. We can use pandas to change the formatting.\n",
        "## The returned value of pd.datetime is a timestamp object for comparisons\n",
        "## i.e. its not just a string. \n",
        "\n",
        "## Create a new column called formatted_start_date use the format m/d/y\n",
        "\n",
        "poll_df['formatted_start_date'] = pd.to_datetime(poll_df['start_date'], format='%m/%d/%y')\n",
        "\n",
        "##DIY: create the formatted end date column\n",
        "## Remove next line\n",
        "poll_df['formatted_end_date'] = pd.to_datetime(poll_df['end_date'], format='%m/%d/%y')\n",
        "## See the difference? \n",
        "poll_df['formatted_start_date'] \n",
        "## Now our conditions are that the poll's start date must be \n",
        "## between 1/1/2019 and 6/30/2019. \n",
        "\n",
        "## Let's create a january 1st timestamp object with the same format\n",
        "## as \"formatted_start_date\" column.\n",
        "\n",
        "#jan_1st_date = pd.to_datetime(\"1/1/19\", format='%m/%d/%y')\n",
        "jan_1st_date = pd.to_datetime(\"11/1/19\", format='%m/%d/%y')\n",
        "#print(jan_1st_date)\n",
        "#print(type(jan_1st_date))\n",
        "## DIY: Create the june 30, 2019 timestamp same as above. \n",
        "## Remove next line\n",
        "#june_30th_date = pd.to_datetime(\"6/30/19\", format='%m/%d/%y')\n",
        "june_30th_date = pd.to_datetime(\"3/5/20\", format='%m/%d/%y')\n",
        "#print(june_30th_date)\n",
        "\n",
        "## Now we will create boolean conditions to filter our pandas dataframe\n",
        "\n",
        "#Anytime on or after 1/1/19\n",
        "condition_1 = (poll_df['formatted_start_date'] >= jan_1st_date)\n",
        "\n",
        "## DIY: Create the condition for anytime on or before 6/30/19\n",
        "# Remove next line\n",
        "condition_2 = (poll_df['formatted_start_date'] <= june_30th_date)\n",
        "\n",
        "#Only consider democratic candidates!\n",
        "condition_3 = (poll_df['party'] == \"DEM\")\n",
        "\n",
        "#Only consider those candidates that are still running.\n",
        "condition_4 = (poll_df['candidate_name'].isin(list_candidates))\n",
        "\n",
        "# Apply the conditions! F for filtered.\n",
        "f_poll_df = poll_df[condition_1 & condition_2 & condition_3 & condition_4]\n",
        "\n",
        "## print out the filtered data frame\n",
        "#print(f_poll_df)\n",
        "\n",
        "## Now we will only look at the two important columns: candidate_name and pct\n",
        "f_poll_df = f_poll_df[['candidate_name','pct']]\n",
        "\n",
        "## print it out now. which columns are showing?\n",
        "#print(f_poll_df)\n",
        "\n",
        "## Now group by candidate name\n",
        "grouped_poll_df = f_poll_df.groupby(\"candidate_name\")\n",
        "\n",
        "## For each group (candidate), take the mean.\n",
        "mean_groups = grouped_poll_df.mean()\n",
        "\n",
        "## Then sort the values by pct. \n",
        "mean_groups.sort_values(by=\"pct\",ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>pct</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>candidate_name</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Bernard Sanders</th>\n",
              "      <td>24.540376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Joseph R. Biden Jr.</th>\n",
              "      <td>24.436170</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                           pct\n",
              "candidate_name                \n",
              "Bernard Sanders      24.540376\n",
              "Joseph R. Biden Jr.  24.436170"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QqbucFBAgxu6"
      },
      "source": [
        "\n",
        "\n",
        "We just added another layer of information by \n",
        "1. using the PCT\n",
        "2. averaging the PCTs\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-m2ehThtkFUN"
      },
      "source": [
        "## What about a weighted average? \n",
        "\n",
        "\n",
        "### What is a weighted average?\n",
        "\n",
        "A weighted average or mean is one where each item being averaged is multiplied by a number (weight) based on the item's relative importance, rather than treating each item equally. The weights or weightings are the equivalent of having that many similar items with the same value involved in the average. The result is summed and the total is divided by the sum of the weights.\n",
        "\n",
        "#### What is the weight in an arithmetic average? \n",
        "An arithmetic average can be considered a special case where all values are valued (weighted) equally.\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cfDUB1mBWrsv"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TiJLAOvXWsJA"
      },
      "source": [
        "## Discussion 9\n",
        "Give an everyday example of a weighted average in real life. \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xeIW8HWq3z4h"
      },
      "source": [
        "##Which weighting scheme will we use? \n",
        "\n",
        "Older data contains more random noise than recent data: You not only have the random noise from sampling error, but also from the changes in polls that build up over time. By reducing the weight of this less reliable data, we diminish that noise.\n",
        "\n",
        "If we keep reducing the weight of old data, though, we eventually hit a point where we start throwing out more useful information than random noise, and going any further starts to make our estimate worse. \n",
        "\n",
        "Weighting depends partially on how much the polls changes from day to day, but it also depends on how much you can tell about the underlying trends from your data. In general, the more your recent observations tell you about the underlying candidate favorability, or the more data you have, the more aggressively you can discount past results.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NZeQ-I24xgLw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "outputId": "edbb4065-b9ae-4b10-c046-9fdc177eb54b"
      },
      "source": [
        "\n",
        "f_poll_df = poll_df[condition_1 & condition_2 & condition_3 & condition_4]\n",
        "\n",
        "f_poll_df['step'] = june_30th_date - f_poll_df['formatted_start_date']\n",
        "\n",
        "\n",
        "f_poll_df = f_poll_df[['candidate_name','formatted_start_date','pct','step']]\n",
        "f_poll_df\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  after removing the cwd from sys.path.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>candidate_name</th>\n",
              "      <th>formatted_start_date</th>\n",
              "      <th>pct</th>\n",
              "      <th>step</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Joseph R. Biden Jr.</td>\n",
              "      <td>2020-03-04</td>\n",
              "      <td>55.0</td>\n",
              "      <td>1 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Bernard Sanders</td>\n",
              "      <td>2020-03-04</td>\n",
              "      <td>45.0</td>\n",
              "      <td>1 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Joseph R. Biden Jr.</td>\n",
              "      <td>2020-03-04</td>\n",
              "      <td>52.0</td>\n",
              "      <td>1 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Bernard Sanders</td>\n",
              "      <td>2020-03-04</td>\n",
              "      <td>48.0</td>\n",
              "      <td>1 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Joseph R. Biden Jr.</td>\n",
              "      <td>2020-03-04</td>\n",
              "      <td>34.0</td>\n",
              "      <td>1 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5107</th>\n",
              "      <td>Bernard Sanders</td>\n",
              "      <td>2019-11-01</td>\n",
              "      <td>15.0</td>\n",
              "      <td>125 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5123</th>\n",
              "      <td>Joseph R. Biden Jr.</td>\n",
              "      <td>2019-11-01</td>\n",
              "      <td>24.0</td>\n",
              "      <td>125 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5124</th>\n",
              "      <td>Bernard Sanders</td>\n",
              "      <td>2019-11-01</td>\n",
              "      <td>15.0</td>\n",
              "      <td>125 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5236</th>\n",
              "      <td>Joseph R. Biden Jr.</td>\n",
              "      <td>2019-11-01</td>\n",
              "      <td>26.0</td>\n",
              "      <td>125 days</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5238</th>\n",
              "      <td>Bernard Sanders</td>\n",
              "      <td>2019-11-01</td>\n",
              "      <td>14.0</td>\n",
              "      <td>125 days</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>949 rows × 4 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           candidate_name formatted_start_date   pct     step\n",
              "0     Joseph R. Biden Jr.           2020-03-04  55.0   1 days\n",
              "1         Bernard Sanders           2020-03-04  45.0   1 days\n",
              "2     Joseph R. Biden Jr.           2020-03-04  52.0   1 days\n",
              "3         Bernard Sanders           2020-03-04  48.0   1 days\n",
              "4     Joseph R. Biden Jr.           2020-03-04  34.0   1 days\n",
              "...                   ...                  ...   ...      ...\n",
              "5107      Bernard Sanders           2019-11-01  15.0 125 days\n",
              "5123  Joseph R. Biden Jr.           2019-11-01  24.0 125 days\n",
              "5124      Bernard Sanders           2019-11-01  15.0 125 days\n",
              "5236  Joseph R. Biden Jr.           2019-11-01  26.0 125 days\n",
              "5238      Bernard Sanders           2019-11-01  14.0 125 days\n",
              "\n",
              "[949 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8PFc6RUyFyhC"
      },
      "source": [
        "## Exponentially Decaying Weights \n",
        "\n",
        "\n",
        "We will use exponentially decaying weights to weight more recent polls heavier than older polls. We will use this exponential decay function: α(1−α)^t\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n8z-iDMxJ7L9",
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "ba07704c-5f8e-410c-c58c-43c142988e68"
      },
      "source": [
        "#@title Calculate Weighted Average\n",
        "\n",
        "#@title Define get weight function\n",
        "#We will use this exponential decay function: α(1−α)^t\n",
        "## source #https://www.itl.nist.gov/div898/handbook/pmc/section4/pmc431.htm\n",
        "import math\n",
        "def get_weight(a,t):\n",
        "  base = math.fabs(1-a)\n",
        "  val = a*math.pow(base,t)\n",
        "  #print(\"a: {}, t: {} , val:{}\".format(a,t,val))\n",
        "  return val\n",
        "\n",
        "\n",
        "# see https://www.itl.nist.gov/div898/handbook/pmc/section4/pmc431.htm\n",
        "sp = (june_30th_date - f_poll_df['formatted_start_date'])\n",
        "##To use the function above, we need to calculate the time change, or delta, which will be the \n",
        "##step size, or t. \n",
        "deltas = sp.apply(lambda x: x.days)\n",
        "\n",
        "##Try using t as is, and try using t/7 (a week of information, sort of normalization\n",
        "##How do your results change?\n",
        "weights = deltas.apply(lambda x: get_weight(a=1.9,t=x/7))\n",
        "f_poll_df['weights'] = weights\n",
        "f_poll_df['weights'] \n",
        "##Group by candidate and get the mean pct. Then sort the values.\n",
        "\n",
        "grouped = f_poll_df.groupby(\"candidate_name\")\n",
        "\n",
        "grouped.mean().sort_values(by=\"pct\",ascending=False)['pct']\n",
        "\n",
        "##Now let's calculate a weighted average. \n",
        "\n",
        "weighted_averages = {}\n",
        "\n",
        "\n",
        "for candidate in grouped.groups:\n",
        "  \n",
        "  group = grouped.get_group(candidate)\n",
        "  \n",
        "  weights_list = []\n",
        "  pct_list = []\n",
        "  for row_number in range(len(group)):\n",
        "    row = group.values[row_number]\n",
        "    \n",
        "    pct = row[2]\n",
        "    weight = row[4]\n",
        "    weights_list.append(weight)\n",
        "    pct_list.append(pct)\n",
        "    \n",
        "  \n",
        "  \n",
        "  weighted_average = np.average(a=pct_list,weights=weights_list)\n",
        " \n",
        "  ## Save the results in a dictionary for displaying later\n",
        "  weighted_averages[candidate] = weighted_average\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "##Sort the results we got above, then print them out.\n",
        "sorted_w_avgs = sorted(weighted_averages.items(), key=lambda x: x[1], reverse=True)\n",
        "\n",
        "#for candidate, weighted_avg in sorted_w_avgs:\n",
        "#  print(candidate, weighted_avg)\n",
        "\n",
        "## Here they are again side by side:\n",
        "\n",
        "unweighted_averages = grouped.mean().sort_values(by=\"pct\",ascending=False)['pct']\n",
        "\n",
        "weighted_averages_series = pd.Series(dict(sorted_w_avgs))\n",
        "\n",
        "combined_df = pd.DataFrame(weighted_averages_series)\n",
