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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/nurfnick/Operations_Research/blob/main/RegressionAssignment.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "source": [
        "# Regression Assignment"
      ],
      "metadata": {
        "id": "JO49YYNwKnFu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "I want you to practice some regression and analyzing your results.  Utilize this document to load data directly."
      ],
      "metadata": {
        "id": "66HuET7aKqij"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pa\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "IxhQv2AxbU8W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Linear Regression"
      ],
      "metadata": {
        "id": "W8z63sF3Pm3A"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Fit goals vs points in a linear regression.  Fit a multiple regression on goals and assists vs points.  What do you notice about these two fits.\n",
        "https://raw.githubusercontent.com/nurfnick/Data_Sets_For_Stats/master/CuratedDataSets/hockey.csv"
      ],
      "metadata": {
        "id": "GiorZQ2JPpDN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pa.read_csv('https://raw.githubusercontent.com/nurfnick/Data_Sets_For_Stats/master/CuratedDataSets/hockey.csv')"
      ],
      "metadata": {
        "id": "QeoRvs95bY-A"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = np.array(df[['G','A']])\n",
        "y = np.array(df.PTS)"
      ],
      "metadata": {
        "id": "GSe2QrnJboE3"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Logistic Regression"
      ],
      "metadata": {
        "id": "o3PcNxSeNKqb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Using the 538 Avengers dataset,https://github.com/fivethirtyeight/data/blob/master/avengers/avengers.csv, fit a logistic regression to predict `Death1` (first time a character might die) based on whatever variables you find interesting.  Make a prediction using probabilities on your favorite character."
      ],
      "metadata": {
        "id": "Kzyw0iBWOUkn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pa.read_csv('https://raw.githubusercontent.com/nurfnick/Data_Sets_For_Stats/master/CuratedDataSets/Avengers')\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
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          "height": 794
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        "id": "jmz1qG3BNM2b",
        "outputId": "c6e5c32d-09a9-456a-8f9d-be976fef974e"
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      "execution_count": 22,
      "outputs": [
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          "output_type": "execute_result",
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              "                                                 URL  \\\n",
              "0      http://marvel.wikia.com/Henry_Pym_(Earth-616)   \n",
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              "2  http://marvel.wikia.com/Anthony_Stark_(Earth-616)   \n",
              "3  http://marvel.wikia.com/Robert_Bruce_Banner_(E...   \n",
              "4   http://marvel.wikia.com/Thor_Odinson_(Earth-616)   \n",
              "\n",
              "                    Name/Alias  Appearances Current?  Gender  \\\n",
              "0    Henry Jonathan \"Hank\" Pym         1269      YES    MALE   \n",
              "1               Janet van Dyne         1165      YES  FEMALE   \n",
              "2  Anthony Edward \"Tony\" Stark         3068      YES    MALE   \n",
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              "  Probationary Introl Full/Reserve Avengers Intro  Year  Years since joining  \\\n",
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              "4                 NaN                      Sep-63  1963                   52   \n",
              "\n",
              "  Honorary  ... Return1 Death2 Return2 Death3 Return3 Death4 Return4 Death5  \\\n",
              "0     Full  ...      NO    NaN     NaN    NaN     NaN    NaN     NaN    NaN   \n",
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              "2     Full  ...     YES    NaN     NaN    NaN     NaN    NaN     NaN    NaN   \n",
              "3     Full  ...     YES    NaN     NaN    NaN     NaN    NaN     NaN    NaN   \n",
              "4     Full  ...     YES    YES      NO    NaN     NaN    NaN     NaN    NaN   \n",
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              "  Return5                                              Notes  \n",
              "0     NaN  Merged with Ultron in Rage of Ultron Vol. 1. A...  \n",
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              "3     NaN  Dies in Ghosts of the Future arc. However \"he ...  \n",
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              "      <th>URL</th>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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          },
          "metadata": {},
          "execution_count": 22
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    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(df['Appearances'],df['Death1'])"
      ],
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          "height": 447
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        "id": "NuIiz-WyNdDM",
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      "execution_count": 23,
      "outputs": [
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          "execution_count": 23
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            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Find Your Own Regression\n",
        "\n",
        "I have compiled olympic 100m dash records from a wikipedia page https://en.wikipedia.org/wiki/100_metres_at_the_Olympics.  Use this data and fit a regression of some type for prediciting `Time`.  Justify your model used in words and pictures. Predict the new Olympic Record for 2024 and 2300.  Describe in words the validity of your predictions."
      ],
      "metadata": {
        "id": "tFoMKiy6K1Nu"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "e8UxLzr5Kmda",
        "outputId": "d7b7c9bd-5c9e-4580-931f-3fb2a81cc7ba"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Time           Athlete               Nation  Games   Round        Date  \\\n",
              "0  12.2      Francis Lane  United States (USA)   1896  Heat 1  1896-04-06   \n",
              "1  12.2     Thomas Curtis  United States (USA)   1896  Heat 2  1896-04-06   \n",
              "2  11.8         Tom Burke  United States (USA)   1896  Heat 3  1896-04-06   \n",
              "3  11.4     Arthur Duffey  United States (USA)   1900  Heat 1   6/14/1900   \n",
              "4  11.4  Walter Tewksbury  United States (USA)   1900  Heat 2   6/14/1900   \n",
              "\n",
              "  Gender  \n",
              "0    Men  \n",
              "1    Men  \n",
              "2    Men  \n",
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          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "\n",
        "\n",
        "\n",
        "df = pa.read_csv('https://raw.githubusercontent.com/nurfnick/Data_Sets_For_Stats/master/CuratedDataSets/100mOlympicRecords.csv')\n",
        "x = np.array(df)\n",
        "\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8XYxaGxpLVWs"
      },
      "execution_count": 6,
      "outputs": []
    }
  ]
}