        "combined_df.columns = [\"weighted_average\"]\n",
        "combined_df['unweighted_average'] = unweighted_averages\n",
        "combined_df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>weighted_average</th>\n",
              "      <th>unweighted_average</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Bernard Sanders</th>\n",
              "      <td>26.555994</td>\n",
              "      <td>24.540376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Joseph R. Biden Jr.</th>\n",
              "      <td>23.994748</td>\n",
              "      <td>24.436170</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     weighted_average  unweighted_average\n",
              "Bernard Sanders             26.555994           24.540376\n",
              "Joseph R. Biden Jr.         23.994748           24.436170"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8a_piWSLVl-y"
      },
      "source": [
        "\n",
        "## Next we will do a fun vizualization using a map of the United States.\n",
        "\n",
        "Oftentimes, it's easier to vizualize certain data using more than just a graph. For example, our polling data includes geographic information (state in which the participants were polled). \n",
        "\n",
        "One way to incorporate this information is by using a chloropleth. A chloropleth map is a map in which areas are shaded or patterned in proportion to the measurement of the statiscial variable being displayed on the map, such as population density or per-capita income.\n",
        "\n",
        "Choropleth maps provide an easy way to visualize how a measurement varies across a geographic area or show the level of variability within a region.\n",
        "\n",
        "##We will use a chloropleth map of the USA to vizualize the leader in each state (those that we have data for).\n",
        "\n"
      ]
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      "source": [
        "#@title Map of Each State's (Weighted Avg) Poll Leader\n",
        "## Try grouping by state on this same filtered data.\n",
        "\n",
        "## SOLUTION (will be hidden/removed)\n",
        "\n",
        "filtered_poll_df = poll_df[condition_1 & condition_2 & condition_3][['state','candidate_name','pct']]\n",
        "\n",
        "grouped_state_poll_df = filtered_poll_df.groupby([\"state\",'candidate_name'])\n",
        "\n",
        "\n",
        "mean_state_df = grouped_state_poll_df.mean().sort_values(by=\"pct\",ascending=False)\n",
        "\n",
        "\n",
        "##Let's print Bernie's average in Alabama.\n",
        "#print(\"Bernie's average in Alabama\", mean_state_df['pct']['Alabama']['Bernard Sanders'])\n",
        "\n",
        "## Your turn! Print Elizabeth Warren's average in Massachussetts and California.\n",
        "\n",
        "##SOLUTION (will be removed/hidden later)\n",
        "#print(mean_state_df['pct']['California']['Elizabeth Warren'])\n",
        "#print(mean_state_df['pct']['Massachusetts']['Elizabeth Warren'])\n",
        "\n",
        "\n",
        "dict_state = {}\n",
        "\n",
        "list_means = []\n",
        "for name, group in grouped_state_poll_df:\n",
        "  \n",
        "  \n",
        "    state,candidate = name\n",
        "    data = (candidate, group.mean()[0])\n",
        "    if state not in dict_state.keys():\n",
        "      dict_state[state] = [data]\n",
        "    else:\n",
        "      dict_state[state].append(data)\n",
        "\n",
        "\n",
        "\n",
        "us_state_abbrev = {\n",
        "    'Alabama': 'AL',\n",
        "    'Alaska': 'AK',\n",
        "    'Arizona': 'AZ',\n",
        "    'Arkansas': 'AR',\n",
        "    'California': 'CA',\n",
        "    'Colorado': 'CO',\n",
        "    'Connecticut': 'CT',\n",
        "    'Delaware': 'DE',\n",
        "    'District of Columbia': 'DC',\n",
        "    'Florida': 'FL',\n",
        "    'Georgia': 'GA',\n",
        "    'Hawaii': 'HI',\n",
        "    'Idaho': 'ID',\n",
        "    'Illinois': 'IL',\n",
        "    'Indiana': 'IN',\n",
        "    'Iowa': 'IA',\n",
        "    'Kansas': 'KS',\n",
        "    'Kentucky': 'KY',\n",
        "    'Louisiana': 'LA',\n",
        "    'Maine': 'ME',\n",
        "    'Maryland': 'MD',\n",
        "    'Massachusetts': 'MA',\n",
        "    'Michigan': 'MI',\n",
        "    'Minnesota': 'MN',\n",
        "    'Mississippi': 'MS',\n",
        "    'Missouri': 'MO',\n",
        "    'Montana': 'MT',\n",
        "    'Nebraska': 'NE',\n",
        "    'Nevada': 'NV',\n",
        "    'New Hampshire': 'NH',\n",
        "    'New Jersey': 'NJ',\n",
        "    'New Mexico': 'NM',\n",
        "    'New York': 'NY',\n",
        "    'North Carolina': 'NC',\n",
        "    'North Dakota': 'ND',\n",
        "    'Northern Mariana Islands':'MP',\n",
        "    'Ohio': 'OH',\n",
        "    'Oklahoma': 'OK',\n",
        "    'Oregon': 'OR',\n",
        "    'Palau': 'PW',\n",
        "    'Pennsylvania': 'PA',\n",
        "    'Puerto Rico': 'PR',\n",
        "    'Rhode Island': 'RI',\n",
        "    'South Carolina': 'SC',\n",
        "    'South Dakota': 'SD',\n",
        "    'Tennessee': 'TN',\n",
        "    'Texas': 'TX',\n",
        "    'Utah': 'UT',\n",
        "    'Vermont': 'VT',\n",
        "    'Virgin Islands': 'VI',\n",
        "    'Virginia': 'VA',\n",
        "    'Washington': 'WA',\n",
        "    'West Virginia': 'WV',\n",
        "    'Wisconsin': 'WI',\n",
        "    'Wyoming': 'WY',\n",
        "}\n",
        "## Let's get the leader in each state so we can plot it!\n",
        "list_of_rows = []\n",
        "data_max_states = {}\n",
        "\n",
        "for state in dict_state.keys():\n",
        "  list_state_means = dict_state[state]\n",
        "  max_for_state = max(list_state_means, key=lambda x:x[1])\n",
        "  candidate, pct = max_for_state\n",
        "  state_abbrev = us_state_abbrev[state]\n",
        "  \n",
        "  print(state_abbrev,candidate, pct)\n",
        "  row = [state_abbrev,candidate, pct]\n",
        "  list_of_rows.append(row)\n",
        "  #data_max_states[state] = max_for_state\n",
        "\n",
        "df_new = pd.DataFrame(list_of_rows,columns=[\"State\",\"Candidate\",\"PCT\"])\n",
        "\n",
        "\n",
        "\n",
        "#We will need to install Plotly first on our virtual environments. Use !pip install plotly\n",
        "!pip install plotly==4.1.0\n",
        "\n",
        "##Then load the module.\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "##Create the figure object\n",
        "fig = go.Figure(data=go.Choropleth(\n",
        "    locations=df_new['State'], # Spatial coordinates\n",
        "    z = df_new['PCT'].astype(float), # Data to be color-coded\n",
        "    text = df_new[\"Candidate\"],\n",
        "    locationmode = 'USA-states', # set of locations match entries in `locations`\n",
        "    colorscale = 'Blues',\n",
        "    #mode=\"text\",\n",
        "    colorbar_title = \"PCT Percentage\",\n",
        "))\n",
        "\n",
        "fig.update_layout(\n",
        "    title_text = 'Leaders in Polls',\n",
        "    geo_scope='usa', # limite map scope to USA\n",
        ")\n",
        "\n",
        "\n",
        "##Show the figure. You can hover and zoom!\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
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            "AL Joseph R. Biden Jr. 44.65\n",
            "AR Joseph R. Biden Jr. 27.433333333333334\n",
            "CA Bernard Sanders 32.54318181818182\n",
            "CO Bernard Sanders 31.160000000000004\n",
            "CT Joseph R. Biden Jr. 33.0\n",
            "DE Joseph R. Biden Jr. 35.0\n",
            "FL Joseph R. Biden Jr. 33.345454545454544\n",
            "GA Joseph R. Biden Jr. 34.525\n",
            "IL Bernard Sanders 20.866666666666667\n",
            "IA Bernard Sanders 22.02727272727273\n",
            "ME Bernard Sanders 32.15\n",
            "MD Bernard Sanders 24.0\n",
            "MA Bernard Sanders 22.25\n",
            "MI Joseph R. Biden Jr. 24.066666666666666\n",
            "MN Bernard Sanders 25.75\n",
            "MO Joseph R. Biden Jr. 36.50666666666667\n",
            "NV Bernard Sanders 25.416666666666668\n",
            "NH Bernard Sanders 24.81272727272727\n",
            "NJ Bernard Sanders 24.566666666666666\n",
            "NM Bernard Sanders 28.2\n",
            "NY Joseph R. Biden Jr. 22.333333333333332\n",
            "NC Joseph R. Biden Jr. 25.535\n",
            "OH Joseph R. Biden Jr. 32.1\n",
            "OK Joseph R. Biden Jr. 23.34\n",
            "PA Joseph R. Biden Jr. 24.433333333333334\n",
            "SC Joseph R. Biden Jr. 33.9\n",
            "TN Joseph R. Biden Jr. 31.1\n",
            "TX Bernard Sanders 25.960869565217394\n",
            "UT Bernard Sanders 26.404999999999998\n",
            "VT Bernard Sanders 52.13333333333333\n",
            "VA Joseph R. Biden Jr. 33.9625\n",
            "WA Bernard Sanders 23.630000000000003\n",
            "WI Bernard Sanders 23.314285714285713\n",
            "Collecting plotly==4.1.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/63/2b/4ca10995bfbdefd65c4238f9a2d3fde33705d18dd50914dd13302ec1daf1/plotly-4.1.0-py2.py3-none-any.whl (7.1MB)\n",
            "\u001b[K     |████████████████████████████████| 7.1MB 3.8MB/s \n",
            "\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from plotly==4.1.0) (1.12.0)\n",
            "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.6/dist-packages (from plotly==4.1.0) (1.3.3)\n",
            "\u001b[31mERROR: cufflinks 0.17.0 has requirement plotly>=4.1.1, but you'll have plotly 4.1.0 which is incompatible.\u001b[0m\n",
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        "# PART 2: DEBATE ANALYSIS \n",
        "\n",
        "##In this section we will see how the Democratic debates afftected the polling averages! \n",
        "\n",
        "#18.1 million ...\n",
        "people watched the second round of the Democratic debate across NBC, MSNBC, and Telemundo, The Los Angeles Times reported, making it the MOST WATCHED Democratic debate in television history — and even more people tuned in to online livestreams. The previous viewership record for a Democratic debate took place in October 2015, when five Democratic candidates faced off as 15.4 million people watched. On Thursday night, there were twice as many candidates, and more viewers than ever before. [source](https://www.bustle.com/p/how-many-people-watched-the-democratic-debate-night-two-set-a-record-18157398)\n",
        "\n",
        "![](https://drive.google.com/uc?id=1j8baXf9lV2jOjc6J-Az8Zq0WN9VioDxh)\n",
        "\n",
        "\n",
        "\n",
        "The goal for this section is to use what we learned above to calculate a weighted average before and after the debate. Then we will calculate the net change between the those time periods to see how the debate might have impacted a candidates favorability in the polls. \n",
        "\n"
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        "### For this section we will use FiveThirtyEight's pre-calculated weights for our weighted average. In addition, we will only consider a certain set of polls. \n",
        "\n",
        "According to Nate Silver:\n",
        "\n",
        ">\"So far, there have been five national polls conducted entirely after the debate that allow for a direct comparison to an earlier poll by the same pollster. These are the polls from Quinnipiac University, Ipsos, Morning Consult,1 YouGov and HarrisX.2 \n",
        "\n",
        "> I weighted the polls based on sample size and the pollster’s rating, as we do for our Trump approval tracker and in our election models.\"\n",
        "\n",
        "See below for the polls and their associated weights.\n",
        "\n",
        "\n",
        "Polls included in the weighted average are Quinnipiac (weight 1.40), Ipsos (1.49),  Morning Consult (2.13), \n",
        "HarrisX (1.16) and YouGov (0.94).  Only candidates who participated in the debates are listed in the table.\n",
        "\n",
        "Note: The second debate occured on July 30 and 31st.\n",
        "\n",
        "\n",
        "\n"
      ]
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        "#@title Pre (1 week) and Post (1 week) Debate (2/25/2020) Analysis\n",
        "weights_dict = {\n",
        "    'Quinnipiac University': 1.40,\n",
        "    \"Ipsos\": 1.49,\n",
        "    \"Morning Consult\": 2.13,\n",
        "    'HarrisX': 1.16,\n",
        "    \"YouGov\": 0.94\n",
        "  \n",
        "}\n",
        "\n",
        "candidates_participated = ['Joseph R. Biden Jr.',\n",
        " 'Bernard Sanders',\n",
        " 'Elizabeth Warren',\n",
        " '#Kamala D. Harris',\n",
        " 'Pete Buttigieg',\n",
        " #\"Beto O'Rourke\",\n",
        " #'Cory A. Booker',\n",
        " #'Julián Castro',\n",
        " 'Amy Klobuchar']\n",
        " #'Kirsten E. Gillibrand',\n",
        " #'John K. Delaney',\n",
        " #'Tulsi Gabbard',\n",
        " #'John Hickenlooper',\n",
        " #'Jay Robert Inslee',\n",
        " #'Marianne Williamson',\n",
        " #'Andrew Yang',\n",
        " #'Wayne Messam', #didn't make it.\n",
        " #'Seth Moulton',#didn't make it.\n",
        " #'Tim Ryan',\n",
        " #'Michael F. Bennet',\n",
        " #'Steve Bullock',\n",
        " #'Bill de Blasio'\n",
        " #'Joe Sestak',#didn't make it.\n",
        " #'Tom Steyer'#didn't make it.\n",
        "                  #  ]\n",
        "\n",
        "## Remind ourselves of all the pollsters in our original dataset.\n",
        "#poll_df['pollster'].unique()\n",
        "\n",
        "\n",
        "\n",
        "##We'll reuse the code above for a more precise investigation of the pcts.\n",
        "\n",
        "def get_weighted_averages(f_poll_df):\n",
        "  f_poll_df = f_poll_df[['candidate_name','pct','pollster']]\n",
        "  grouped = f_poll_df.groupby(\"candidate_name\")\n",
        "  weighted_averages = {}\n",
        "\n",
        "\n",
        "  for candidate in grouped.groups:\n",
        "   \n",
        "    group = grouped.get_group(candidate)\n",
        "    #print(candidate,group)\n",
        "    weights_list = []\n",
        "    pct_list = []\n",
        "    for row_number in range(len(group)):\n",
        "      row = group.values[row_number]\n",
        "      \n",
        "      pct = row[1]\n",
        "      pollster = row[2]\n",
        "      weight = weights_dict[pollster]\n",
        "      weights_list.append(weight)\n",
        "      pct_list.append(pct)\n",
        "\n",
        "\n",
        "\n",
        "    weighted_average = np.average(a=pct_list,weights=weights_list)\n",
        "    #np.mean(np.multiply(pct_list,weights_list))\n",
        "    #weighted_average = np.mean(np.multiply(pct_list,weights_list))\n",
        "    \n",
        "\n",
        "    ## Save the results in a dictionary for displaying later\n",
        "    weighted_averages[candidate] = weighted_average\n",
        "\n",
        "\n",
        "  sorted_w_avgs = sorted(weighted_averages.items(), key=lambda x: x[1], reverse=True)\n",
        "\n",
        "  for candidate, weighted_avg in sorted_w_avgs:\n",
        "    print(candidate, weighted_avg)\n",
        "  return sorted_w_avgs\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "  #### Recycling and modularizing the code from before. \n",
        "\n",
        "##We'll pass the following as arguments to our function:\n",
        "## - entire dataframe (this contains our polling data)\n",
        "## - debate num (as of now only 2 debates have occured (6/26,27 and 7/30,31))\n",
        "from datetime import datetime, timedelta\n",
        "\n",
        "\n",
        "def before_after_debate(poll_df,debate_num):\n",
        "  \n",
        "  ## Now we will create boolean conditions to filter our pandas dataframe\n",
        "  if debate_num == 1:\n",
        "    print(\"Will analyze debate ONE \\n\")\n",
        "    night_one = pd.to_datetime(\"6/26/19\", format='%m/%d/%y')\n",
        "    night_two = pd.to_datetime(\"6/27/19\", format='%m/%d/%y')\n",
        "  elif debate_num == 2:\n",
        "    print(\"Will analyze debate TWO \\n\")\n",
        "    night_one = pd.to_datetime(\"7/30/19\", format='%m/%d/%y')\n",
        "    night_two = pd.to_datetime(\"7/31/19\", format='%m/%d/%y')\n",
        "    days_to_subtract = 7\n",
        "    timeframe_before = night_one - timedelta(days=days_to_subtract)\n",
        "    \n",
        "  elif debate_num == 3:\n",
        "    \n",
        "    night_one = pd.to_datetime(\"2/25/20\", format='%m/%d/%y')\n",
        "    night_two = pd.to_datetime(\"2/25/20\", format='%m/%d/%y')\n",
        "    days_to_subtract = 7\n",
        "    timeframe_before = night_one - timedelta(days=days_to_subtract)\n",
        "  \n",
        "  #aug_7_date = pd.to_datetime(\"8/07/19\", format='%m/%d/%y')\n",
        "  \n",
        "  #condition_before = (poll_df['formatted_start_date'] < night_one)\n",
        "  condition_before = (poll_df['formatted_start_date'] < night_one) & (poll_df['formatted_start_date'] > timeframe_before)\n",
        "  \n",
        "  \n",
        "  ##to copy Nate Silver, do only until august 7th\n",
        "  #condition_after = (poll_df['formatted_start_date'] > night_two) & (poll_df['formatted_end_date'] < aug_7_date) \n",
        "  #condition_after = (poll_df['formatted_start_date'] >= night_one) & (poll_df['formatted_end_date'] <= aug_7_date) \n",
        "  condition_after = (poll_df['formatted_start_date'] >= night_one) & (poll_df['formatted_end_date'] <= pd.to_datetime(\"3/1/20\", format='%m/%d/%y')) \n",
        "  \n",
        "  ## The pollster is one of the 5 listed above.\n",
        "  condition_1 = (poll_df['pollster'].isin(weights_dict.keys()))\n",
        " \n",
        "  #Only consider democratic candidates!\n",
        "  condition_2 = (poll_df['party'] == \"DEM\")\n",
        "\n",
        "  #Only consider those candidates that are still running.\n",
        "  condition_3 = (poll_df['candidate_name'].isin(candidates_participated))\n",
        "  print(candidates_participated)\n",
        "  \n",
        "  # Apply the conditions! F for filtered.\n",
        "\n",
        "  common_conditions = (condition_1 & condition_2 & condition_3)\n",
        "\n",
        "  data_before =  poll_df[common_conditions & condition_before]\n",
        "  data_after =  poll_df[common_conditions & condition_after]\n",
        "  \n",
        "  ## Perform some basic checks on our filtered datasets. \n",
        "  ## check the columns \n",
        "  #print(data_before.head(n=5))\n",
        "  #print(data_after.head(n=5))\n",
        "  \n",
        "  ###\n",
        "  #print(data_before['formatted_start_date'].unique())\n",
        "  \n",
        "  ##check the number of rows\n",
        "  #print(len(data_before))\n",
        "  #print(len(data_after))\n",
        "  ##check that we only use the specified pollsters\n",
        "  #print(data_before[\"pollster\"].unique())\n",
        "  #print(data_after[\"pollster\"].unique())\n",
        "  \n",
        "  sorted_before_w_avgs = get_weighted_averages(data_before)\n",
        "  #print(\"-----------\\n\\n----------\") #for seperation \n",
        "  sorted_after_w_avgs = get_weighted_averages(data_after)\n",
        "\n",
        "  return sorted_before_w_avgs, sorted_after_w_avgs\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "## Call our function and save the results so we can use them later!\n",
        "\n",
        "sorted_before_w_avgs, sorted_after_w_avgs = before_after_debate(poll_df, 3)\n",
        "\n",
        "\n",
        "\n",
        "#sorted_before_w_avgs.keys ==sorted_after_w_avgs.keys\n",
        "# we need to create 3 seperate lists for ordering purposes so we cn presnet the data accurately.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "print(len(sorted_before_w_avgs)==len(sorted_after_w_avgs))\n",
        "cand_names = []\n",
        "\n",
        "before_pcts = []\n",
        "\n",
        "after_pcts = []\n",
        "\n",
        "net_changes = []\n",
        "\n",
        "for i in range(len(sorted_before_w_avgs)):\n",
        "  tuple_before = sorted_before_w_avgs[i]\n",
        "  cand_b = tuple_before[0]\n",
        "  \n",
        "  pct_before = tuple_before[1]\n",
        "  \n",
        "  ##look for the pct after the debate for that candidate\n",
        "  pct_after = [tuple_a[1] for tuple_a in sorted_after_w_avgs if tuple_a[0] == cand_b][0]\n",
        "  \n",
        "  net_change = pct_after - pct_before\n",
        "  #print(cand_b, pct_before, pct_after, net_change)\n",
        "  cand_names.append(cand_b)\n",
        "  before_pcts.append(pct_before)\n",
        "  after_pcts.append(pct_after)\n",
        "  net_changes.append(net_change)\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "## Load the python module with table generating functions\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "#fig = go.Figure(data=[go.Table(header=dict(values=['Candidate Name', 'Before Debate','After Debate','Net Change']),\n",
        "#                 cells=dict(values=[cand_names, before_pcts, after_pcts, net_changes]))\n",
        "#                     ])\n",
        "#fig.show()\n",
        "\n",
        "## Limit the pcts and net changes to 2 decimal points only.\n",
        "\n",
        "##the round function takes as an argument the element to be rounded and the number of digits to round to. \n",
        "## in our case we'd like to keep only 2 decimal points. \n",
        "\n",
        "rounded_before_pcts = [round(element,2) for element in before_pcts]\n",
        "rounded_after_pcts = [round(element,2) for element in after_pcts]\n",
        "rounded_net_changes = [round(element,2) for element in net_changes]\n",
        "\n",
        "##TBD \n",
        "##Could be DIY \n",
        "\n",
        "#Decimal Red, Green, Blue, and Opacity Color Codes\n",
        "#This page demonstrates the decimal representation of color of the form rgba(R,G,B,a), where R, G, and B are the decimal \n",
        "#values for the red, green, and blue values of the color on the range 0 to 255 and a is the opacity of the color (a = 0 = transparent; a = 1 = opaque). \n",
        "\n",
        "#https://www.december.com/html/spec/colorrgbadec.html\n",
        "\n",
        "##Use the link above and try changing the colors and the opacity! \n",
        "##DIY\n",
        "\n",
        "fig = go.Figure(data=[go.Table(header=dict(values=['Candidate Name', 'Before Debate','After Debate','Net Change']),\n",
        "                 cells=dict(values = [cand_names, rounded_before_pcts, rounded_after_pcts, rounded_net_changes],\n",
        "                           fill = dict(color=['rgb(245,245,245)',#unique color for the first column\n",
        "                                              'rgb(245,245,245)',#unique color for the second column\n",
        "                                              'rgb(245,245,245)',#unique color for the third column\n",
        "                                             ['rgba(0,250,0, 0.3)' if val>=0 else 'rgba(250,0,0, 0.3)' for val in rounded_net_changes] \n",
        "                                             \n",
        "                                             \n",
        "                                             ]\n",
        "                               #the cells in the second column colored with green or red, according to their values\n",
        "                                         ))\n",
        "                              \n",
        "                              \n",
        "                              )\n",
        "                     ])\n",
        "fig.show()\n"
      ],
      "execution_count": null,
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            "['Joseph R. Biden Jr.', 'Bernard Sanders', 'Elizabeth Warren', '#Kamala D. Harris', 'Pete Buttigieg', 'Amy Klobuchar']\n",
            "Bernard Sanders 28.24950298210736\n",
            "Joseph R. Biden Jr. 18.054671968190853\n",
            "Elizabeth Warren 11.927435387673958\n",
            "Pete Buttigieg 10.033797216699803\n",
            "Amy Klobuchar 4.305168986083499\n",
            "Bernard Sanders 30.062344139650875\n",
            "Joseph R. Biden Jr. 22.445137157107233\n",
            "Elizabeth Warren 14.164588528678305\n",
            "Pete Buttigieg 9.296758104738155\n",
            "Amy Klobuchar 4.203241895261844\n",
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        "id": "2QpJaRTMef43"
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      "source": [
        "## DISCUSSION 10\n",
        "- How did the trends change before and after the democratic debate ? \n",
        "- Are the changes real or random fluctuations/noise? \n",
        "- Can we conclusively say that the net changes are directly due to the debates? Why or why not?\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qwqBqdtPb1XZ"
      },
      "source": [
        "# PART 3: Linear Regression Model - Primary Winner vs. Trump?\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S37WJttrod7d"
      },
      "source": [
        "# **An Analysis of Presidential Approval Ratings**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DTB-WR9DR_rQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
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        "cellView": "form",
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      "source": [
        "#@title Show Approval Ratings\n",
        "# Midterm election happens at: 365*2 - 79 = 651 days into office\n",
        "\n",
        "# Approval Rating Data\n",
        "# The dates during a presidency when approval ratings are measured:\n",
        "dates = [4,30,60,90,120,150,180,210,240,270,300,330,360,390,420,450,480,510,540,570,600,630,651]\n",
        "\n",
        "# Trump\n",
        "trump_approval_rate = [45.5,44.3,42.6,41.9,38.9,38.7,38.7,37.9,38.8, 38.1,36.5,39.1,41.4, 40.5,40.8,42.3,42.3,41.4,42.1,41.9,40,41.8,42.0]\n",
        "\n",
        "# Obama (lost both house and senate in midterm)\n",
        "obama_approval_rate = [68,62.7,59.2,60.2,60.4,57.9,55.1,52,53.2, 52.6, 52.1, 49.7,49.2,48.1,48.1,47.9,49.4,47.8,47, 45.9, 45.2, 45.2,44.9]\n",
        "\n",
        "# George W Bush\n",
        "gb_approval_rate = [46,52.2,55.7,54.1,54.4,52.2, 52.8,52.3,82.7,88.1,85.8,83.2,81.2,78.3,77.8,73.8,70.3,71.2,69.4,65.8,64.8,62.6,60.1]\n",
        "\n",
        "# Clinton (lost both house and senate in midterm)\n",
        "clinton_approval_rate = [54,53.2,55.4,53.4,47.9,42.9,45.7,44.2,48.3,49.7,46.9,54.5,56.2,55.4,50.9,53.3,54.2,50.8,46,45.3,43.2,43.9,45.4]\n",
        "\n",
        "# George H. W. Bush (lost both house and senate in midterm)\n",
        "ghwb_approval_rate = [61,61,59.1,58,61.9,70.2,67.1,68.8,69.5,67.7,69.8,71.2,78.3,73.3,62.2,68.1,66.8,67.9,63.2,59.8,75.9,66,54.9]\n",
        "\n",
        "# Reagan (lost house, same senate)\n",
        "reagan_approval_rate = [54,54,60,67.6,68,59,58.1,60.1,60.1,55.6,53.3,49.2,48.9,46.9,46.9,45,43.6,45,44,41.1,42,42,42]\n",
        "\n",
        "# Carter\n",
        "carter_approval_rate = [69.8,66,74.9,62.8,64.9,62.9,62,60.7,54.3,58.5,54.5,57,55,52.1,50.1,48,42.1,44,39.9,39,42.1,48.1,49]\n",
        "\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "# Before performing the regressions, we need to \"reshape\" the date data to transpose from being a horizontal list to\n",
        "# a vertical vector of numbers... this is just something that sklearn requires\n",
        "dates = np.array(dates).reshape(-1,1)\n",
        "linear_regressor = LinearRegression()  # create object from the class\n",
        "markers = [\".\", \"^\", \"v\", \"s\", \"8\", \"p\", \"P\"]\n",
        "names = [\"Trump\", \"Obama\", \"G.W. Bush\", \"Clinton\", \"G.H.W. Bush\", \"Reagan\", \"Carter\"]\n",
        "ratings = [trump_approval_rate, \n",
        "          obama_approval_rate, \n",
        "          gb_approval_rate, \n",
        "          clinton_approval_rate, \n",
        "          ghwb_approval_rate, \n",
        "          reagan_approval_rate, \n",
        "          carter_approval_rate]\n",
        "\n",
        "# loop through all the ratings and add a scatter plot + regression line to the plot\n",
        "for i, approval_rate in enumerate(ratings):\n",
        "  plt.scatter(dates, approval_rate, marker=markers[i], label = names[i])\n",
        "  linear_regressor.fit(dates, approval_rate)  # perform linear regression\n",
        "  pred = linear_regressor.predict(dates)  # make predictions\n",
        "  plt.plot(dates, pred)\n",
        "\n",
        "plt.title(\"Approval Ratings\")\n",
        "plt.xlabel(\"Days Since Start of Presidency\")\n",
        "plt.ylabel(\"Approval Rating as Percentage\")\n",
        "plt.legend()\n",
        "plt.gcf().set_size_inches(15, 9)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
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5QtEjD/H6iv/hWN1h3ljxJEWPPETDkSM9cn2v18vq1au56qqrAPjb3/5GeXk5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KOF6KO/T6w/QiwWJvbSjxCL2CgFaVK3MYHPfQuFRq9amq5QTWkpA8EA5y52cPp0K20njnP1wnnA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\niIiIiIjksf8LOsyJi0TA5u4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1080x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "be_tq5qYSVNi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "3e2571f8-3ee8-4543-c735-adafcbe8c0dd"
      },
      "source": [
        "#@title Fitting of a linear regression to each president. Calculating relative % change in approval from start of presidency to day 420\n",
        "# loop through all ratings and create a separate scatter plot + regression line for each one\n",
        "for i, approval_rate in enumerate(ratings):\n",
        "  plt.scatter(dates, approval_rate)\n",
        "  linear_regressor.fit(dates, approval_rate)  # perform linear regression\n",
        "  pred = linear_regressor.predict(dates)  # make predictions\n",
        "  plt.plot(dates, pred)\n",
        "  \n",
        "  # Calculate the relative % change in approval from start of presidency to day 420\n",
        "  diff = round(int(approval_rate[14] - approval_rate[0])/ int(approval_rate[0]) * 100)\n",
        "  plt.title(\"Approval Rating: {} Changed by {}% From Start to Month 16\".format(names[i], diff))\n",
        "  plt.xlabel(\"Days Since Start of Presidency\")\n",
        "  plt.ylabel(\"Approval Rating as Percentage\")\n",
        "  plt.gcf().set_size_inches(8, 6)\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Vx62EYM2ylREQsDKqxkWaVxBpXKS/rBouTMfq6Yvzjfy5AFaurL28lYVpVtdl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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jeDsxU/HOYASzUxKMW/FOYa/Duz76Nt7vrLCO4dUzfI33HpfhfY9XAvjv9X9461eqf5ng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\nY5N5bPIqDh09nu9YAuX3TIBEByVuEYk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jzAEuzPMXku7/1sovyE9djgC2FpqaSkWSgDuA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\nrkJggg==\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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cl7LcEsLBax3h4HGAY2uy+iGhivKNaaz7F/HvdjN7kq57G6FJaDvhutH9hG/1AFioAn1b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\nAVKBu5RSShkgfwIjJwPjuluX2QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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1lYuIiKSIEreIiEiKKHGLiIikiBK3iIhIiihxi4iIpIgSt4iISIoocYuIiKSIEreIiEiK\nKHGLiIikiBK3iIhIiqTikqcikn5zGpuYNXcZa5pbGN1Qz4xpk5g+ZUy1wxJJHSVuESm7OY1NzJy9\nlJbtrQA0Nbcwc/ZSACVvkU5SU7mIlN2sucvaknZGy/ZWZs1dVqWIRNJLiVtEym5Nc0unykWkfWVN\n3Ga2ysyWmtkSM1uUKP+4mT1lZk+Y2bfKGYOIVN/ohvpOlYtI+ypR4z7V3Y9y96kAZnYqcA5wpLu/\nAbimAjGISBXNmDaJ+t51u5XV965jxrRJVYpIJL2q0TntI8DV7v46gLuvrUIMIlJBmQ5o6lUuUjxz\n9/Kt3GwlsBFw4Cfufq2ZLQF+D5wJbAU+5+4P53juZcBlAOPGjTtm9erVZYtTRESkOzGzxZmW6mzl\nrnG/yd2bzGwkMM/MnorbHAocDxwL/MbMJnjWLwh3vxa4FmDq1Knl+3UhIiKSImU9x+3uTfHvWuB2\n4DjgeWC2Bw8BO4Hh5YxDRESkpyhb4jaz/mY2MPM/8FbgcWAOcGosPxjoA6wrVxwiIiI9STmbykcB\nt5tZZjs3uftdZtYH+IWZPQ5sAy7JbiYXERGR3MqWuN39GeDIHOXbgIvLtV0REZGeTFdOExERSREl\nbhERkRRR4hYREUkRJW4REZEUUeIWERFJkWpcq1yAOY1Num6zSBfosyO1Tom7CuY0NjFz9lJatrcC\n0NTcwszZSwH0BSSShz47Imoqr4pZc5e1ffFktGxvZdbcZVWKSCQd9NkRUeKuijXNLZ0qF5FAnx0R\nJe6qGN1Q36lyEQn02RFR4q6KGdMmUd+7brey+t51zJg2qUoRiaSDPjsi6pxWFZlONOoZK9I5+uyI\ngKVhYq6pU6f6okWLqh2GiIhIRZjZYnefmmuZmspFRERSRIlbREQkRZS4RUREUkSJW0REJEWUuEVE\nRFJEiVtERCRFlLhFRERSRIlbREQkRZS4RUREUkSJW0REJEWUuEVERFJEiVtERCRFNDuYiLRrTmOT\nZuIS6WaUuEUkpzmNTcycvZSW7a0ANDW3MHP2UgAlb5EqUlO5iOQ0a+6ytqSd0bK9lVlzl1UpIhEB\nJW4Racea5pZOlYtIZShxi0hOoxvqO1UuIpWhxC0iOc2YNon63nW7ldX3rmPGtElVikhEQJ3TRKQd\nmQ5o6lVeeerNL/kocYtIu6ZPGaOEUWHqzS8dUVO5iEg3ot780hElbhGRbkS9+aUjZU3cZrbKzJaa\n2RIzW5S17LNm5mY2vJwxiIikiXrzS0cqUeM+1d2PcvepmQIz2w94K/BsBbbfZk5jEyddfQ8HfPH/\nOOnqe5jT2FTJzYuIdEi9+aUj1eqc9h3g88DvK7VBdfgQkTRQb37pSLkTtwN3m5kDP3H3a83sHKDJ\n3R81szJvfpd8HT70gRCR7kS9+SWfcifuN7l7k5mNBOaZ2VPAfxCayfMys8uAywDGjRtXdCDq8CEi\nIj1BWc9xu3tT/LsWuB04GTgAeNTMVgFjgUfMbJ8cz73W3ae6+9QRI0YUHYs6fIiISE9QtsRtZv3N\nbGDmf0It+2F3H+nu4919PPA8cLS7v1iuODLU4UNERHqCcjaVjwJuj+exewE3uftdZdxeXurwISIi\nPYG5e7Vj6NDUqVN90aJFHT9QRESkBzCzxclh1Em6cpqIiEiKKHGLiIikiBK3iIhIiihxi4iIpIgS\nt4iISIpU61rlUkJzGps0zE1EpEYocaecJk8REaktStwpp8lTRLoHtXxJpShxp5wmTxGpPrV8SSWp\nc1rKafIUkerL1/IlUmpK3J00p7GJk66+hwO++H+cdPU9zGlsqmo8mjxFpPrU8iWVpKbyTuiOzWGa\nPEWk+kY31NOUI0mr5UvKQYm7E7prR7DpU8YoUYtU0Yxpk3b7UQ9q+ZLyUeLuBDWHiUguavmSSlLi\n7gQ1h4lIe9TyJZWizmmdoI5gIiJSbapxd4Kaw0REpNqUuDtJzWEiIlJNHTaVm9koM/u5mf0x3j/M\nzD5Q/tBEREQkWyHnuK8H5gKj4/2ngU+VKyARERFpXyGJe7i7/wbYCeDuO4DW/E8RERGRcigkcb9q\nZsMABzCz44FNZY1KREREciqkc9pngDuAiWY2HxgBnFfWqERERCSnDhO3uz9iZicDkwADlrn79rJH\nJiIiInvoMHGb2blZRQeb2SZgqbuvLU9YIiIikkshTeUfAE4A/hLvnwIsBg4wsyvc/cYyxSYiIiJZ\nCkncvYBD3f0lCOO6gRuANwL3AUrcIiIiFVJIr/L9Mkk7WhvLNgA61y0iIlJBhdS47zWzO4Hb4v13\nx7L+QHPZIhMREZE9FJK4/52QrE+K928AfufuDpxarsBERERkT4UMB3Pgt/EmIiIiVVTIJCPHm9nD\nZrbFzLaZWauZvVKJ4ERERGR3hXRO+yFwIfAPoB74IPDf5QxKREREciskcePuy4E6d2919+uAM8sb\nloiIiORSSOe018ysD7DEzL4FvECBCV9ERERKq5AE/M/xcR8DXgX2A7IvgyoiIiIVUEjinu7uW939\nFXf/mrt/BnhHISs3s1VmttTMlpjZolg2y8yeMrPHzOx2M2soZgdERERqSSGJ+5IcZZd2YhunuvtR\n7j413p8HHO7uk4GngZmdWJeIiEhNa/cct5ldCFxEmEzkjsSigcCGrm7Q3e9O3H0Aze0tIiJSsHyd\n0xYQOqINB/4rUb4ZeKzA9Ttwt5k58BN3vzZr+fuBW3M90cwuAy4DGDduXIGb69itDz/L7Y1NnDhx\nOCdOHMaR+zXQu0597UREJB3aTdzuvhpYTZjSs6ve5O5NZjYSmGdmT7n7fQBm9iVgB/DrdrZ/LXAt\nwNSpU72IGHbTu24vNm/dwXf+9DTfngf9+tRx7PihnDhxGCcdOJxD9x1E3V5Wqs2JiIiUlIUrmuZ5\ngNm5wH8CIwGLN3f3QZ3akNnlwBZ3v8bMLgX+DTjd3V/r6LlTp071RYsWdWZzHdr46jYeXLmeBSvC\nbfnaLQAMru/N8ROGttXIDxw5ADMlchERqRwzW5zoG7b7sgIS93Lgne7+ZCc32h/Yy903x//nAVfE\nxd8GTnb3lwtZVzkSd7a1r2xl4TPrWbB8PQueWcdzG1oAGD5gb06cOCzehjNuWL+yxiEiIpIvcRdy\nAZaXOpu0o1HA7bG22gu4yd3vij8E9iY0nQM84O4f7sL6S2rkoL6cc9QYzjlqDADPbXiNhSvWs2DF\nOuavWM8dj64BYExDfVuz+gkThzFqUN9qhi0iIjWmkBr394B9gDnA65lyd59d3tB2qUSNOx93Z8XL\nW0Kz+vL1LHxmPZtatgMwcUT/tmb14ycMY0j/PlWLU0REeoZim8qvy1Hs7v7+UgRXiGon7mw7dzp/\nf+GVthr5Qys38Oq2Vszg0H0GhWb1A4dx7PihDOzbu9rhiohIyhSVuLuD7pa4s21v3cljz29i4Yp1\nLFixnkWrN7Jtx07q9jImjx3cdn78mP2H0Ld3XbXDFRGRbq7YGvfBwI+AUe5+uJlNBs529ytLH2pu\n3T1xZ9u6vZVHVm+MPdbX8ejzm2jd6fTptRfHjBvSViOfPFZjyEVEZE/FJu6/AjMIF1CZEssed/fD\nSx5pO9KWuLNteX0HD6/cwIJYI//7C6/gHsaQH3fA0LYaucaQi4gIFN+rvJ+7P5Q1lnlHSSKrEQP2\n7sWph4zk1ENGArvGkM9fHmrk31wWRsVpDLmIiHSkkMS9zswmEi5fipmdR7gUqnTRkP59OPPwfTnz\n8H0BeOmVrbuGni1fz9wnXgI0hlxERPZUSFP5BMKlR08ENgIrgYvdfVXZo4vS3lTeWc9teK2tWX3B\nivW8vDmMwhs7pL4tiWsMuYhIz1WSXuXJK6GVMrhC1FriTtIYchGR2lNs57RvAt9y9+Z4fwjwWXf/\ncskjbUctJ+5srTudJ3OMIQc4bF+NIRcR6QmKTdyNmd7kibJH3P3oEsaYlxJ3+8IY8uZwjfUV61n8\n7J5jyE+aOJyjNYZcRCQ1ik3cjwHHuvvr8X49sMjd31DySNuhxF04jSEXEUm/YhP3F4B3AplLn/4r\ncIe7f6ukUeahxN11GkMuIpI+RXdOM7MzgTPi3XnuPreE8XVIibt0kvOQz1++jhUvvwqEMeQnTAi1\n8RMnDmPiCI0hFxGpli4nbjOrA/7k7qeWK7hCKHGXT/YY8qbmMA/5iIG7jyHfb6jGkIuIVEqXr5zm\n7q1mttPMBrv7pvKEJ9U0alBfpk8Zw/Qpu+Yhn798HQufCbXy3y8J85BrDLmISPdQyDnu3wNTgHnA\nq5lyd/9EeUPbRTXu6sg3hvzAkQPaauRvPEBjyEVESqnYzmmX5Cp391+WILaCKHF3D5kx5JmObg+t\n3MBrcR7ytjHkE4dz7AFDGbB3IVfTFamuOY1NzJq7jDXNLYxuqGfGtEltrU8i1VSKzmn1wDh3X1bq\n4AqhxN095RtDfuTYwW1XddMYcumO5jQ2MXP2Ulq2t7aV1feu46pzj1Dylqortsb9TuAaoI+7H2Bm\nRwFXuPvZpQ81NyXudMiMIZ8fa+SPaQy5dGMnXX1PW2fMpDEN9cz/4mlViKg81KqQTsVO63k5cBxw\nL4C7L4kTj4jspm/vOk48cDgnHjgcgM1bt/PQyg2x1/p6/mve0/zXvD3HkB+27yD20hhyqbA1OZJ2\nvvI0ym5VaGpuYebspQBK3ilWSOLe7u6bssb07ixTPNKDDOzbm9MPHcXph44CYMOr23gw9lbPnodc\nY8il0kY31OescY9uqK9CNOUxa+6y3U4FALRsb2XW3GVK3ClWSOJ+wswuAurM7CDgE8CC8oYlPdHQ\n/n142xH78rYjcs9DftcTLwIaQy6VMWPapJznuGdMm1TFqEqrFloValEhifvjwJeA14GbgLnAleUM\nSmpDcgy5u/Pchpa2Huvzl2sMuZRXpsbZk8//1kKrQi3q6MppI4D9geWZaT2rQZ3Tao+7s3ztlrZm\n9YUr1vPK1h2A5iEXKZR6zqdXl3qVm9kHgW8CK4ADgMvc/Y6yRZmHEre07nT+vuYVFj6jMeQinaFe\n5enU1cT9OHCqu78ce5H/2t1PKGOc7VLilmwaQy4iPVlXE/cj7n50e/crSYlbOpKch3z+inUaQy4i\nqdbVxL0WuCVRdEHyvq5VLt3Z5q3beXjVhrYa+d9feAXQPOQikg5dvQDLjKz7i0sXkkh5Dezbm9MO\nGcVph2gMuYj0LAVdq7zaVOOWUtM85CLSnRU9yUi1KXFLuT234bW2MeQLVqzn5c2vA7vGkJ904HBO\nmDCMkRpDLiIVoMQt0gnZY8gfeGZDznnIj58wjIZ+GkMuIqWnxC1SBM1DLiKVVuy0nt8iXOK0BbgL\nmAx82t1/VepA26PELd3JHmPIV29kW+tOeu1lHLlfAydOHMYJE4dx9DiNIReRrik2cS9x96PM7F3A\nO4DPAPe5+5GlDzU3JW7pzvLNQz51/yExkQ9n8tjBGkMuIgUpdj7uzGPeDtyWY4rPfBteBWwGWoEd\n7j7VzIYCtwLjgVXAe919Y0ErFOmGcs1DnhxDfs3dTwNP079tDHmYLEXzkItIVxSSuO80s6cITeUf\niROPbO3ENk5193WJ+18E/uzuV5vZF+P9L3RifSLdWr4x5PNXrOMvy54EoKFfb44/QGPIRaRzCuqc\nFmvJm9y91cz6AYPc/cUCnrcKmJpM3Ga2DDjF3V8ws32Be9097wS4aiqXnuTFTVvDZCmxRp49hvyk\nWCPXGHKR2lV0r3IzOxw4DGgbxOruNxTwvJXARsCBn7j7tWbW7O4NcbkBGzP326PELT1Vch7y+SvW\ns3DFetZtCWPI9xtaz4kThnPigcM0hlykxhTbOe2rwCmExP0H4G3A39z9vAI2PMbdm8xsJDAP+Dhw\nRzJRm9lGdx+S47mXAZcBjBs37pjVq1d3tDmR1EuOIZ+/fB0PPLNrHnKNIRepHcUm7qXAkUCjux9p\nZqOAX7n7WzoZxOXAFuBDqKlcpCCZecgzY8gfXqUx5CK1oNhe5S3uvtPMdpjZIGAtsF8BG+0P7OXu\nm+P/bwWuAO4ALgGujn9/X+B+iNScur2MI8YO5oixg/m3kyeybUccQx6v6vbLBav56f0rNYZcpIYU\nUuP+H+A/CNN6fpZQa17i7v/awfMmALfHu72Am9z9G2Y2DPgNMA5YTRgOtiHfulTjlp5uTmMTs+Yu\nY01zC6Mb6pkxbRLTp4zp8Ap6LqoAAB2rSURBVHlbt7eyePXGthq5xpCL9Awlu+SpmY0n9Ch/rDSh\nFUaJW3qyOY1NzJy9lJbtrW1l9b3ruOrcIwpK3kmZMeTzY4/1J+M85BpDLpIuula5SDd20tX3tA0J\nSxrTUM/8L55W1Lo3vLqNB55Z31Yjf+blVwGNIRfp7oo9xy0iZbQmR9LOV94ZQ/v34awj9uWsI/YF\n9hxDftcT4XIMmodcJD2UuEWqbHRDfc4a9+iG+pJva5/BfXnXlLG8a8rY3caQh+Fn6/n9kjWAxpCL\ndGcdJu541bRsm919exniEak5M6ZNynmOe8a0vKMki2ZmjBvWj3HDxnHBceP2GEP+x8df4NZFzwEa\nQy7SnRTSq3wVYfjXRsCABuBF4CXgQ+6+uMwx6hy39Hhd7VVeTgWNIT9wOMeO1xhykVIr9gIsPwV+\n6+5z4/23Au8GrgO+5+5vLHG8e1DiFqm+7DHkj6xu1jzkImVS9JXT3P2IrLLH3H1yZq7uEsaakxK3\nSPdT6BjyI8cOppfGkIt0SrG9yl8wsy8At8T75wMvmVkdsLNEMYpIyvTtXcdJBw7npA7mIR+wd684\nhjzUyA/dR2PIRYpRSOK+CPgqMCfenx/L6oD3likuEUmZjuYhv+eptUAYQ37ChGFtNfKJI/prDLlI\nJ+gCLCJSES9u2sqCFetYuGL3echHto0h1zzkIhnFnuM+GPgcMJ5EDd3di7ukUycocYv0LNljyBe0\nNw/5xGGMHKgx5FJ7ik3cjwI/BhYDbQNNKzEMLEOJW6Rny4whn788JPLkPOQHxTHkJ0wczvEThmoM\nudSEYhP3Ync/piyRFUiJW6S2ZI8hf2jlBlq2hzHkbxg9qK1Z/bjxQ+mvMeTSAxWbuC8nzMF9O/B6\npryjqThLSYlbpLZlxpCHWc/W0fisxpBLz1Zs4l6Zo9jdfUIpgiuEEreIJLVsa2XR6g1tHd0ee76Z\nnY7GkEuPoWk9RaRHe2Xrdh5euaGto1tmHnKNIZe06tIFWMzsNHe/x8zOzbXc3WeXKkARkWIM6tub\n0w8dxemHhjHk67e8zoMrNzB/eRh+pjHk0pPk69VxMnAP8M4cyxxQ4haRbmnYgL13m4f8hU0tbc3q\nC1es54+Ph3nINYZc0qiQc9wHuPvKjsrKSU3lIlIq7s6zG15ra1ZfuGId67ZsAzSGXLqPYjunPeLu\nR+dYYcWGiClxi0i5uDv/WLuFBRpDLt1IV89xHwK8ARicdZ57EKCfoSLSI5gZB48ayMGjBnLpSQfs\nMYb8N4ue55cLV2sMuXQb7da4zewcYDpwNnBHYtFm4BZ3X1D+8ALVuEWkWpLzkM9frjHkUhnFNpWf\n4O4LyxJZgZS4RaS7aNmWPQ957jHkk8cOprfGkEsXFZu4+wIfIDSbtzWRu/v7SxlkPkrcItJdJceQ\nz1++jqde3AxoDLkUp0vnuBNuBJ4CpgFXAO8DnixdeCIi6ZU9hnzDq9t44JlwadYFyzWGXEqvkBp3\no7tPMbPH3H2ymfUG7nf34ysTomrcIpJeyTHkC5avY82mrUBiDPmBwzlx4jDGDtEYctml2Br39vi3\n2cwOB14ERpYqOBGRnmzfwfWce/RYzj167B5jyP+2fB1zlqwBYNzQfm3N6hpDLvkUkrivNbMhwJcJ\nvcsHAF8pa1QiIj2QmbH/sP7sP6w/Fx43rm0MeWYe8v9b+gK3PPwcoDHk0j5NMiIi0k207nSeWLOp\nraPbolUb2+YhP3z04LYa+bEaQ97jdblXuZnVAUPcfV283we4BPiMux9ajmBzUeIWkVq0bcdOHn2+\nua1G3vjsRra3Or32Mo5qG0M+nCnjGjSGvIfpUuI2swuAnwCvAv8AvgH8AngY+Lq7P1KecPekxC3d\n0ZzGJmbNXcaa5hZGN9QzY9okpk8ZU+2wpAfLzEOeOUe+NI4h37vXXkwdP6Ttqm6Tx2ge8rTrauJ+\nHJju7svN7GhgIXCeu/9v+ULNTYlbups5jU3MnL2Ulu2tbWX1veu46twjlLylYl7Zup2Hnskkco0h\n70m62qt8m7svB3D3R8zsH9VI2iLd0ay5y3ZL2gAt21uZNXeZErdUzKC+vTnjsFGccdiuecgfeGYD\nC1bsPg/5kH69Y2/1MPRswnCNIU+zfIl7pJl9JnG/IXnf3b9dvrBEurc1zS2dKhephGED9ubtk/fl\n7ZP3nId8wfJ1/GFpmId81KC925rVNYY8ffIl7p8CA/PcF6lZoxvqacqRpEc31FchGpHcsseQr17/\nWluz+n1Pv8ztjU2AxpCnjYaDiXSBznFL2rk7T7+0pW2ylAeeWc9mzUPebRQ1yUgJNl4HLAKa3P0d\nZnY6MAvYC9gCXJo5l94eJW7pjtSrXHqS5BjyBSvW8/DKDW1jyDUPeeVVO3F/BpgKDIqJ+2ngHHd/\n0sw+Chzn7pfmW4cSt4hIZWXGkC9YHprWk/OQawx5+RV7rfJiNjwWeDthDHimY5sDg+L/g4E15YxB\nOke1SBGBML/4seOHcuz4oXzyjIPa5iGfH5vWf/iX5Xz/nuUaQ14F+cZxfybngqiQXuVm9lvgKkKn\nts/FGvebgTlAC/AKcLy7v5LjuZcBlwGMGzfumNWrV3e0OSmSztuKSKEKGUN+4sThHLLPQI0h74Ku\n1riL6kFuZu8A1rr7YjM7JbHo08BZ7v6gmc0Avg18MPv57n4tcC2EpvJiYpHCaGyyiBRKY8irp93E\n7e5fK3LdJwFnm9lZQF9gkJn9H3CIuz8YH3MrcFeR25ES0dhkEemqzo4hz8xFPkZDKDutw3PcZtYX\n+ADwBkICBsDd35/vee4+E5gZ13EK8DlgOvCimR3s7k8DbwGe7GrwUloamywipZJvHvL7/7FrDPn+\nw/q1dXQ7YcIwRgzcu8qRd3+FdE67EXgKmAZcAbyPLiZbd99hZh8CfmdmO4GNQN4fAFI5M6ZNynmO\ne8a0SVWMSkTSrqN5yO987AVufijMQ37wqAFtHd2OP2AYg/v1rnL03U+Hw8HMrNHdp5jZY+4+2cx6\nA/e7+/GVCVHDwSpJvcpFpNLyjSHPzEN+4oHDOXb8EPr1qY0x5EWN4zazh9z9ODO7D/go8CLwkLtP\nKH2ouSlxi4jUjnzzkE8Z19DW0W3KuAb27tUzx5AXm7g/CPwOmAxcBwwA/p+7/6TUgbZHiVtEpHbl\nm4f82PFD2yZLOaIHjSEvNnHXuXtr3geVmRK3iIhk5BtD/sYDMok83WPIi71y2kozu4swdOseT8Os\nJCIi0mNljyFft+V1Hngm1MYXrljPn3v4GPJCatz9gHcAFwDHAP8L3OLufyt/eIFq3CIiUqg1zYkx\n5CvW8cKmrUC65iEv2SQjZjYE+B7wPnevWI8AJW4REemK7HnIF65Yz/pXtwHdewx50ZOMmNnJwPnA\nmYQpOt9buvBERETKw8wYP7w/44f356I3jmubh7xtDPmj6RtDXkhT+SqgEfgNcIe7v1qBuHajGreI\niJTDjtadPLHmlbYa+cOrNrB1+87dxpCfMHEYx1Z4HvJie5UPyjV7VyUpcYuISCVs27GTJc81s2DF\nnmPIKzkPebGJeyzwA8KkIQD3A5909+dLGmUeStwiIlIN+caQZ+YhL8cY8mIT9zzgJsI1ywEuJnRO\ne0vJIuyAEreIiHQH7Y0hP3jUAO7+9Mkl206xndNGuPt1ifvXm9mnShOaiIhIerQ3D3lycqZyKyRx\nrzezi4Gb4/0LgfXlC0lERCQdMvOQV1IhDfLvJwz/ehF4ATgP+NdyBiUiIiK55a1xm1kdcK67n12h\neERERCSPvDXuOLnIhRWKRURERDpQyDnu+Wb2Q8IkI20XX3H3R8oWlYh02ZzGJmbNXcaa5hZGN9Qz\nY9okpk8ZU+2wRKRECkncR8W/VyTKHDit9OGISDHmNDYxc/bSth6uTc0tzJy9FEDJW6SH6DBxu/up\nlQhERIo3a+6yPYaltGxvZdbcZUrcIj1Eh73KzWyYmX3fzB4xs8Vm9j0zG1aJ4ESkc9Y0t3SqXETS\np5DhYLcALwPvJgwFe5lwvltEupnRDfWdKheR9Ckkce/r7l9395XxdiUwqtyBiUjnzZg2ifqsiQ/q\ne9cxY9qkKkUkIqVWSOK+28wuMLO94u29wNxyByYinTd9yhiuOvcIxjTUY8CYhnquOvcInd8W6UEK\nmWRkM9Af2BmL9mLXsDB390HlCy/QJCMiIlJLippkxN0Hlj4kERER6YpCxnFjZucCbyKM377f3eeU\nNSoRERHJqZDhYP8DfBhYCjwOfNjM/rvcgYmIiMieCqlxnwYc6vFkuJn9EniirFGJiIhIToX0Kl8O\njEvc3y+WiYiISIUVUuMeCDxpZg8RznEfBywyszsANOWniIhI5RSSuL9S9ihERESkIIUMB/tr8r6Z\nvQm40N3/vWxRiYiISE6FDgebAlwEvAdYCfyunEGJiIhIbu0mbjM7GLgw3tYRJhYxTfMpIiJSPflq\n3E8B9wPvcPflAGb26YpEJSIiIjnlGw52LvAC8Bcz+6mZnQ5YZzdgZnVm1mhmd8b7ZmbfMLOnzexJ\nM/tE10IXERGpPe3WuONlTeeYWX/gHOBTwEgz+xFwu7vfXeA2Pgk8CWQmI7mUMBb8EHffaWYjuxq8\niIhUxpzGJmbNXcaa5hZGN9QzY9okzTpXJR1egMXdX3X3m9z9ncBYoBH4QiErN7OxwNuBnyWKPwJc\n4e474/rXdjpqERGpmDmNTcycvZSm5hYcaGpuYebspcxpbKp2aDWpkCuntXH3je5+rbufXuBTvgt8\nnl1TggJMBM43s0Vm9kczO6gzMYiISGXNmruMlu2tu5W1bG9l1txlVYqotnUqcXeGmb0DWOvui7MW\n7Q1sjfOM/hT4RTvPvywm90Uvv/xyucIUEZEOrGlu6VS5lFfZEjdwEnC2ma0CbgFOM7NfAc8Ds+Nj\nbgcm53pyrNlPdfepI0aMKGOYIiKSz+iG+k6VS3mVLXG7+0x3H+vu44ELgHvc/WJgDpAZC34y8HS5\nYhARkeLNmDaJ+t51u5XV965jxrRJVYqothV05bQSuxr4dRwTvgX4YBVikJRRj1aR6sl81vQZ7B4s\nTrPdrU2dOtUXLVpU7TCkSjI9WpOdY+p713HVuUfoi0NEeiQzWxz7gu2hnOe4RUpCPVpFRHZR4pZu\nTz1aRUR2UeKWbk89WkVEdlHilm5PPVpFRHapRq9ykU5Rj1YRkV2UuCUVpk8Zo0QtIt1ONYaqKnGL\niIh0QfZQ1czkK0BZk7cSt4iIVFRPuaBSvqGqStwiItIjVKuWWg7VGqqqXuUiIlIxPemCStUaqqrE\nLSIiFdOTLqhUraGqStwiIlIxPemCStOnjOGqc49gTEM9BoxpqK/IHAo6xy0iIhUzY9qknJMGpfWC\nStUYqqrELSIiFaMLKhVPiVtERCpKF1Qqjs5xi4iIpIgSt4iISIoocYuIiKSIEreIiEiKKHGLiIik\niBK3iIhIiihxi4iIpIgSt4iISIoocYuIiKSIEreIiEiKKHGLiIikiBK3iIhIiihxi4iIpIgSt4iI\nSIoocYuIiKSIEreIiEiKKHGLiIikSK9qByBSSXMam5g1dxlrmlsY3VDPjGmTmD5lTLXDEhEpmBK3\n1Iw5jU3MnL2Ulu2tADQ1tzBz9lIAJW8RSQ01lUvNmDV3WVvSzmjZ3sqsucuqFJGISOcpcUvNWNPc\n0qlyEZHuqOyJ28zqzKzRzO7MKv++mW0p9/ZFMkY31HeqXESkO6pEjfuTwJPJAjObCgypwLZF2syY\nNon63nW7ldX3rmPGtElVikhEpPPK2jnNzMYCbwe+AXwmltUBs4CLgHeVc/siSZkOaOpVLiK5pGXU\nSbl7lX8X+DwwMFH2MeAOd3/BzNp9opldBlwGMG7cuHLGKDVk+pQx3fKDKCLVlaZRJ2VrKjezdwBr\n3X1xomw08B7gBx09392vdfep7j51xIgR5QpTREQkVaNOylnjPgk428zOAvoCg4AngNeB5bG23c/M\nlrv7gWWMQ0REJK80jTopW43b3We6+1h3Hw9cANzj7kPcfR93Hx/LX1PSFhGRakvTqBON4xYRkZqX\nplEnFbnkqbvfC9ybo3xAJbYvIiKST5pGneha5SIiIqRn1ImaykVERFJEiVtERCRFlLhFRERSRIlb\nREQkRZS4RUREUkSJW0REJEWUuEVERFJEiVtERCRFdAEWERFJrbTMoV1KStwiIpJKaZpDu5TUVC4i\nIqmUpjm0S0mJW0REUilNc2iXkhK3iIikUprm0C4lJW4REUmlNM2hXUrqnCYiIqmUpjm0S0mJW8qm\nFodpiEhlpWUO7VJS4payqNVhGiIi5aZz3FIWtTpMQ0Sk3JS4pSxqdZiGiEi5KXFLWdTqMA0RkXJT\n4payqNVhGiIi5abOaVIWtTpMQ0Sk3JS4pWxqcZiGiEi5qalcREQkRZS4RUREUkSJW0REJEWUuEVE\nRFJEiVtERCRFlLhFRERSRIlbREQkRZS4RUREUkSJW0REJEWUuEVERFLE3L3aMXTIzF4GVpdgVcOB\ndSVYT9rU6n6D9l37Xltqdb+h5+37/u4+IteCVCTuUjGzRe4+tdpxVFqt7jdo37XvtaVW9xtqa9/V\nVC4iIpIiStwiIiIpUmuJ+9pqB1AltbrfoH2vVbW677W631BD+15T57hFRETSrtZq3CIiIqlWE4nb\nzM40s2VmttzMvljteErNzH5hZmvN7PFE2VAzm2dm/4h/h8RyM7Pvx9fiMTM7unqRF8fM9jOzv5jZ\n383sCTP7ZCyvhX3va2YPmdmjcd+/FssPMLMH4z7eamZ9Yvne8f7yuHx8NeMvBTOrM7NGM7sz3q+J\nfTezVWa21MyWmNmiWFYLx3yDmf3WzJ4ysyfN7IRa2O9cenziNrM64L+BtwGHARea2WHVjarkrgfO\nzCr7IvBndz8I+HO8D+F1OCjeLgN+VKEYy2EH8Fl3Pww4Hvj3+N7Wwr6/Dpzm7kcCRwFnmtnxwH8C\n33H3A4GNwAfi4z8AbIzl34mPS7tPAk8m7tfSvp/q7kclhj/VwjH/PeAudz8EOJLw3tfCfu/J3Xv0\nDTgBmJu4PxOYWe24yrCf44HHE/eXAfvG//cFlsX/fwJcmOtxab8BvwfeUmv7DvQDHgHeSLgARa9Y\n3nbsA3OBE+L/veLjrNqxF7HPYwlf1KcBdwJWQ/u+ChieVdajj3lgMLAy+33r6fvd3q3H17iBMcBz\nifvPx7KebpS7vxD/fxEYFf/vka9HbP6cAjxIjex7bCpeAqwF5gErgGZ33xEfkty/tn2PyzcBwyob\ncUl9F/g8sDPeH0bt7LsDd5vZYjO7LJb19GP+AOBl4Lp4euRnZtafnr/fOdVC4q55Hn5y9tjhA2Y2\nAPgd8Cl3fyW5rCfvu7u3uvtRhNrnccAhVQ6pIszsHcBad19c7Viq5E3ufjShOfjfzeyfkgt76DHf\nCzga+JG7TwFeZVezONBj9zunWkjcTcB+iftjY1lP95KZ7QsQ/66N5T3q9TCz3oSk/Wt3nx2La2Lf\nM9y9GfgLoXm4wcx6xUXJ/Wvb97h8MLC+wqGWyknA2Wa2CriF0Fz+PWpj33H3pvh3LXA74UdbTz/m\nnweed/cH4/3fEhJ5T9/vnGohcT8MHBR7nPYBLgDuqHJMlXAHcEn8/xLC+d9M+b/EXpfHA5sSTU2p\nYmYG/Bx40t2/nVhUC/s+wswa4v/1hHP7TxIS+HnxYdn7nnlNzgPuiTWU1HH3me4+1t3HEz7P97j7\n+6iBfTez/mY2MPM/8FbgcXr4Me/uLwLPmdmkWHQ68Hd6+H63q9on2StxA84CniacA/xSteMpw/7d\nDLwAbCf8Mv0A4Rzen4F/AH8ChsbHGqGX/QpgKTC12vEXsd9vIjSNPQYsibezamTfJwONcd8fB74S\nyycADwHLgduAvWN533h/eVw+odr7UKLX4RTgzlrZ97iPj8bbE5nvsxo55o8CFsVjfg4wpBb2O9dN\nV04TERFJkVpoKhcREekxlLhFRERSRIlbREQkRZS4RUREUkSJW0REJEWUuKXmmFlrnFnpiTi71mfN\nrGyfBTN7f5zN6TEze9zMzonlV5jZGWXc7vFxNqwlcTaly2P5KWZ2YhfWd5SZndWF590c9/3TWeWX\nm1lTjO9xMzu7s+tuZ3sL2im/3szOy7VMJE16dfwQkR6nxcOlQjGzkcBNwCDgq6XekJmNBb4EHO3u\nm+LlWUcAuPtXSr29LL8E3uvuj8ZZ8jIXrzgF2ALkTHC5xCuOHQVMBf7QieftAxzrYWauXL7j7teY\n2aHA/WY20t13Jp7fy3ddf7wg7t7pHyUiaaIat9Q0D5eNvAz4WLzK0ngzu9/MHom3EwHM7AYzm555\nnpn92szOMbM3WJgXe0msVR6UtYmRwGZCosTdt7j7yriOthqghTmWvxa3udTMDonlA8zsukSN/d2x\n/K1mtjA+/rb4gyDbSMKFefBwXfO/W5iM5cPAp2PMbzazd8aaeaOZ/cnMRsVtXG5mN5rZfOBG4Arg\n/Pi885MbsjA/eCbORjM7NS66GxiT2Vae9+FJwjStw+Pr8mMzexD4Vrxa2C/i69yYaLHI+dqb2Zb4\n18zsh2a2zMz+FF+PTLzHmNlfLUzUMdd2XTbzXjP7z7jepzMxW5jQ5ZrYMvCYmX3czE4zszmJdb7F\nzG5vbx9FSqbaV4DRTbdK34AtOcqaCTML9QP6xrKDgEXx/5OBOfH/zBSDvYAfAO+L5X2A+qz11hGm\nlXwWuA54Z2LZ9cB58f9VwMfj/x8Ffhb//0/gu4nnDAGGA/cB/WPZF4hXTsva9lcI81LfDvxbYr8u\nBz6Xtc7MxZg+CPxX4nGLM/sEXAr8sJ3X9LPAL+L/h8T97UvWdLNZz2mLgzAl6RrCFa+uJ0zVWReX\nfRO4OP7fQLgKYv/2XvvM+wucS5g1rQ4YHd/j84DehNaGEfFx5ydivzex/2cBf4r/f4RwfezMtKFD\nY6xPJdZzU/L91U23ct3UVC6yu97AD83sKKAVOBjA3f9qZv9jZiOAdwO/c/cdZrYQ+FJsEp/t7v9I\nrszdW83sTOBYwvWVv2Nmx7j75Tm2nZkkZTEh6QCcQbged2Z9Gy3MjnUYMN/MICSthdkrc/crzOzX\nhOtZXwRcSGgmzzYWuDXWOvsQfpRk3OHuLTmek+1NhESKuz9lZqsJr90reZ8Vav4XE1olznd3j/t0\nm7u3xse8lTCpyOfi/b7AOMI+t/vaA/8E3BzXs8bM7onlk4DDgXlxW3XEloko+T6Mj/+fAfzYY7O9\nu28AMLMbgYvN7DrCJC//0sH+ihRNiVtqnplNICTptYTz3C8BRxJOJW1NPPQG4GJCIv1XAHe/KTbp\nvh34g5n9m7vfk3gO7u6Ea2Q/ZGbzCDXvy3OE8nr820r+z6YB89z9wo72zd1XAD8ys58CL5tZrnmo\nfwB8293vMLNTsmJ7taNtFOk77n5NjvLkdg14t7svy3rMkx299u0w4Al3P6Gd5YW+DxDey/8lHCe3\neSfPx4t0hc5xS02LNegfE5qAndAM/oKHDlL/TKiNZVwPfArA3f8enz8BeMbdv0+YmWhy1vpHm9nR\niaKjgNWdCHEe8O+J9Q0BHgBOMrMDY1l/Mzs4x7693WKVktDs30poLt4MDEw8dDC7pjy8hPZlPy/p\nfuB9cbsHE2rE2Ym2q+YCH8/si5lNiX/zvvaE0wnnx/PT+wKZ8+7LgBFmdkJcT28ze0MHMcwD/s3i\ntKFmNhTA3dcQmvi/TEjiImWnxC21qD52aHqCMKPQ3cDX4rL/AS4xs0cJ52rban7u/hJh6szkF/R7\ngcfNbAmh+fWGrG31Bq4xs6fiY84HPtmJWK8EhsROUY8Cp7r7y4TzzTeb2WOEJuNDcjz3n4Flcbs3\nEs4HtxJqiO9KdBi7HLjNzBYD6/LE8hfgsFyd0wiv215mthS4FbjU3V/fYw1d83XC6/hYfM++Hss7\neu1vJ8wa9fe4bCGAu28jnOv+z/iaLgE66on+M8J5+8ficy5KLPs18JyHDnYiZafZwUQKZGb9CFME\nHu3um6odj3QPZvZDoNHdf17tWKQ2qMYtUgALF0p5EviBkrZkxFaKycCvqh2L1A7VuEVERFJENW4R\nEZEUUeIWERFJESVuERGRFFHiFhERSRElbhERkRRR4hYREUmR/w/H8fywVqF93AAAAABJRU5ErkJg\ngg==\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3DaChPvrTcG/OtKGrc2vwxPpNPdSMN3RNr6nSVA2pqbqUeA+YNG67RFxKzJPG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\n/w94Iqahly1yVQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/nUbMSWYx5tYPeOvY6cDr/v9n+rEl4a2Dr+L9Zi4EevkHFoUmvfevZGDeQA9rnXMPZDHd\n4TVDLSvUwCKYmX2K1ynk4XDHEshvAUnB+z5XhjueUDHvUr1XnXN1cnxxaJb/EHCCcy7jDrCI5EJJ\nPeKWQuA3XTfwm/fOxds7HR3uuADM7AIzK+ufPxsOzMfrGV1smFmcmfXwm5oT8FqPvgpTLJXwenO/\nHo7lixQnStwSSsfhXT62G69X+K3OucSwRvSXC/Ga2NcBjfAuvypuzU+GNwDMdrym8sX81Ueg8IIw\nuxHvFMx3zrmpOb1eRLKnpnIREZEIoiNuERGRCKLELSIiEkGK9D2A01WpUsXVrVs33GGIiIgUitmz\nZ29xzlXNbFpEJO66desya9ascIchIiJSKMwsy2GG1VQuIiISQZS4RUREIogSt4iISARR4hYREYkg\nStwiIiIRRIlbREQkgihxi4iIRBAlbhERkQiixC0iIhJBlLhFREQiSEQMeSqFZ3RiMsPGJ7EuJZWa\n8XEM6t6Y3m0Swh2WiIj4lLjliNGJyQweNZ/Ug2kAJKekMnjUfAAlbxGRIkJN5XLEsPFJR5J2utSD\naQwbnxSmiEREJCMlbjliXUpqrspFRKTwqam8GCio89I14+NIziRJ14yPK4gwRUSkAOiIO8Kln5dO\nTknF8dd56dGJybmua1D3xsTFRh9VFhcbzaDujQsoWhERyS8l7ghXkOele7dJYGifliTEx2FAQnwc\nQ/u0VMc0EZEiRE3lEa6gz0v3bpOgRC0iUoTpiDvCZXX+WeelRUSKJyXuCKfz0iIiJYuayiNcerO2\nRjsTESkZlLiLAZ2XFhEpOdRULiIiEkGUuEVERCKIEreIiEgEUeIWERGJIErcIiIiEUSJW0REJIIo\ncYuIiEQQJW4REZEIosQtIiISQZS4RUREIogSt4iISARR4hYREYkgStwiIiIRRIlbREQkgihxi4iI\nRBAlbhERkQiixC0iIhJBlLhFREQiiBK3iIhIBAlp4jazeDP7wsyWmNliM2tvZkPMLNnM5viPHqGM\nQUREpDiJCXH9zwHfO+cuNrNSQFmgOzDSOTc8xMsWEREpdkKWuM2sInAmcA2Ac+4AcMDMQrVIERGR\nYi+UTeX1gM3A22aWaGZvmtkx/rQ7zGyemb1lZseGMAYREZFiJZSJOwY4CXjFOdcG2APcD7wCNABa\nA+uBEZnNbGY3mdksM5u1efPmEIYpIiISOUKZuNcCa51zv/nPvwBOcs5tdM6lOecOA28Ap2Q2s3Pu\ndedcW+dc26pVq4YwTBERkcgRssTtnNsArDGzxn5RV2CRmdUIeNlFwIJQxSAiIlLchLpX+Z3Ah36P\n8hXAtcDzZtYacMAq4OYQxyAiIlJshDRxO+fmAG0zFP8zlMsUEREpzjRymoiISARR4hYREYkgStwi\nIiIRRIlbREQkgihxi4iIRBAlbhERkQiixC0iIhJBlLhFREQiiBK3iIhIBFHiFhERiSBK3CIiIhFE\niVtERCSCKHGLiIhEkBKXuKcv28J9X8xj254D4Q5FREQk10pc4k7asIsvf1/LWcMn8+Fvq0k77MId\nkoiISNBKXOK+rmM9vr37DJrWKM//fbWAi16eztw1KeEOS0REJCglLnEDnFC9PB/feBrPXdaa9Tv2\n0fvl6fz7q/mk7FXzuYiIFG0lMnEDmBkXtk5g0sBOXHt6PT6duYazhk/m05l/cljN5yIiUkSV2MSd\nrnyZWB66oBlf39mRhtXKcd+X8+n76gwWJO8Id2giIiJ/U+ITd7qmNSrw2c3tGXFJK9Zs20uvF6fx\n8JgF7Eg9GO7QREREjlDiDmBm9D25FhMHduafp9Xh/V9X03XEZL6cvRbn1HwuIiLhp8SdiYpxsTxy\nYQvG3tGR2pXKMvDzufR77RcWr98Z7tBERKSEU+LORouEinx5y+k83fdElm/eQ88XpvHouEXs2qfm\ncxERCQ8l7hxERRn92tVm0sBOXNauNm/PWEmXEVMYMydZzeciIlLolLiDFF+2FE9c1JLRt3WgRsUy\n3P3JHC5/41eWbtwV7tBERKQEUeLOpVa14/nqtg48cVELFq/fxXnP/cx/vl3M7v2Hwh2aiIiUAErc\neRAdZVx5ah1+urczfU+qxetTV9BtxBS+nrdOzeciIhJSStz5UOmYUjx18YmMuu10KpcrxR0fJfLP\n//6P5Zt3hzs0EREpppS4C8BJxx/L2Ds68uiFzZm7NoVzn53K098vYe8BNZ+LiEjBUuIuINFRxlXt\n6zJpYGcuaFWTlycv5+xnpvL9gg1qPhcRkQKjxF3AqpYvzTP9WvPZze0pXyaGWz6YzTVvz2Tllj3h\nDk1ERIoBJe4QOaVeJb6+syMP9mzG7NXb6T5yKs/8kMS+g2nhDk1ERCKYEncIxURHcX3Hekwa2Inz\nWh7H85OW0e2ZKUxYtDHcoYmISIRS4i4E1SqU4bnL2vDxjacRFxvNDe/N4oZ3Z7Jm295whyYiIhFG\nibsQtW9QmW/vPoN/92jCjOVb6fbMFJ6bsFTN5yIiErQcE7eZVTez/5rZd/7zZmZ2fehDK55io6O4\n6cwGTBzYiW7NqjNywh90f3YqPyVtCndoIiISAYI54n4HGA/U9J//AfQPVUAlRY2Kcbx0xUm8f/0p\nRJtx7dszufn9WSSnpIY7NBERKcKCSdxVnHOfAYcBnHOHALXtFpAzGlXlu/5nMKh7Y6b8sZmuIybz\n0k/LOHDocLhDExGRIiiYxL3HzCoDDsDMTgN2hDSqEqZ0TDS3n9WQiQM70+mEqgwbn8S5z01l2tIt\n4Q5NRESKmGAS9wBgLNDAzKYD7wF3hjSqEiohPo7X/tmWt69tR9phxz/++xu3f/g763eo+VxERDwW\nzHCcZhYDNAYMSHLOHQx1YIHatm3rZs2aVZiLDLt9B9N4feoKXvppGdFRxt1dG3Fdx3rERutCABGR\n4s7MZjvn2mY6LafEbWZ9MineAcx3zhVKV+iSmLjTrdm2l0fGLWTC4k00qlaORy9sQfsGlcMdloiI\nhFB2iTuYw7frgTeBK/3HG8B9wHQz+2eBRSmZql2pLG9e3Y43r2pL6sE0Ln/jV+7+JJFNO/eFOzQR\nEQmDmCBf09Q5txG867rxznOfCkwF3g9deJKuW7PqdGxUhZd/WsarU1YwcfEm7jn7BK5uX4cYNZ+L\niJQYwSTu2ulJ27fJL9tmZtme6zazeLyj9RZ4vdKvA5KAT4G6wCqgn3Nue+5DL3nKxEYz4JzG9Dmp\nFg+PXchjXy/i81lreKx3C9rVrRTu8CLC6MRkho1PYl1KKjXj4xjUvTG92ySEvS4RkWAFc6g22cy+\nNrOrzexqYIxfdgyQksO8zwHfO+eaAK2AxcD9wETnXCNgov+8xBmdmEyHJydR7/5v6PDkJEYnJgc9\nb90qx/DOte149R8ns2vfIS559RcGfDaHzbv2hzDiyDc6MZnBo+aTnJKKA5JTUhk8an6uPvtQ1CUi\nkhvBdE4zoC/QwS+aDnzpcpjRzCoCc4D6ga81sySgs3NuvZnVACY75xpnV1dx65yWvtFPDRijPC42\nmqF9Wub6iG3vgUO8OGkZb/y8gjKx0dx7TmP+cVodoqOsoMOOeB2enJTpyHQJ8XFMv79L2OoSEcko\nX53TnOcL59w9/uOLnJK2rx6wGXjbzBLN7E3/KL26c269/5oNQPVg30hxMWx80lFJGyD1YBrDxifl\nuq6ypWL417lN+L7/mbSqFc/DYxfS68VpzF6tsw8ZrctiONmsygurLhGR3AjmJiOnmdlMM9ttZgfM\nLM3MdgZRdwxwEvCKc64NsIcMzeL+DkCmOwFmdpOZzTKzWZs3bw5icZEjFBv9BlXL8f71p/DSFSex\ndfcB+r4yg399MZetu9V8nq5mfFyuygurLhGR3AjmHPeLwOXAUiAOuAF4KYj51gJrnXO/+c+/wEvk\nG/0mcvy/mV4L7px73TnX1jnXtmrVqkEsLnKEaqNvZpx/Yg0mDuzEzWfWZ9TvyXQZMYUPf1tN2uFg\nGkmKt0HdGxMXG31UWVxsNIO6Z3umJuR1iYjkRlDXETnnlgHRzrk059zbwLlBzLMBWGNm6VuyrsAi\nvOFTr/bL0ju7lSih3ugfUzqGwT2a8u3dZ9C0Rnn+76sFXPTydOauyakvYfHWu00CQ/u0JCE+DsM7\nH52XfgUFXZeISG4E0zltKtAN77KuDcB64BrnXKscKzdr7c9XClgBXIu3s/AZcDywGu9ysG3Z1VPc\nOqdB4V1K5Jxj7Nx1PPHNYjbv3s/lpxzPv7o3Jr5sqQJfloiIFIz8DnlaB9iIl3zvASoCLznnlhd0\noFkpjom7sO3ad5BnJyzlnRmrqFAmhvvPa8IlJ9cmSr3PRUSKnPwOedrbObfPObfTOfeIc24A0LNg\nQ5RQK18mlgd7NuPrOzvSsFo57vtyPn1fncGCZN2hVUQkkgSTuK/OpOyaAo5DCknTGhX47Ob2jLik\nFWu27aXXi9N4aMwCdqQW6g3fREQkj7Ic8tTMLgeuAOqZ2diASeWBbM9JS9FmZvQ9uRbdmlXnmR+S\neP/X1Xwzbz2DezSl70kJeGPu5J+GBBURKXhZnuP2z23XA4Zy9PXXu4B5zrlDoQ/Po3PcobUgeQcP\njllA4p8ptKt7LI9e2IKmNSrkq86CHB1ORKSkyVfntKJAiTv0Dh92fDF7LU9+v4QdqQe5qn0d7jn7\nBCqUic1TfRoSVEQk7/LVOc3M+pjZUjPbYWY7zWxXkCOnSQSJijL6tavNpIGduKxdbd6ZsYquI6Yw\nOjGZvOzcaUhQEZHQCKZz2tNAL+dcRedcBedceedc/tpRpciKL1uKJy5qyZjbO1CzYhn6fzqHy17/\nlT827spVPRoSVEQkNIJJ3Budc4tDHokUKSfWimfUbR34z0UtWbJhFz2e+5n/fLuY3fuD69qgIUFF\nREIjmAFYngOOA0YDR+5Y4ZwbFdrQ/qJz3OG1bc8Bnv5+CZ/MXEP1CqV54Pxm9DyxRo69z9WrXEQk\nb/I7ctrbmRQ759x1BRFcMJS4i4bf/9zOg6MXsHDdTjo2rMKQXs1pWK1cuMMSESl21KtcCkzaYceH\nv61m2Pgk9h1M44Yz6nNnl4aULZXlkAAiIpJL+e1VfoKZTTSzBf7zE83sgYIOUiJDdJRxVfu6/HRv\nZy5sncArk5fTbcQUvl+wPk+9z0VEJHeC6Zz2BjAYOAjgnJsHXBbKoKToq1KuNMMvacXnt7SnQlws\nt3zwO1e/PZOVW/aEZHmjE5Pp8OQk6t3/DR2enMToxOSQLEdEpKgLJnGXdc79L0NZoY2aJkVbu7qV\n+PrOjjzUsxm/r95O95FTGfFDEqkH0nKeOUjpo7Alp6TigOSUVAaPmq/kLSIlUjCJe4uZNQAcgJld\njHdPbhEAYqKjuK5jPSYN7MT5J9bghUnLOHvkFH5ctLFA6h82PumooVMBUg+mMWx8UoHULyISSYJJ\n3LcDrwFNzCwZ6A/cGtKoJCJVq1CGkZe25pObTqNsqWhufG8W178zkz+37s1XvRqFTUTkLzkmbufc\nCudcN6Aq0MQ519E5tyrkkUnEOq1+Zb656wz+r0dTfl2xlbNHTuG5CUvZdzBvzecahU1E5C/B9Cr/\nj5nFO+f2OOd2mdmxZvZ4YQQnkSs2Ooobz6zPxIGdObtZdUZO+INzRk7lpyWbcl2XRmETEflLME3l\n5znnUtKfOOe2Az1CF5IUJ8dVLMOLV5zEhzecSmy0ce07M7npvVms3R5883nvNgkM7dOShPg4DO8O\nY7o9qIiUVMGMnDYPaOec2+8/jwNmOeeaF0J8gAZgKS4OHDrMf6et5PmJS3E47uzSiBvOqEfpmOic\nZxYRKUHyNQAL8CEw0cyuN7PrgR+BdwsyQCkZSsVEcWvnBkwY2ImzGldj2Pgkznv2Z35eujncoYmI\nRIyghjw1s3OBbv7TH51z40MaVQY64i6eJidtYsjYhazaupfzW9bggZ5NqVFRHc5ERPI8VrmZRQMT\nnHNnhSq4YChxF1/7D6Xx+pQVvPjTMqKjjLu6NuK6DvUoFRNMY5CISPGU56Zy51wacNjMKoYkMinx\nSsdEc2fXRkwY0InTG1Thye+W0OP5n5mxfEu4QxMRKZKCOazZDcw3s/+a2fPpj1AHJiVL7UplefPq\ntvz36rbsP5TGFW/8xl0fJ7Jx575whyYiUqQEcy/GUf5DJOS6Nq1Oh4ZVeGXycl6ZspxJSzbRv1sj\nrj69LrHRaj4XEQm2c1occN8vgtYAACAASURBVLxzLiyDQ+scd8m0assehoxbyOSkzTSuXp5HL2zO\nqfUrhzssEZGQy+/9uC8A5gDf+89bm9nYgg1R5O/qVjmGt69px+v/PJnd+w9x6eu/MuDTOWzetT/c\noYmIhE0wbY9DgFOAFADn3BygfghjEjnCzDin+XFMGNCJ289qwLh56+gyfDLvTF/JobTD4Q5PRKTQ\nBZO4DzrndmQo0xZTClVcqWgGdW/C+P5n0vr4eIaMW0SvF6cze/W2cIcmIlKogkncC83sCiDazBqZ\n2QvAjBDHJZKp+lXL8d51p/DylSexfe8B+r7yC4M+n8vW3Wo+F5GSIZjEfSfQHNgPfATswLsnt0hY\nmBk9WtZgwoBO3NypPl8lJnPW8Mm8/+tq0g7n3NlSRCSS5TRyWlWgDrAs8A5hhU29yiU7yzbt4sHR\nC/llxVZaJlTksd4taF07PtxhiYjkWZ56lZvZDcBC4AVgiZn1ClF8IvnSsFp5PrrxVJ6/vA0bd+7j\nopenM3jUPLbvORDu0EREClx2TeX9gebOufbA6cDgwglJJPfMjF6tajJxYCeu71CPz2atpcuIyXzy\nvz85rOZzESlGskvcB5xzmwGccyuA0oUTkkjelS8TywM9m/HNXR1pVK0894+aT59XZjB/bcYLI0RE\nIlOW57jNbBPwSUDRZYHPnXN3hTa0v+gct+SFc47Rc5J54pslbN2zn3+cWod7z2lMxbKx4Q5NRCRb\n2Z3jzm6s8kEZns8uuJBEQs/MuKhNLbo0qc7IH//gvV9W8e389dx/XhP6nlSLqCgLd4giIrkW1Fjl\n4aYjbikIC9ft4KExC5m9ejsn1zmWRy9sTvOaumOtiBQ92R1xK3FLiXL4sOPL39cy9LslpOw9wFXt\n6zLgnBOoUCa8zeejE5MZNj6JdSmp1IyPY1D3xvRukxDWmEQkfPJ1kxGR4iQqyrikbW1+GtiZK049\nnnd/WUWX4VP4KnEt4dqJHZ2YzOBR80lOScUBySmpDB41n9GJyWGJR0SKNiVuKZEqlo3l8d4tGXt7\nRxKOjeOeT+dy6eu/krRhV6HHMmx8EqkH044qSz2YxrDxYbmLrogUccHc1vNpM6tgZrFmNtHMNpvZ\nPwojOJFQa1mrIl/dejpD+7Tkj4276PH8zzzxzSJ27z9UaDGsS0nNVbmIlGzBHHGf45zbCfQEVgEN\n+XuPc5GIFRVlXH7K8Uwa2Jl+bWvx5rSVdB0xmbFz1xVK83nN+LhclYtIyRZM4k6/ZOx84PNMbvEp\nUixUOqYUQ/ucyKhbT6dq+dLc9XEiV775G8s2hbb5fFD3xsTFRh9VFhcbzaDujUO6XBGJTMEk7q/N\nbAlwMjDRv/HIvmAqN7NVZjbfzOaY2Sy/bIiZJftlc8ysR97DFyl4bY4/ljG3d+Sx3i1YkLyD8577\nmSe/W8LeA6FpPu/dJoGhfVqSEB+HAQnxcQzt01K9ykUkU0FdDmZmlYAdzrk0MysLVHDObQhivlVA\nW+fcloCyIcBu59zwYIPU5WASLlt27+fJ75bwxey11KxYhgd7NuPcFsdhpsFbRCR0CuJysJpAXzO7\nCrgYOKegghMpyqqUK83wS1rxxS3tqRAXy60f/s7Vb89k5ZY94Q5NREqoYHqVP4x3a88XgLOAp4Fg\nb/HpgB/MbLaZ3RRQfoeZzTOzt8zs2CyWe5OZzTKzWZs3bw5ycSKh0bZuJb6+syMPX9CMxNXb6T5y\nKsPHJ5F6IC3nmUVEClCOTeVmNh9oBSQ651qZWXXgA+fc2TlWbpbgnEs2s2rAj8CdQBKwBS+pPwbU\ncM5dl109aiqXomTTrn0M/XYJXyUmkxAfx8MXNOPsZtXVfC4iBSa/TeWpzrnDwCEzqwBsAmoHs2Dn\nXLL/dxPwFXCKc26jcy7Nr/MN4JRg6hIpKqqVL8PIS1vz6U2ncUzpaG56fzbXvzuLP7fuDXdoIlIC\nBJO4Z5lZPF6SnQ38DvyS00xmdoyZlU//H++8+AIzqxHwsouABbmOWqQIOLV+Zb656wweOL8pv63Y\nSreRU3h2wh/sO6jmcxEJnVzdZMTM6uL1KJ8XxGvr4x1lg3ct+EfOuSfM7H2gNV5T+SrgZufc+uzq\nUlO5FHUbduzjiW8XM27uOo6vVJZHejXnrCbVwh2WiEQo3R1MpJBMX7aFh8YsYPnmPZzdrDoP9WxG\n7Uplwx2WiEQY3R1MpJB0aFiF7+4+k/vObcK0pVs4e+QUXpy0lP2H1HwuIgVDiVukgJWKieLWzg2Y\nOLATXZpUY/gPf3Dusz8z9Q9d1igi+RfM5WCVMine5Zw7GJqQ/q6gmspHJyYzbHwS61JSqRkfx6Du\njXM9rGRB1CEly5Q/NjNk7EJWbtnDeS2O48GezXQDERHJVr7OcfvDltYGtgMGxAMbgI3Ajc652QUa\nbSYKInGPTkxm8Kj5R933OC42OldjQhdEHVIy7T+UxhtTV/DiT8swjLu6NuL6jvUoFaNGLxH5u/ye\n4/4R6OGcq+KcqwycB3wN3Aa8XHBhhtaw8UlHJVyA1INpDBufVKh1SMlUOiaaO7o04sd7OnFGoyo8\n9f0SzntuKjOWbcl5ZhGRAMEk7tOcc+PTnzjnfgDaO+d+BUqHLLICti4lNVfloapDSrbalcry+lVt\neeuathxMc1zx5m/c+XEiG3YEdcM9EZGgEvd6M7vPzOr4j38BG80sGjgc4vgKTFbnFHNzrrEg6hAB\n6NKkOj/ccyb9uzVi/MINdB0xmTd/XsHBtIj5SYlImASTuK8AagGj/cfxflk00C90oRWsQd0bExcb\nfVRZXGw0g7o3LtQ6RNKViY2mf7cT+PGeMzmlXiUe/2Yx5z//M7+u2Bru0ESkCCtRA7CoV7kUVc45\nfly0kUfGLSI5JZWL2iQwuEcTqpUvE+7QRCQM8tur/ATgXqAu3tClADjnuhRgjNnSyGlSUqQeSOPl\nyct4bcoKSsdEMeCcE/jnaXWIiQ6+97l2LkUiX34T91zgVbwbjBzpUl0Yl4GlU+KWkmbF5t08PHYh\nPy/dQtMaFXjswua0rZvZkApH0yWLIsVDfi8HO+Sce8U59z/n3Oz0RwHHKCIB6lctx3vXncIrV55E\nyt4DXPzqL9z7+Vy27N6f7Xy6ZFGk+AsmcY8zs9vMrIaZVUp/hDwykRLOzDivZQ0mDuzELZ0aMDox\nmS7DJ/P+L6tIO5x5S5kuWRQp/oJJ3FcDg4AZeM3lswG1W4sUkrKlYrj/vCZ83/8MWiRU5MExC7nw\npWkk/rn9b6/VJYsixV+Oids5Vy+TR/3CCE5E/tKwWnk+vOFUXri8DZt37afPKzMYPGoe2/ccOPIa\nXbIoUvzFZDXBzLo45yaZWZ/MpjvnRoUuLBHJjJlxQauanNWkGs9N+IO3pq/iuwUbuO/cJlzatvaR\nDmjqVS5SfGXZq9zMHnHOPWxmb2cy2TnnrgttaH9Rr3KRzCVt2MWDYxbwv5XbaFU7nscvbEHLWhXD\nHZaI5FN+Lwer55xbmVNZKClxi2TNOcfoOck88c0Stu7Zz5WnHs+gc5pQsWxsuEMTkTzK7+VgX2ZS\n9kX+QhKRgmJmXNSmFpPu7cTV7evy0W9/ctaIyXw2aw2Hs+h9LiKRK7tz3E2A5kDFDOe5KwAah1Gk\niKlQJpYhvZrTr21tHhyzgH99MY9PZ67h0Qub07ymms9FiovsjrgbAz2BeOCCgMdJwI2hD01E8qJZ\nzQp8fnN7hl18Iqu27OGCF6YxZOxCdu47GO7QRKQABHOOu71z7pdCiidTOsctkjc79h5k+A9JfPDb\naiofU5p/92jCRW0SMLNwhyYi2chv57QywPV4zeZHmsjVq1wkcsxfu4MHxixg7poUTqlbiUd7N6fJ\ncRXCHZaIZCG/ndPeB44DugNT8O7NvavgwhORUGtZqyJf3Xo6T/ZpydJNuzj/+Wk8/vUidqn5XCTi\nBJO4GzrnHgT2OOfeBc4HTg1tWCJS0KKijMtOOZ5JAzvTr21t/jt9JV1HTGHs3HXk1PImIkVHMIk7\nfZc8xcxaABWBaqELSURC6dhjSjG0T0u+uq0D1SuU4a6PE7nijd9YulENaSKRIJjE/bqZHQs8AIwF\nFgFPhzQqEQm51rXjGX17Bx7v3YJF63dy3nM/M/S7xezZfyjcoYlINnLsnFYUqHOaSGht3b2fp75f\nwmez1lKjYhke7NmM81ocp97nImGS585pZhZtZlUCnpcysxvNbHFBByki4VO5XGmevrgVX97anviy\npbjtw9+56q3/sWLz7nCHJiIZZJm4zewyYBswz8ymmNk5wAqgB3BlIcUnIoXo5DqVGHdHB4Zc0Iw5\nf6bQ/dmpDBu/hNQDaeEOTUR82d0dbAHQ2zm3zMxOAn4BLnbOjSvMAEFN5SLhsHnXfoZ+u5hRickk\nxMfx0AXNOKdZdTWfixSCvDaVH3DOLQNwzv0OLA1H0haR8KhavjTPXNqaz25uT7nSMdz8/myue2cm\nq7fuCXdoIiVadkfca4FnAooGBD53zj3zt5lCREfcIuF1MO0w785YxbMTlnIg7TC3dGrAbZ0bUCY2\nOtyhiRRLeT3ifgMoH/DI+FxESojY6ChuOKM+Ewd24tzmx/H8xKWcPXIKExdvDHdoIiWOLgcTkVyb\nsXwLD41ZyLJNu+nWtDoPX9CM2pXKhjsskWIjv2OVi4gc5fQGVfj2rjO4/7wmzFi+hW7PTOGFiUvZ\nf0i9z0VCTYlbRPKkVEwUt3RqwIQBnejatBojfvyD7iOnMuWPzeEOTaRYU+IWkXypGR/Hy1eezHvX\nnUKUGVe/9T9ueX82ySmp4Q5NpFjKrlf5gOxmVK9yEclo/6E03vx5JS9MWoph3Nm1ITd0rE+pGB0j\niORGXs9xl8/hISJylNIx0dx+VkMmDOjEGY2q8PT3SZz73FSmL9sS7tBEig31KheRkPlpySaGjFvI\n6q176XliDR44vxnHVSwT7rBEirzsjrhjgpi5DHA90Bw48otzzl1XYBGKSLF0VpNqtG9QmVenLOfl\nycv5ackm+nc7gWs61CU2Ws3nInkRzC/nfeA4oDswBagF7AplUCJSfJSJjaZ/txOYcE8nTq1fmSe+\nXcz5z//Mryu2hjs0kYgUTOJu6Jx7ENjjnHsXOB84NbRhiUhxc3zlsrx1TTveuKote/ancdnrv9L/\nk0Q27doX7tBEIkowifug/zfFzFoAFYFqwVRuZqvMbL6ZzTGzWX5ZJTP70cyW+n+PzVvoIhKJzm5W\nnQkDOnFnl4Z8O38DXYdP4a1pKzmUdjjcoYlEhGAS9+t+cn0QGAssAp7KxTLOcs61DjjJfj8w0TnX\nCJjoPxeREiSuVDQDz2nM+HvOpE2dY3n060X0fGEas1ZtC3doIkVejr3KzSzaOZencQzNbBXQ1jm3\nJaAsCejsnFtvZjWAyc65xtnVo17lIsWXc47xCzfw6LhFrNuxj74n1WJwjyZUKVc63KGJhE1+xypf\naWavm1lXM7NcLtsBP5jZbDO7yS+r7pxb7/+/AaieRdA3mdksM5u1ebOGUBQprsyMc1vUYMLATtza\nuQFj5ybTZfhk3vtlFWmHi/7lqiKFLZgj7rJAT+Ay4GRgHPCJc25ajpWbJTjnks2sGvAjcCcw1jkX\nH/Ca7c65bM9z64hbpORYtmk3D49dwPRlW2leswKP9W7BScerK4yULPk64nbO7XXOfeac6wO0Birg\nXRaWI+dcsv93E/AVcAqw0W8ix/+7Kah3ISIlQsNq5fjg+lN58Yo2bNm9nz4vz+D+L+exbc+BcIcm\nUiQENQKCmXUys5eB2XiDsPQLYp5jzKx8+v/AOcACvA5uV/svuxoYk4e4RaQYMzN6nliTiQM7c9OZ\n9fli9lq6jJjMR7/9qeZzKfGCaSpfBSQCn+E1c+8JqmKz+nhH2eCN0PaRc+4JM6vs13U8sBro55zL\ntiupmspFSrY/Nu7iwdEL+G3lNlrVqshjvVtwYq34nGcUiVDZNZUHk7grOOd2hiSyIClxi4hzjrFz\n1/H4N4vZsns/V5xyPIO6Nya+bKlwhyZS4PI1VjlQwczeBTr4z38G7nbOrS2oAEWkaBqdmMyw8Ums\nS0mlZnwcg7o3pnebhLDEYmZc2DqBs5pUY+SPf/DeL6v5bsEG7j+3CRefXIuoqNxe9CISmYI5x/02\n3nnpmv5jnF8mIsXY6MRkBo+aT3JKKg5ITkll8Kj5jE5MDmtcFcrE8vAFzRl3R0fqVzmGf305j4tf\nncGC5B1hjUuksASTuKs65952zh3yH+8AVUMcl4iE2bDxSaQePHrspdSDaQwbnxSmiI7WrGYFPr+l\nPcMvacXqrXvp9eI0Hh6zgB2pB3OeWSSCBdNUvtXM/gF87D+/HNBtfUSKuXUpqbkqz0komt3NjItP\nrsXZzaoz4ock3v91Nd/MX8/g85rS56QEcj9mlEjRF8wR93V4l39tANYDFwPXhjIoEQm/mvFxuSrP\nTqib3SvGxfLohS0Ye0dHah1bloGfz6Xfa7+wZENY+9WKhES2idvMooE+zrlezrmqzrlqzrnezrk/\nCyk+EQmTQd0bExcbfVRZXGw0g7pne2uBTBVWs3uLhIqMuvV0nurbkmWbdnP+89N47OtF7Nqn5nMp\nPrJN3P7NRS4vpFhEpAjp3SaBoX1akhAfhwEJ8XEM7dMyT83bBd3snp2oKOPSdsczaWBnLm1Xm7em\nr6TLiCmMmZNMTpe/ikSCYK7jHgnEAp8CRwZfcc79HtrQ/qLruEUiW4cnJ5GcSZJOiI9j+v1dQrrs\nuWtSeHDMAuat3UH7+pV59MLmNKpePqTLFMmv/N4drDXQHHgUGOE/hhdceCJS3BVks3tutaodz1e3\ndeDx3i1YtH4n5z33M0O/Xcye/YdCvmyRUMjxiLso0BG3SOQrCoO5bN29n6e+X8Jns9ZyXIUyPNiz\nGT1aHqfe51Lk5HfI08rAw0BHvPtrTwMedc4V2iVhStwiUpBmr97Og6MXsGj9Ts5oVIUhvZrToGq5\ncIclckR+m8o/ATYDffEuBduMd75bRCQinVznWMbe0YFHejVnzpoUzn12Kk9/v4S9B9R8LkVfMEfc\nC5xzLTKUzXfOtQxpZAF0xC0iobJ5136GfreYUb8nkxAfx0MXNOOcZtXVfC5hld8j7h/M7DIzi/If\n/YDxBRuiiEh4VC1fmmf6teazm9tTrnQMN78/m2vfmcmqLUHdwVik0AVzxL0LOAY47BdF8ddlYc45\nVyF04Xl0xC0iheFg2mHe+2U1I3/8gwNph7mlUwNu69yAMhl6xIuEWr6OuJ1z5Z1zUc65GP8R5ZeV\nL4ykLSJSWGKjo7i+Yz0mDuzEeS2O4/mJSzl75BQmLt4Y7tBEjgimqRwz62Nmz5jZCDPrHeqgRETC\nqXqFMjx3WRs+uvFUSsdEc/27s7jh3Zms2bY33KGJ5Jy4zexl4BZgPrAAuMXMXgp1YCIi4XZ6gyp8\ne9cZ/LtHE2Ys30q3Z6bw/MSl7Msw7rpIYQrmHPcSoKnzX2hmUcBC51zTQogP0DluEQm/9TtSefyb\nxXwzbz11K5dlSK/mdG5cLdxhSTGV317ly4DjA57X9stEREqMGhXjeOmKk3j/+lOIMuOat2dyy/uz\nMx2DXSSUgknc5YHFZjbZzH4CFgEVzGysmY0NbXgiIkXLGY2q8l3/MxjUvTGT/9hE1xGTeemnZRw4\ndDjnmUUKQDBN5Z2ym+6cm1KgEWVCTeUiUhQlp6Ty6LiFjF+4kfpVj+HRXi3o2KhKuMOSYiBfY5Vn\nUllH4HLn3O0FEVwwlLhFpCj7KWkTQ8YuZPXWvZzfsgYP9GxKjYpx4Q5LIlh+z3FjZm3MbJiZrQIe\nAxYXYHwiIhHtrMbVGN//TAacfQITFm+k64gpvDZlOQfT1HwuBS/LI24zOwG43H9swbuxyL3OuTqF\nF55HR9wikq4gbw8ailuNrtm2l0fGLWTC4k00qlaORy9sQfsGlfNVp5Q8eWoqN7PDwM/A9c65ZX7Z\nCudc/ZBFmgUlbhEBL9EOHjWf1IDrqONioxnap2WuE25B1pWZCYs2MmTcQtZuT+XC1jX5vx5NqVah\nTL7rlZIhr03lfYD1wE9m9oaZdQV0uxwRCZth45OOSrQAqQfTGDY+Kax1ZaZbs+pMGNCJu7o05Lv5\nG+gyYgr/nbaSQ2o+l3zKMnE750Y75y4DmgA/Af2Bamb2ipmdU1gBioikW5fFNdNZlRdWXVkpExvN\ngHMa88M9Z3JynWN57OtF9HxhGjNXbSuwZUjJE8xNRvY45z5yzl0A1AISgftCHpmISAY14zPvqZ1V\neWHVlZO6VY7hnWvb8eo/TmbXvkNc8uovDPhsDpt37S/wZUnxF1Sv8nTOue3Oudedc11DFZCISFYG\ndW9MXIZbbMbFRjOoe+Ow1hUMM+PcFsfx44Azua1zA8bNXUeXEZN5d8Yq0g7n7rJcKdlylbhFRMKp\nd5sEhvZpSUJ8HAYkxMfluTNZQdaVG2VLxfCvc5vwff8zaVUrnofHLqTXi9P4/c/tIV2uFB+5HoAl\nHNSrXESKI+cc38xfz2NfL2Ljzv1c2rY2953XhErHlAp3aBJm+R6ARURECp6Z0fPEmkwc2Jmbz6zP\nl7+v5azhk/nwt9VqPpcsKXGLiIRZudIxDO7RlG/vPoOmNcrzf18t4KKXpzN3TUq4Q5MiSIlbRKSI\nOKF6eT6+8TSeu6w163fso/fL0/n3V/NJ2Xsg3KFJEaLELSJShJgZF7ZOYNLATlx7ej0+nbmGs4ZP\n5tOZf3JYzeeCEreISJFUvkwsD13QjK/v7EjDauW478v59H11BguSd4Q7NAkzJW4RkSKsaY0KfHZz\ne0Zc0oo12/bS68VpPDxmATtSD4Y7NAkTJW4RkSLOzOh7ci0mDuzMP0+rw/u/rqbriMl8MXstkXBJ\nrxQsJW4RkQhRMS6WRy5swdg7OlK7Ulnu/Xwu/V77hcXrd4Y7NClEStwiIhGmRUJFvrzldJ7ueyLL\nN++h5wvTeHTcInbtU/N5SaDELSISgaKijH7tajNpYCcua1ebt2espMuIKYyZk6zm82JOiVtEJILF\nly3FExe1ZPRtHahRsQx3fzKHy17/lT827gp3aBIiStwiIsVAq9rxfHVbB/5zUUuWbNhFj+d+5j/f\nLmb3/kPhDk0KWMhvMmJm0cAsINk519PM3gE6AekXI17jnJuTXR26yYiIFHWjE5MZNj6JdSmp1IyP\nY1D3xiG/01hWtu05wFPfLeHTWWuoGBdLlMH2vQdJCHNcErzsbjISUwjLvxtYDFQIKBvknPuiEJYt\nIhJyoxOTGTxqPqkH0wBITkll8Kj5AGFJkpWOKcVTF59IjYpleG7iUtIPz8IdlxSMkDaVm1kt4Hzg\nzVAuR0QknIaNTzqStNOlHkxj2PikMEXk+Xz2WjK2qaYeTOOp75eEJR4pGKE+x/0s8C/gcIbyJ8xs\nnpmNNLPSmc1oZjeZ2Swzm7V58+YQhykiknfrUlJzVV5Yslr++h37+H7BevU+j1AhS9xm1hPY5Jyb\nnWHSYKAJ0A6oBNyX2fzOudedc22dc22rVq0aqjBFRPKtZnxcrsoLS1bLj4kybvngd655eyYrt+wp\n5Kgkv0J5xN0B6GVmq4BPgC5m9oFzbr3z7AfeBk4JYQwiIiE3qHtj4mKjjyqLi41mUPfGYYrIk1Vc\nT/U9kYd6NmP26u10HzmVET8kkXogLYtapKgJWec059xgvKNrzKwzcK9z7h9mVsM5t97MDOgNLAhV\nDCIihSG9o1dR6VUebFw9T6zBf75dzAuTlvFVYjJDLmhOt2bVwxmyBCHkl4PBUYm7p5lNAqoCBswB\nbnHO7c5ufl0OJiISOr8s38pDYxawdNNuujapxpBezaldqWy4wyrRsrscrFASd34pcYuIhNbBtMO8\nPX0lz05YStphx22dG3Jzp/qUydDULoUju8StkdNERITY6ChuOrMBEwd2oluz6oyc8Afdn53KT0mb\nwh2aZKDELSIiR9SoGMdLV5zEB9efSnSUce3bM7npvVms3b433KGJT03lIiKSqQOHDvPmtBW8MHEZ\nhw4fJi42mp37Dmno1EKgpnIREcm1UjFR3Na5If/q3pi0w46d+7wblqQPnTo6MTnMEZZMStwiIpKt\nN6et5HCGxtnUg2k8+Z2GTg0HJW4REclWVkOnbti5j9emLOfAoYyjWksoKXGLiBQxoxOT6fDkJOrd\n/w0dnpwU9ibprIZOLRMTxdDvltDj+Z+ZsXxLIUdVcilxi4gUIem3CE1OScVRNM4nZzV06pN9T+S/\nV7dl/6E0rnjjN+76OJFNO/eFKcqSozDuxy0iIkHK7hah4erFndPQqR0aVuHlyct5dcpyJi3ZxD1n\nn8DV7esQE61jw1DQ5WAiIkVIvfu/+ds9tMEbI3rlk+cXdji5smrLHoaMW8jkpM00Oa48j/VuQbu6\nlcIdVkTS5WAiIhGiqN4iNBh1qxzD29e047V/nsyufYe45NVfGPDZHDbv2h/u0IoVJW4RkSKkqN4i\nNFhmRvfmx/HjgDO5/awGjJu7ji4jJvPujFUcSlPv84KgxC0iUoT0bpPA0D4tSYiPw4CE+DiG9mkZ\ncaOUlS0Vw6DuTRjf/0xa147n4bEL6fXidGav3h7u0CKeznGLiEhIOef4bsEGHh23iA0799GvbS3u\nO7cJlcuVDndoRZbOcYuISNiYGT1a1mDiwE7c3Kk+o35PpsuIKXzw62rSMg7JJjlS4hYRkUJxTOkY\nBp/XlO/7n0GzGhV4YPQCLnp5OnPXpIQ7tIiixC0iIoWqYbXyfHTjqTx3WWs27NhH75en8++v5rN9\nz4FwhxYRlLhFRKTQmRkXtk5g4sBOXNehHp/OXEOXEZP5dOafHFbzebaUuEVEJGzKl4nlwZ7N+Oau\njjSqVp77vpxP31dnsCB5R7hDK7KUuEVEJOyaHFeBT28+jWf6tWLNtr30enEaD41ZwI69B8MdWpGj\nxC0iIkWCmdHnpFpM6a67+wAAEFZJREFUHNiZq9rX5YNfV9NlxGS+mL1WzecBlLhFRKRIqRgXy5Be\nzRl3Z0fqVC7LvZ/Ppd9rv7B4/c5wh1YkKHGLiEiR1LxmRb645XSe7nsiK7bsoecL03hk3EJ27ivZ\nzee6raeIiBRZUVFGv3a1Oad5dYb/kMQ7M1bx9bz1/N//t3fvcVaV9R7HP19mIJBMRPBKBSRKeDJE\nMg0TxWt4rXxF3tK0tDIzs5svO4bVq6MnC29lx/vleDuWoscuCILKKcWwAUQBRdICL6ABpRHK+Dt/\nPM/W7TTDMMPM3u69vu/Xa71mrWfdnt/aG36znrXmeca/n8NHbsudc55tc7jRjprctKzDx+rMPhvL\nXZ6amVnNmLd0Ff8+eT5zl67mfQP7snTlGtaue3Pwkj49GzrVt/vkpmWcdfujbxkLvb1jdWafDeUu\nT83MrC7sPKgfd3xpDD/8+AdY8uIrb0naAGtea+ZHUxZ1+Lg/mrLoLQl4Q47VmX26ghO3mZnVlB49\nxNEffg9tNRg/u2pNh4/Z1j7rO1Zn9ukKTtxmZlaTtuvXp9XygZt2fNSxbds4Vlvlnd2nKzhxm5lZ\nTfrGgTvSp2fDv5S/9MqrnP/bhfzj1XUbdaw+PRv4xoE7duk+XcFvlZuZWU0qvQBW/lb3F8YOZe7S\n1Vx231Pc2bSMcw4dwYE7bY2kDh+rvTfEO7NPV/Bb5WZmVndmP/1XvjN5Pguf/zt77TCQcw/biSED\n+la7WhvMb5WbmVmhjB7cn7tP25PvHjqCpmdWcuCkB/jxPYtY82pz+zu/zTlxm5lZXWps6MFnxwzh\n3jPHcvDO23DJ9MXsP+l+pj7+QrWrtlGcuM3MrKImNy1jzHnTGfLtXzHmvOlMblrWrefb8l29mTRh\nJLecvDub9Grg89fP5qRr/8CfX/pHt563uzhxm5lZxZR6G1u2ag0BLFu1hrNuf7TbkzfA7kO34Fdf\n+Shnj38/Dy15if0n3c9F057kn6/VVvO5E7eZmVVMtXobK+nZ0IPP7zWUe8/cm/1HbMWkaU9w4IUP\nMGPR8oqcvys4cZuZWcVUq7exlrberDeXHj2KGz/3YRp7iM9e8wdOvn42S1e+/ZvPnbjNzKxiqtXb\nWFvGbD+A35y+F986aDgzn3yR/X5yPz+dsZi1696+zedO3GZmVjHV6m1sfXo19uCLe7+Pe88cyz47\nbsmPpiziYxfOZOaTK6pWp/Vx4jYzs4o5Ypft+I9PfIDt+vVBpP7Gu2IYzK6wbb8+XHbsrlx34m4E\ncNxVD3PqjX/kudWVbcZvj3tOMzMza2HtumaueGAJl85YTA+Jr+w7jBPHDKFXY2Xud91zmpmZWQe8\no7GBL48bxtQzxjJm+wGc95uFjL94Jr9/6sVqV82J28zMrC3v7r8JV3xmNFefMJq165o5+opZfOXm\nJl742z+rVicnbjMzs3aMG74VU88Yy+n7DuO3jz3PuAvu48qZS3it+fWK16XbE7ekBklNku7Oy0Mk\nzZK0WNKtknp1dx3MzMw2Vu+eDZyx/w5MPWMvdhvSnx/8agGHXPx/zFryUkXrUYk77tOBBWXL5wOT\nImJ7YCVwUgXqYGZm1iXeu0Vfrj7hQ1x+3K68vHYdEy5/iK/9zxxef70yL3t3a+KWNAg4GLgyLwsY\nB/wib3IdcER31sHMzKyrSeKAnbZm2tfG8uV9tqdvr0Z69FBFzt3Yzce/EPgmsGle3gJYFRHr8vJS\noPp/vGdmZtYJfXo18PUDd6SSf1rdbXfckg4BlkfEI53c/2RJsyXNXrHi7dl7jZmZGaQ78Erpzqby\nMcBhkp4GbiE1kV8E9JNUutMfBLQ6lltEXB4RoyNi9MCBA7uxmmZmZrWj2xJ3RJwVEYMiYjDwaWB6\nRBwDzACOzJsdD9zZXXUwMzOrN9X4O+5vAV+TtJj0zPuqKtTBzMysJnX3y2kARMR9wH15fgmwWyXO\na2ZmVm/cc5qZmVkNceI2MzOrIU7cZmZmNcSJ28zMrIY4cZuZmdUQJ24zM7Ma4sRtZmZWQ5y4zczM\naogqOaJJZ0laATzTBYcaALzYBcepNUWNGxy7Yy+WosYN9Rf7eyOi1YE6aiJxdxVJsyNidLXrUWlF\njRscu2MvlqLGDcWK3U3lZmZmNcSJ28zMrIYULXFfXu0KVElR4wbHXlRFjb2ocUOBYi/UM24zM7Na\nV7Q7bjMzs5pWiMQt6SBJiyQtlvTtatenq0m6WtJySfPLyvpLmirpyfxz81wuSRfnazFP0qjq1Xzj\nSHq3pBmSHpf0mKTTc3kRYu8t6WFJc3Ps5+byIZJm5RhvldQrl78jLy/O6wdXs/5dQVKDpCZJd+fl\nQsQu6WlJj0qaI2l2LivCd76fpF9IWihpgaQ9ihB3a+o+cUtqAH4KfAwYARwlaUR1a9XlrgUOalH2\nbeDeiBgG3JuXIV2HYXk6GbisQnXsDuuAMyNiBLA7cGr+bIsQ+1pgXER8EBgJHCRpd+B8YFJEbA+s\nBE7K258ErMzlk/J2te50YEHZcpFi3yciRpb9+VMRvvMXAb+NiOHAB0mffRHi/lcRUdcTsAcwpWz5\nLOCsaterG+IcDMwvW14EbJPntwEW5fn/Ao5qbbtan4A7gf2LFjuwCfBH4MOkDigac/kb331gCrBH\nnm/M26nadd+ImAeR/qMeB9wNqECxPw0MaFFW1995YDPgTy0/t3qPu62p7u+4ge2Av5QtL81l9W6r\niHguzz8PbJXn6/J65ObPXYBZFCT23FQ8B1gOTAWeAlZFxLq8SXl8b8Se168GtqhsjbvUhcA3gdfz\n8hYUJ/YA7pH0iKSTc1m9f+eHACuAa/LjkSsl9aX+425VERJ34UX6lbNu/3xA0juBXwJfjYi/la+r\n59gjojkiRpLuPncDhle5ShUh6RBgeUQ8Uu26VMmeETGK1Bx8qqS9ylfW6Xe+ERgFXBYRuwCv8Gaz\nOFC3cbeqCIl7GfDusuVBuazevSBpG4D8c3kur6vrIaknKWnfGBG35+JCxF4SEauAGaTm4X6SGvOq\n8vjeiD2v3wx4qcJV7SpjgMMkPQ3cQmouv4hixE5ELMs/lwN3kH5pq/fv/FJgaUTMysu/ICXyeo+7\nVUVI3H8AhuU3TnsBnwbuqnKdKuEu4Pg8fzzp+W+p/DP5rcvdgdVlTU01RZKAq4AFEfGTslVFiH2g\npH55vg/p2f4CUgI/Mm/WMvbSNTkSmJ7vUGpORJwVEYMiYjDp3/P0iDiGAsQuqa+kTUvzwAHAfOr8\nOx8RzwN/kbRjLtoXeJw6j7tN1X7IXokJGA88QXoGeHa169MN8d0MPAe8RvrN9CTSM7x7gSeBaUD/\nvK1Ib9k/BTwKjK52/Tci7j1JTWPzgDl5Gl+Q2HcGmnLs84FzcvlQ4GFgMXAb8I5c3jsvL87rh1Y7\nhi66DnsDdxcl9hzj3Dw9Vvr/rCDf+ZHA7PydnwxsXoS4W5vcc5qZmVkNKUJTuZmZWd1w4jYzM6sh\nTtxmZmY1xInbzMyshjhxm5mZ1RAnbiscSc15ZKXH8uhaZ0rqtn8Lkk7MoznNkzRf0uG5/HuS9uvG\n8+6eR8Oak0dTmpjL95b0kU4cb6Sk8Z3Y7+Yc+xktyidKWpbrN1/SYR09dhvn+30b5ddKOrK1dWa1\npLH9TczqzppIXYUiaUvgJuBdwHe7+kSSBgFnA6MiYnXunnUgQESc09Xna+E64FMRMTePklfqvGJv\n4GWg1QTXmtzj2EhgNPDrDuy3NfChSCNztWZSRFwg6f3ATElbRsTrZfs3xpv9j2+QiOjwLyVmtcR3\n3FZokbqNPBn4cu5labCkmZL+mKePAEi6XtIRpf0k3SjpcEk7KY2LPSffVQ5rcYotgb+TEiUR8XJE\n/Ckf4407QKUxls/N53xU0vBc/k5J15TdsX8ylx8g6cG8/W35F4KWtiR1zEOkfs0fVxqM5QvAGbnO\nH5V0aL4zb5I0TdJW+RwTJd0g6XfADcD3gAl5vwnlJ1IaH7xUzyZJ++RV9wDblc61ns9hAWmY1gH5\nuvxc0izgP3NvYVfn69xU1mLR6rWX9HL+KUmXSlokaVq+HqX67irpfqWBOqbozW4z75N0fj7uE6U6\nKw3ockFuGZgn6TRJ4yRNLjvm/pLuaCtGsy5T7R5gPHmq9AS83ErZKtLIQpsAvXPZMGB2nh8LTM7z\npSEGG4FLgGNyeS+gT4vjNpCGlfwzcA1waNm6a4Ej8/zTwGl5/kvAlXn+fODCsn02BwYADwB9c9m3\nyD2ntTj3OaRxqe8ATimLayLw9RbHLHXG9Dngx2XbPVKKCTgBuLSNa3omcHWeH57j7U2L4WZb7PNG\nPUhDkj5L6vHqWtJQnQ153Q+BY/N8P1IviH3buvalzxf4BGnUtAZg2/wZHwn0JLU2DMzbTSir+31l\n8Y8HpuX5L5L6xy4NG9o/13Vh2XFuKv98PXnqrslN5WZv1RO4VNJIoBnYASAi7pf0M0kDgU8Cv4yI\ndZIeBM7OTeK3R8ST5QeLiGZJBwEfIvWvPEnSrhExsZVzlwZJeYSUdAD2I/XHXTreSqXRsUYAv5ME\nKWk92PJgEfE9STeS+rM+GjiK1Eze0iDg1nzX2Yv0S0nJXRGxppV9WtqTlEiJiIWSniFdu7+td690\n538sqVViQkREjum2iGjO2xxAGlTk63m5N/AeUsxtXntgL+DmfJxnJU3P5TsC/wZMzedqILdMZOWf\nw+A8vx/w88jN9hHxVwBJNwDHSrqGNMjLZ9qJ12yjOXFb4UkaSkrSy0nPuV8APkh6lPTPsk2vB44l\nJdLPAkTETblJ92Dg15JOiYjpZfsQEUHqI/thSVNJd94TW6nK2vyzmfX/2xQwNSKOai+2iHgKuEzS\nFcAKSa2NQ30J8JOIuEvS3i3q9kp759hIkyLiglbKy88r4JMRsajFNgvau/ZtEPBYROzRxvoN/Rwg\nfZb/S/qe3BYdfB5v1hl+xm2Flu+gf05qAg5SM/hzkV6QOo50N1ZyLfBVgIh4PO8/FFgSEReTRiba\nucXxt5U0qqxoJPBMB6o4FTi17HibAw8BYyRtn8v6StqhldgOVr6lJDX7N5Oai/8ObFq26Wa8OeTh\n8bSt5X7lZgLH5PPuQLojbploO2sKcFopFkm75J/rvfakxwkT8vPpbYDSc/dFwEBJe+Tj9JS0Uzt1\nmAqcojxsqKT+ABHxLKmJ/zukJG7W7Zy4rYj65BeaHiONKHQPcG5e9zPgeElzSc9q37jzi4gXSENn\nlv8H/SlgvqQ5pObX61ucqydwgaSFeZsJwOkdqOsPgM3zS1FzgX0iYgXpefPNkuaRmoyHt7LvccCi\nfN4bSM+Dm0l3iB8ve2FsInCbpEeAF9dTlxnAiNZeTiNdtx6SHgVuBU6IiLX/coTO+T7pOs7Ln9n3\nc3l71/4O0qhRj+d1DwJExKukZ93n52s6B2jvTfQrSc/t5+V9ji5bdyPwl0gv2Jl1O48OZraBJG1C\nGiJwVESsrnZ97O1B0qVAU0RcVe26WDH4jttsAyh1lLIAuMRJ20pyK8XOwH9Xuy5WHL7jNjMzqyG+\n4zYzM6shTtxmZmY1xInbzMyshjhxm5mZ1RAnbjMzsxrixG1mZlZD/h9fmaBcmh9GtgAAAABJRU5E\nrkJggg==\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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buPX9BSxdv50eB9XllpPa0bJu1XiHJSJSqhRoWM/iQIm7eNmTvo8XvvyR+ycu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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i2mk6O8CYQIR",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "cellView": "form",
        "outputId": "4e39013a-4912-45e2-dc81-85819c0d9f64"
      },
      "source": [
        "#@title # Load Data. You must select approval_topline.csv from your computer once downloaded from https://projects.fivethirtyeight.com/trump-approval-data/approval_topline.csv\n",
        "import pandas as pd\n",
        "from google.colab import files\n",
        "uploaded = files.upload() # perhaps easier way to do this in google colab?\n",
        "import io\n",
        "\n",
        "# This will load in the data. You must select approval_topline.csv from your computer once downloaded\n",
        "# from https://projects.fivethirtyeight.com/trump-approval-ratings/\n",
        "approvals = pd.read_csv(io.StringIO(uploaded['approval_topline.csv'].decode('utf-8')))\n",
        "approvals.head() # inspect first few rows of data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-974b0a1e-4e7a-4192-8289-180e08aa1bb5\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-974b0a1e-4e7a-4192-8289-180e08aa1bb5\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving approval_topline.csv to approval_topline.csv\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>president</th>\n",
              "      <th>subgroup</th>\n",
              "      <th>modeldate</th>\n",
              "      <th>approve_estimate</th>\n",
              "      <th>approve_hi</th>\n",
              "      <th>approve_lo</th>\n",
              "      <th>disapprove_estimate</th>\n",
              "      <th>disapprove_hi</th>\n",
              "      <th>disapprove_lo</th>\n",
              "      <th>timestamp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Donald Trump</td>\n",
              "      <td>Voters</td>\n",
              "      <td>3/5/2020</td>\n",
              "      <td>43.439084</td>\n",
              "      <td>47.953360</td>\n",
              "      <td>38.924808</td>\n",
              "      <td>52.878107</td>\n",
              "      <td>57.361916</td>\n",
              "      <td>48.394299</td>\n",
              "      <td>16:49:45  5 Mar 2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Donald Trump</td>\n",
              "      <td>Adults</td>\n",
              "      <td>3/5/2020</td>\n",
              "      <td>41.950918</td>\n",
              "      <td>45.789781</td>\n",
              "      <td>38.112055</td>\n",
              "      <td>53.138018</td>\n",
              "      <td>57.339622</td>\n",
              "      <td>48.936413</td>\n",
              "      <td>16:48:21  5 Mar 2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Donald Trump</td>\n",
              "      <td>All polls</td>\n",
              "      <td>3/5/2020</td>\n",
              "      <td>42.853260</td>\n",
              "      <td>47.443586</td>\n",
              "      <td>38.262935</td>\n",
              "      <td>52.868294</td>\n",
              "      <td>57.188355</td>\n",
              "      <td>48.548233</td>\n",
              "      <td>16:47:26  5 Mar 2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Donald Trump</td>\n",
              "      <td>Adults</td>\n",
              "      <td>3/4/2020</td>\n",
              "      <td>41.869315</td>\n",
              "      <td>45.711976</td>\n",
              "      <td>38.026655</td>\n",
              "      <td>53.042271</td>\n",
              "      <td>57.324208</td>\n",
              "      <td>48.760334</td>\n",
              "      <td>15:20:20  4 Mar 2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Donald Trump</td>\n",
              "      <td>Voters</td>\n",
              "      <td>3/4/2020</td>\n",
              "      <td>43.830943</td>\n",
              "      <td>48.344926</td>\n",
              "      <td>39.316960</td>\n",
              "      <td>52.462662</td>\n",
              "      <td>57.102826</td>\n",
              "      <td>47.822498</td>\n",
              "      <td>15:21:45  4 Mar 2020</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      president   subgroup  ... disapprove_lo             timestamp\n",
              "0  Donald Trump     Voters  ...     48.394299  16:49:45  5 Mar 2020\n",
              "1  Donald Trump     Adults  ...     48.936413  16:48:21  5 Mar 2020\n",
              "2  Donald Trump  All polls  ...     48.548233  16:47:26  5 Mar 2020\n",
              "3  Donald Trump     Adults  ...     48.760334  15:20:20  4 Mar 2020\n",
              "4  Donald Trump     Voters  ...     47.822498  15:21:45  4 Mar 2020\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o16jqVPha9Fx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "cellView": "form",
        "outputId": "c63f6dd5-78e7-4f11-f0ba-e31cf0ea8f67"
      },
      "source": [
        "#@title Graph a regression of Donald Trumps approval rating (using all polls as data points)\n",
        "# convert the dates from strings to pandas DateTime objects\n",
        "approvals[\"modeldate\"] = pd.to_datetime(approvals[\"modeldate\"], infer_datetime_format=True)  \n",
        "\n",
        "# this line converts all the DateTime objects to TimeDeltas since we would like every date to be\n",
        "# the time elapsed since Trump first took office (the first poll date will suffice for the start of his presidency)\n",
        "approvals[\"modeldate\"] = (approvals[\"modeldate\"] - approvals[\"modeldate\"].iloc[-1])\n",
        "\n",
        "# now we will only use the \"days\" portion of each TimeDelta\n",
        "approvals[\"modeldate\"] = approvals[\"modeldate\"].map(lambda x: x.days)\n",
        "\n",
        "# our dates now look like this! They are of type int\n",
        "#approvals[\"modeldate\"].head(10)\n",
        "\n",
        "# Graph a regression of Donald Trumps approval rating (using all polls as data points)\n",
        "# Also plot the result of every poll\n",
        "dates = np.array(approvals[\"modeldate\"]).reshape(-1,1)\n",
        "\n",
        "plt.scatter(dates, approvals[\"approve_estimate\"])\n",
        "linear_regressor.fit(dates, approvals[\"approve_estimate\"])  # perform linear regression\n",
        "pred = linear_regressor.predict(dates)  # make predictions\n",
        "plt.plot(dates, pred, \"red\")\n",
        "\n",
        "plt.title(\"Donald Trump Approval Rating\")\n",
        "plt.xlabel(\"Days Since Start of Presidency\")\n",
        "plt.ylabel(\"Approval Rating as Percentage\")\n",
        "plt.gcf().set_size_inches(15, 9)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ecZQrUwCA0kQZ2/aOAwADOyIiIiIiSgynX7bI8IHjMwGdp1yZwvCB4x3aIyIiIiIi6kUM\n6lrk5EQ50u1ERERERERxMKhrkcX5XKTbiYiIiIiI4mBQ1yLrli+CBG7LZTMYWr+sI/tDRERERES9\niUFdCxRHS3jiSAnKd5sAuO3GApOkEBERERFRohjUtYAuSYoC8Mihl1EcLXVmp4iIiIiIqCcxqGsB\nUzKUKaWwbe84AzsiIiIiIkoM69S1wOJ8DiVDYOcva8DC5ERERERE1CyO1LXAuuWLrPd7hchLE2Uo\n3785gkdERERERFExqGuBZ58/Zb0/I8LC5ERERERElAgGdS1gKzCey2YwpZT2PhYmJyIiIiKiqBjU\ntYCpwHhGBPdtXIl8LhvpeURERERERCYM6lpgaP0y5LKZutty2Qw+cccNAIDzFy9pnxe2Fo+IiIiI\niCiIQV0LDA4UcN/GlSjkcxAAhXwO921cicGBAoYPHEdlSj/9cuRLrGNHRERERETRsKRBiwwOFLQl\nCmzr5irTCsMHjrO0AREREREROeNIXZuFrZtjshQiIiIiIoqCQV2bDa1fBrHcz2QpREREREQUBYO6\nNhscKOCuNUu092X7BEPrl7V5j4iIiIiIKM24pq5NthfH8cihl+tq1PUJMF37Zz6XxY4NK7iejoiI\niIiIImFQ1wbbi+N46OCJhtunVbXUgZcZk4iIiIiIKCpOv2yDRw69bLyvXJnC8IHjbdwbIiIiIiLq\nJRypS1hxtISdTx3DmckKAKA/21c35VKHGS+JiIiIiCguBnUJKo6WMPT40bri4pOV6dDnMeMlERER\nERHFxemXCRo+cLwuoHPBjJdERERERNQMBnUJijWN0la0joiIiIiIKASDugTFmUZZmVJMlEJERERE\nRLExqEvQ0PplyGaiD70xUQoREREREcXFRCkJ8mrNBbNfAvaEKUyUQkREREREcbU8qBORDIDDAEpK\nqXeLiADYBeA9AKYA/LFS6pOt3o8kBcsW5HNZ7NiwAoMDhZk/0/O27R1HuTI1c1sum2GiFCIiIiIi\niq0dI3W/CeBrAK6s/fsDAK4BsFwpNS0ir2/DPiRGV7ZgolzB0GNHAcAY0PnvGz5wHCcnylicz2Fo\n/TLrc4iIiIiIiGxaGtSJyJsA3ALg9wF8uHbzfwTwXqXUNAAopV5p5T4kzVS2oDJdTXgSFqDZRvKI\niIiIiIiianWilAcB/BYA/4Ky6wFsEpHDIvJZEfkx3RNF5EO1xxw+depUi3fTnS2pCROeEBERERFR\nu7UsqBORdwN4RSl1JHDXPACvKqVWA/gUgD/XPV8p9SdKqdVKqdWLFi1q1W5GZktqsjifQ3G0hLW7\nn8F1W/dj7e5nUBwttXHviIiIiIhotmnlSN1aABtE5EUA/xvAzSLyEIBvAdhbe8yTAN7awn1InKls\nQbZPsPTqHLaMjKE0UYYCUJooY9vecQZ2RERERETUMi0L6pRS25RSb1JKLQXwSwCeUUrdDaAIYF3t\nYT8D4J9btQ+tMDhQwPDtN2BBf3bmtnwui01vuwZfeOE0gqvtypUpFhcnIiIiIqKW6USdut0A9ojI\nFgDnAPxqB/ahKbpkJ2t3P9MQ0Hm41o6IiIiIqDOKo6Wezz7flqBOKfV5AJ+v/f8Eqhkxe4otcGNx\ncSIiIiKi9gvWifaWRwH2UmRp04mRup60OJ9DSRPYCZBocXFb4XMiIiIiIrps+MDxmYDO4y2P6qX2\nc6tLGswaQ+uXIZfN1N0mAO5asySxA8YrfO4FdMDlwudMxkJEREREVM80m67XlkcxqEvI4EAB921c\niUI+BwFQyOfwwKZV2DW4MrHXCCt8TkREREREl5mWQfXa8ihOv0yQLoFKM4qjJXzkyXGcvzgV+the\n620gIiIiImrWuuWLsOfgibqEhrlsJtHlUd2AQV1Mrc6iUxwt4Z7HjmJq2pRTs16v9TYQERERETWj\nOFrCE0dKdQGdALjtxmQHYroBg7oYksyiYwoOhw8cdw7ogGovBBERERERVemSpCgAzz5/qjM71EJc\nUxeDLYtOFF7ik9JEGQrV4HDo8WrSk6jTKZ84UmKyFCIiIiKimtmSJAVgUBdLUgfIzqeONSQ+qUwp\n7HzqWOTplHGCSiIiIiKiXjVbkqQADOpiuSqX1d4e9QDxlyYI3j60fhkyfRJpe73Y60BEREREFIeu\n5FgvJkkBuKYusuJoCecvXmq4vQ/JFhn31ua5Zr8EgHy/PtgkIiIiIpptvPZ0K5MbdgsGdRGZasVN\nOzw3mBQll+1DudL4zHxtJNArkbB29zMoOYzCKfe8KkREREREPS/pkmPditMvI7JNcbStafMyZvqT\nolyaVg1fQLZPsGPDCufX9Dtb1k/nJCIiIiKi3sWgLiLburmwgC+YMbMypXBVfxaFfA4CoJDPYfg9\nNzT0JuSybl9TLy76JCIiIiIiO06/jGho/TJsGRmDbqZjnIBvYrKC0Y++0/i84mgJk5opmkG9uuiT\niIiIiIjsOFIX0eBAAXetWYJgXsqwoCpuSlWXMgWFfA73bVw5K+YLExERERFRPQZ1MewaXIkHNq2q\nmzYZFlTFTakatp5OADy39WYGdEREREREsxSnX8YUNZNO1JSqXqbMsISWXEdHRERERDS7MahrI9dA\n0MuUGUysEsR1dEREREREdsGyYr1Yq45BXRfSZcr0ZEQwpRQKPXpAEhERERElJThYUpooY9vecQDo\nqXY0g7ouZCs0/sJ972rjnhARERERpZdusKRcmcLwgeM9FdQxUUoXykgwt6b9diIiIiIiamRKOhiW\njDBtGNR1oSmlT49iup2IiIiIiBrFLSuWNgzqulDBcJCZbiciIiIiokZxy4qlDYO6hBRHS1i7+xlc\nt3U/1u5+BsXRUuxtzZaDj4iIiIiolQYHCrhv48pI9aXTiIlSYiiOlrDzqWM4M1kBAPRn+1CZUqhM\nV6dHNptVJ2pNOyIiIiIi0otaXzqNRKVgndbq1avV4cOHO70bAKoB3dDjR1GZCv/cCvkcntt6cxv2\nioiIiIiIepmIHFFKrdbdx+mXEQ0fOO4U0AG9l1WHiIiIiIi6D4O6iKIEar2WVYeIiIiIiLoP19RF\ntDifsxYH90RNbBJcpwcA+VwWOzas6Pk5wEREREREJsXREnNNhGBQF9HQ+mXaNXV9Alx5RRZny5XI\nB1txtIQPPzqG6cCszolyBUOPHQUQL+EKEREREVErtCPQKo6W8JEnx3H+4tTMbaWJMtvHGpx+GdHg\nQAHDt9+ABf3ZmdvyuSzuv2MVdmxYgXx/FqWJMjaPjGHVzqedShvs2HesIaDzVKYVhg8cT2r3iYiI\niIiaUhwtYdvecZQmylC4nPm9mZJeutcYevxoXUDnqUwr7Nh3LLHX6gUcqYtBlxZVlxXTdaRtolwx\n3gc0ruPjEDQRERERdcrwgeMoV+qDrXJlCsMHjifWJg1LThjWfp5tOFKXgOJoCfc8qi9zkMRIW943\nKtiOnhEiIiIiIhNT4sAkM7+7bIvt38sY1DXJC7KmLPX+wg5K/1ROHf+mbT0jREREREStZsrwnmTm\nd5dtsf17GYO6JumCrKCwg/LeW1cgmxHj/f7hZVPmTdbEIyIiIqJ2GFq/DLlspu62qJnfXV7D1j4G\nwtu/xdES1u5+Btdt3Y+1u5/p6ZE9BnVNcgmmShNl64HkJV/JiP7AFVQPyuJoCaZDWwE9f7ASERER\nUecNDhRw38aVKORzEACFfA73bVyZaI4HXXLCINvAyWxbsiTKMm2wW6xevVodPny407vRwFtLZ5t6\n6ZfLZqwHfHG0hC0jY9BtrVA7aMNq5AmqAV6BCVSIiIiIqAd4AZp/dlxYu3rt7me07eZCPofntt7c\nsn1tJRE5opRarbuP2S9jcllLF+Rf+6bLXjk4UMDmkTHtc12nV3p74/VGAKzhQUREyWIWZiICgO3F\ncTxy6GVMKYWMCO686RrsGlyZ+Ot455co5x1T2zlsgCStGNTF5LKWTscLtrznBoOvQj6nPdgWO47U\n+SWdWpaIiCjYY85ORKLZaXtxHA8dPDHz7ymlZv7dqsAuyjlmsaFN7S1r6rXzFdfUxRQ2cmZa+5YR\nsWavtC08HVq/DNk++4LRqPtJREQUBbMwExEAPHLo5Ui3t9vQ+mXa9rhCb2bNZFAXk21hZiGfw11r\nljQEZwIYp2t6wVfowtNoMV2iqWWJiIjaUZ+KiLqfqU0bZWlSKw0OFLR5KoDePF9x+mVMQ+uXOS3Y\n3HPwxMwBZTvE/QXGTcPLwweOawuch+0nEZGr4mgJO586hjOT1VIq+VwWOzas6LlpKhSfaUpTPqTm\nKhH1loyINoAzZXNvh+B633wuW1cazNOLgx4cqYspbETNm2fsGoKde/VSaIrVOL0KbIgRkaviaAlD\njx+dCeiAap3MoceO9mwKaIrOVDvK5TpGRL2hOFrCFVl9GHHnTde0eW+qdCUMzl+81LB0Kel6et2C\nQV0TBgcKWLd8EfpEUJoo455Hj2J7cbxh4aiLyrQKnd/bi70KRNQ9TLMBKtMKO586NquKuJLZ4EAB\n8+c2TvTxjhMi6m1e8HT+oj5h4J6DJ7C9ON7mvdKv961MKbzmijktrafXLTj9sgl3feqLeO6F0zP/\n9mf9iSNsJE435TPM0q37IQDuWrOkJZmIiKh32M5BZyarI3aV6WrQx4yHs9tZzXQmoHqc9GJWOSK6\nLCwDvAJamgXTxHQNm5isYPSj72zbfnQKg7qYiqOluoAuCbaROH8dkKha+eNirSKi3mFaK+XxAjoP\ny6bMXrZj5Xf2fpXHBFEPc10O9Mihl1vS7jSt+zadlxbnc7OivSqqSzLU2KxevVodPny407tRx1Sl\nvlkFzYEWZzqnTkYEL9z3rqa34wnWKvJwZJAonbw1dVETMhXyuZ6+UFKj4mgJm0fGjPffzWsAUc8G\nElHawC/uviWx17Vdo37s9fPx9VfON+SyEAA/df1CfOXE2dDkhmkgIkeUUqt193FNXUytSoXqTWny\nr1VxqfchAB7ctMpa8SDpFLOm4XdvZLAT86mJKL7BgQKGb78BURKXCVC3KD14/qLOatU6yLCG0J6D\nJ3gc0KymS9rRK+dHXU1lnaSzYNqywP+LJqADqm3SL7xwelbU1mRQF1Mrk5YEDzSXYMwrpBi2X3FP\nJrqGQVhg2y3FJ4nI3eBAAXfdtER7X/CCIWgs1dKLF8q0SrJRWRwtYeBjT2Pp1v0zfza9WtyXyJWu\n47tXzo/+DPA2SWfBjDtDbrbUqmNQF5NrL0Vc/gPNtafj5EQZQ+uXIdNnfnyck4mpYZAzpLL1dEvx\nSSKK5tnnT2lvv6o/W5dBbLZcKNMqqUalrtSFCx4HNJuZjv9e+V0MDhTw3Nab8eLuW/DgplWYP/dy\nm1jQminYSY/89Yn0VDZnJkqJyZt6MnzgOEoTZW2PdTP8I2533nSN05q6xfnczH6Z1jrEOZmYGgYu\nmAWNKH1cM4iZ1lWw/Ep3SKpRaZvyZMPjgGYzW9KOXjM4UGhLW6+ZwQJdO93bXmmijM0jY9ix79hM\n0pU04khdE7xeigc3rUK+P5vYdoNFEV16OvzPGRwoGIfE45xMmulV6pX540Sziek8EbxdN2OhV4u6\nppHr9xgmzjWAxwHNdjw/Ji9suqfNXWuWzMw0MY34TZQrqW63Mqhr0vbiOLaMjEWaliIA1l6/cObg\nWtCfRT6XtRZFDDuQvedsL47j+m2f0fYOxT2ZNNOr1Cvzx4lmE9fGiH9dRa8XdU2jofXLkM3UN16y\nGYl8HXC9BmREeBwQ1fT6+bFVSZhs4i59WtCfxa7BlXhu68345u5bMG0Z8Utzu5XTL5tQHC1hz8ET\nTtMu87kszpYrdSltg6luTUO+xdESJi9eMm7b63GwlT7QlUpwFafouV+vzB8nmi3808uDqbh1Kbqf\n23pzh/eYjIIXqBizl4bWLwstdZHtEwy/54aeabASJaFd0xLbLVjSysu1AIRnxm2Gt21/nbowuWwG\n9966AkB1vz/y5HjoaTCt7VbWqWuCS50OU802XY03Xc0MUy24oFw2g1crU9oDNW59Ou/gP38xXjDn\nKeRzbPQRpViw2KtfWmv9zAama1RGBJ+4I1oAZjsG8rks3n3DG/Hs86d6rh4XETUynVva2d7bXhwP\nHVgp5HNYt3wRnn3+VKTMmd3cbrXVqeNIXRPCInnbhdOWlcz/eFMtuCDbY+IsLC2OlnDPY0cxNd1c\n0C8A548TpVhYQXLdeYu6g61hUEcAACAASURBVOkaNaVU5F5124hDp3rtqbf0aqHuXtQNmT2fff6U\nNaBb0J/F5MVLTokGg9YtXxR/xzoodE2diLxBRP5MRD5b+/dbRORXXF9ARDIiMioinw7c/kkRORd9\nl7tH2DqDaaWMJyTXH0QSP5A4KWB37DuWSEB315olPCkTpZhL5sO4tYOota7KmRN4BdeN6OrQrdr5\ntNM6mV6ux0XtURwtYeixo3Wlk4YeO5rahBW9LqkkTM0Iax+fe/VS5DIsnpEvvZzKY88lUcpfAjgA\nYHHt3/8MYHOE1/hNAF/z3yAiqwEsiLCNrhS2YNN2cLv+IKL8QPw1QvyiFn8sjpYwUY73Q/B7YNOq\nxGuUEFF7uXYsbS+Ot3hPKIrtxfHQ87gXjG8vjmOzJuHXRLni1LDuhl57Srcd+46hEuhIrkwr7Nh3\nrEN7RDbdkNkzrH0cPJ6iqEyrVHZKuQR1r1NKPQpgGgCUUpcAOC2yEpE3AbgFwJ/6bssAGAbwW5H3\ntst4mY0WaMoZhB3crj+IdcsXwWWcLZfN4Pd/cSXuXrNkZmQuIxKr+GMSB3LBVzOPiNLLtWPpoYMn\nGNh1CS+Jl4u3/O5nrdOTXBo33dBrT+lm6oBIooOZktfOzJ6mLJtD65c5tY/jSmOnlMuauvMicjVq\nObNEZA2As47bfxDV4O21vtv+M4B9Sqlvi2VaoIh8CMCHAGDJkiWOL9d+3jqDqHPBbdnlPLZsln75\nXHYmc+bgQKHp0bEoB3KfVINHf48I67AQ9YawzLtBjxx6maPzHbS9OI5HDr0caR31ZGU69DFh1wRd\nhmReB4h6Wzsye4at1z380umGZCm2xIFRpLFTyiWo+zCAfQCuF5HnACwCcHvYk0Tk3QBeUUodEZF3\n1G5bDOA9AN4R9nyl1J8A+BOgmv3SYT87IhjMPbBpVWILz10Xd86fNyfRH9bifM55jcy0Aq7KzUH/\n3Dlc3EzUQ1wz7/rFScpEyXDtBIwjrHHj76QsTZSREalbU8frAYVZ0J/Vrn/SzYSi2SMsqeCuwZVY\nfe3Cusy88+b0xS7B5cn2Ra/n2Q1Cgzql1FdE5GcALEM198VxpZTLePhaABtE5F0ArgBwJYBjAC4A\n+HptlK5fRL6ulHpz3DfQSUlk/DKN8EWZApn0ELGp19X0I5mYrGD0o+9MdB+IqLNcM+/6ZUQaUt/7\nZxKkUTdl5LPtyyOHXm7Ja7o2brz9YBZMiuPeW1c0ZNnNZmSmvhjNPsXRknGAIdjufdU346DZKbuC\n+mnnaTp3hQZ1IrIxcNOPi8hZAONKqVdMz1NKbQOwrbaNdwD4r0qpdwe2fS6tAR3gXpbAxBYURpsC\nWW1IJXXgmaaGer2wQWkcoiYiuzidRT+6qL+hYeYl2wDSdXEE2p+q3xa0he1LK0ZJc9k+3Lfxrc7v\ntdlrIs1eLktSaPbwzncm/nZnnA5IXWdjL5RmcZl++SsA3g7g2dq/3wHgCIDrRORjSqm/adG+dT1T\no6c0UcbSrftDe6htF8AoUyDj1BwKY5oaynUTRLND2DkoI0Cw0sG/vHJe+1iv1zMtF0ZPO4MUL6W7\ntz7ZS+kOYGb2hm1fMiKJBXZxR1eZBZOa0Y41Wu3WTSP9rdCq92cL1ILtzjgldXTLlnqhU8ol++Uc\nAP9KKXWbUuo2AG9BNWnKTQB+2+VFlFKfD47S1W5/TZSd7TZhI1Rh6aBtF0BT4UOp/QW1oyZQO7Md\nEVFn2Tpr8rksfuSqaCP0aWzYtzNICUvpbutELI6WIpeuCSrkc3hw0yo8uGkV5s+bgy0jY3WZ5lww\nCybRZd7Ij7/23ra946msf6bTyvdnO8f6253F0VKsDJi67btO9exmLkHdNUqp7/r+/UrtttMAZnWu\n2bA6dYA9HbTpQpfvz2Lky43rI3LZPjywaZXxtdpRAHhwoIDntt6Mb+6+Bc9tvZkBHVGPGhwooD+r\nv0SIRL/QpbFhbztHJy0spbvt89u2dxyrr12Iu9csQZ+mhZPpE+QthcgFwHNbb57ZVtxGWjfUrqLe\nZ0px321sIz+9oJXvz3S+C5bLGj5wPFaWy+D2bcdQmq5dLkHd50Xk0yLyfhF5P4C/rd02H8BEa3ev\nu/lHrmxMjR/TBfBCZapuTYrn1UvT2DIyhj5DKQiB/cAkIgrjbzCZ0t1PTFYiX+jS2LAfWr8M2Uzj\n+fbsZKXt51rTvgCXG1K7Blfiyisag7epaYX58+YYr1Xed9lsI42zOahZYQFbmka/en06civfn2sH\nUZzX0m3HVuQ+Tdcul6Du1wH8JYBVtb+/BvDrSqnzSql1Ldy3VBgcKIQWQDQ1fgYHCrjtxkJdsfDb\nbiwYG1JKVee9mtZNKCRTOJyIZqdgg8nEWzsRNlPBs6A/m9qG/SVNB9s07I2AOGyp270G66Vp87dy\nsjYN0zTid3KiHNpQsk3xHPjY01i6dT+Wbt2PVTufNjaiOZuD4tIFbFtGxrC9eDlhRppGv3p9OnIr\n359rB5HLa2Uz1ZkKtu3YMmam6RzmUtJAAXi89kcatuFfWzro4mgJTxwpzQRpU0rhiSPN9Tb1Sg8Q\nEbWfSxYxLwgI1iYzyfalMy2518A0ndubTZsddO+tK7B5ZEx7386njuHVyjRseVAW53PWhu1i37Ql\nU2IDW3Icfw0xU0bTXk8KQa2lO/8oAHsOnsDqaxdicKCQqtEvU3moNI382ER9f8FyN4A9KVNY4pzi\naAmTFy813J7pE0xPKyhUB0s2/eQ12DW4MsI7S6/QkToRWSMiXxaRcyJyUUSmROQH7di5tLCdTIbf\nc0Pk7JeG2ZVOWrHWg4hmB9u5TNfL6c1UME3ry+ey1nNgN4uTJrsZts/ozGTFui+CagPL9v15DS1v\nJM1bn+1PiKIbyTNdjoLrxV2nxaVlPRS1n+n49c9CStPoV69PR47y/oqjJQw9frShwHwwoaDr+cE7\n3wS315/tQx8w0xnnDZaEnWdMMyVsMyi6kUtJg/8J4JcAPAZgNYD3AfjxVu5U2ph6N4MLOoOMJzBV\n7d0OZkJzce7VS4nWrCOi2cN2LvMSafgF6/r45bKZ1BYdtxW99dteHE+0B7gQoZSN311rlsyUPdA9\nPzj9VVePafPIGPK5LG67sYBnnz+FkxNl5PuzDY0mP/81zNRJuXlkDMMHjs8ElWmvA0WtYxsp9o61\ntI1+9WKZBj/X9zd84Lg2VwRQ30Hken4wdbpduKQalii5lCW499YVDTVWs5n0zTJxWVMHpdTXAWSU\nUlNKqb8A8POt3a10iZvxy5bdZ/g9N8z0fizozzZkNMtmBDlNZjpbtk0iIpt1yxc1jMzYzmW20ax2\nr3NJagTI61F28dDBE3XrfZoVZZ2iZ0F/diawNF2Lgg0T0/c2Ua7giSPVEbsHNq3CuQuNU5v8/Ncw\n2yih1zjbse9YatZDUfvZ8hN4x1paRr84Il0vbHrsyYlypPWSpu2Zck6Evf7gQAHDt99Qd1wN356+\nWSYuI3WTIjIXwJiI/AGAb8M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vQzfqGuwoSAOXoO7XAfwJgOUiUgLwTQB3t3SvUiKp4X3T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\nRA6hipJnmxnSfUZFaXJzLBHcC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bbX/3ryvVm8+lq64AnAvJEMAIylhwon02STEbA5MQ2z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\nDgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pu0uaKDyfp7M",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "cellView": "form",
        "outputId": "aeb43219-53ae-44fc-8d75-b13ac861d1e8"
      },
      "source": [
        "#@title Show OLS Results\n",
        "# Redo the above but using a different package to perfrom regression\n",
        "# this package will give us a nice output similar to what's given in R\n",
        "\n",
        "from statsmodels.formula.api import ols\n",
        "\n",
        "mod = ols(formula = \"approve_estimate ~ modeldate\", data=approvals)\n",
        "res = mod.fit()\n",
        "res.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>    <td>approve_estimate</td> <th>  R-squared:         </th> <td>   0.115</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.115</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   444.0</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Fri, 06 Mar 2020</td> <th>  Prob (F-statistic):</th> <td>9.35e-93</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>04:29:26</td>     <th>  Log-Likelihood:    </th> <td> -6999.3</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td>  3414</td>      <th>  AIC:               </th> <td>1.400e+04</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td>  3412</td>      <th>  BIC:               </th> <td>1.401e+04</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>    \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Intercept</th> <td>   39.9932</td> <td>    0.064</td> <td>  621.736</td> <td> 0.000</td> <td>   39.867</td> <td>   40.119</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>modeldate</th> <td>    0.0021</td> <td>  9.8e-05</td> <td>   21.071</td> <td> 0.000</td> <td>    0.002</td> <td>    0.002</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td>93.747</td> <th>  Durbin-Watson:     </th> <td>   0.813</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 138.384</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>          <td> 0.282</td> <th>  Prob(JB):          </th> <td>8.92e-31</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>      <td> 3.810</td> <th>  Cond. No.          </th> <td>1.31e+03</td>\n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.31e+03. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            OLS Regression Results                            \n",
              "==============================================================================\n",
              "Dep. Variable:       approve_estimate   R-squared:                       0.115\n",
              "Model:                            OLS   Adj. R-squared:                  0.115\n",
              "Method:                 Least Squares   F-statistic:                     444.0\n",
              "Date:                Fri, 06 Mar 2020   Prob (F-statistic):           9.35e-93\n",
              "Time:                        04:29:26   Log-Likelihood:                -6999.3\n",
              "No. Observations:                3414   AIC:                         1.400e+04\n",
              "Df Residuals:                    3412   BIC:                         1.401e+04\n",
              "Df Model:                           1                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "==============================================================================\n",
              "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------\n",
              "Intercept     39.9932      0.064    621.736      0.000      39.867      40.119\n",
              "modeldate      0.0021    9.8e-05     21.071      0.000       0.002       0.002\n",
              "==============================================================================\n",
              "Omnibus:                       93.747   Durbin-Watson:                   0.813\n",
              "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              138.384\n",
              "Skew:                           0.282   Prob(JB):                     8.92e-31\n",
              "Kurtosis:                       3.810   Cond. No.                     1.31e+03\n",
              "==============================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "[2] The condition number is large, 1.31e+03. This might indicate that there are\n",
              "strong multicollinearity or other numerical problems.\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OZCr_ySKmK4g"
      },
      "source": [
        "# We see that the intercept is nearls 40 and there is a slight positive slope (0.0021).\n",
        "# The slope has a standard error of 9.85e-05 and it's p-value is extremely small, which indicates\n",
        "# that the probability of observing this positve slope given that the true slope is 0 (null hypothesis)\n",
        "# is very unlikely.\n",
        "\n",
        "# However, we also see from the data that a linear model may not be the best fit for this data! It seems\n",
        "# Trump's approval rating sank throughout the first 300 days of his presidency but now it is on a slow rise."
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KwPENhKGfucP"
      },
      "source": [
        "## DISCUSSION 11\n",
        "- What assumptions are made under this analysis? \n",
        "- What assumpotions would you challenge? \n",
        "- What would you do differently?"
      ]
    }
  ]
}