mattbullen · October 16, 2025 11:42
diff --git a/unit03-ex2-linear_regression.ipynb b/unit03-ex2-linear_regression.ipynb
 {
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/mattbullen/5a29124e29c28fcfbd3850f670c65ae7/unit03-ex2-linear_regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wglk35RZjXoq",
        "outputId": "b7d8b2e1-e64b-4bdf-d168-c2c165746949"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Pearsons correlation: -0.759\n"
          ]
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from scipy import stats\n",
        "\n",
        "#Create the arrays that represent the values of the x and y axis\n",
        "x = [5,7,8,7,2,17,2,9,4,11,12,9,6]\n",
        "y = [99,86,87,88,111,86,103,87,94,78,77,85,86]\n",
        "\n",
        "#Execute a method that returns some important key values of Linear Regression\n",
        "slope, intercept, r, p, std_err = stat.linregress(x, y)\n",
        "\n",
        "# measure the correlation\n",
        "corr, _ = stat.pearsonr(x, y)\n",
        "print('Pearsons correlation: %.3f' % corr)\n",
        "\n",
        "#Create a function that uses the slope and intercept values to return a new value.\n",
        "#This new value represents where on the y-axis the corresponding x value will be placed\n",
        "def myfunc(x):\n",
        "  return slope * x + intercept\n",
        "\n",
        "#Run each value of the x array through the function. This will result in a new array with new values for the y-axis\n",
        "mymodel = list(map(myfunc, x))\n",
        "\n",
        "#Draw the original scatter plot & the line of linear regression\n",
        "plt.scatter(x, y)\n",
        "plt.plot(x, mymodel)\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sBGaxAlgjXos"
      },
      "source": [
        "## Predict Future Values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aNeGy6pBjXos",
        "outputId": "7832a556-aeed-4e2a-8b11-f12e1593cb90"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "85.59308314937454\n"
          ]
        }
      ],
      "source": [
        "from scipy import stats\n",
        "\n",
        "x = [5,7,8,7,2,17,2,9,4,11,12,9,6]\n",
        "y = [99,86,87,88,111,86,103,87,94,78,77,85,86]\n",
        "\n",
        "slope, intercept, r, p, std_err = stats.linregress(x, y)\n",
        "\n",
        "def myfunc(x):\n",
        "  return intercept + slope * x\n",
        "\n",
        "speed = myfunc(10)\n",
        "\n",
        "print(speed)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sMezXAcFjXot"
      },
      "source": [
        "If x=10 then predicted y is 85.59"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0NIikYDRjXot"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.6"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
 }
	{
	"cells": [
	{
	"cell_type": "markdown",
	"metadata": {
	"id": "view-in-github",
	"colab_type": "text"
	},
	"source": [
	"<a href=\"https://colab.research.google.com/gist/mattbullen/5a29124e29c28fcfbd3850f670c65ae7/unit03-ex2-linear_regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {
	"id": "wglk35RZjXoq",
	"outputId": "b7d8b2e1-e64b-4bdf-d168-c2c165746949"
	},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"Pearsons correlation: -0.759\n"
	]
	},
	{
	"data": {
	"image/png": 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\n",
	"text/plain": [
	"<Figure size 432x288 with 1 Axes>"
	]
	},
	"metadata": {
	"needs_background": "light"
	},
	"output_type": "display_data"
	}
	],
	"source": [
	"import matplotlib.pyplot as plt\n",
	"from scipy import stats\n",
	"\n",
	"#Create the arrays that represent the values of the x and y axis\n",
	"x = [5,7,8,7,2,17,2,9,4,11,12,9,6]\n",
	"y = [99,86,87,88,111,86,103,87,94,78,77,85,86]\n",
	"\n",
	"#Execute a method that returns some important key values of Linear Regression\n",
	"slope, intercept, r, p, std_err = stat.linregress(x, y)\n",
	"\n",
	"# measure the correlation\n",
	"corr, _ = stat.pearsonr(x, y)\n",
	"print('Pearsons correlation: %.3f' % corr)\n",
	"\n",
	"#Create a function that uses the slope and intercept values to return a new value.\n",
	"#This new value represents where on the y-axis the corresponding x value will be placed\n",
	"def myfunc(x):\n",
	" return slope * x + intercept\n",
	"\n",
	"#Run each value of the x array through the function. This will result in a new array with new values for the y-axis\n",
	"mymodel = list(map(myfunc, x))\n",
	"\n",
	"#Draw the original scatter plot & the line of linear regression\n",
	"plt.scatter(x, y)\n",
	"plt.plot(x, mymodel)\n",
	"plt.show()\n"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {
	"id": "sBGaxAlgjXos"
	},
	"source": [
	"## Predict Future Values"
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {
	"id": "aNeGy6pBjXos",
	"outputId": "7832a556-aeed-4e2a-8b11-f12e1593cb90"
	},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"85.59308314937454\n"
	]
	}
	],
	"source": [
	"from scipy import stats\n",
	"\n",
	"x = [5,7,8,7,2,17,2,9,4,11,12,9,6]\n",
	"y = [99,86,87,88,111,86,103,87,94,78,77,85,86]\n",
	"\n",
	"slope, intercept, r, p, std_err = stats.linregress(x, y)\n",
	"\n",
	"def myfunc(x):\n",
	" return intercept + slope * x\n",
	"\n",
	"speed = myfunc(10)\n",
	"\n",
	"print(speed)"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {
	"id": "sMezXAcFjXot"
	},
	"source": [
	"If x=10 then predicted y is 85.59"
	]
	},
	{
	"cell_type": "code",
	"execution_count": null,
	"metadata": {
	"id": "0NIikYDRjXot"
	},
	"outputs": [],
	"source": []
	}
	],
	"metadata": {
	"kernelspec": {
	"display_name": "Python 3",
	"language": "python",
	"name": "python3"
	},
	"language_info": {
	"codemirror_mode": {
	"name": "ipython",
	"version": 3
	},
	"file_extension": ".py",
	"mimetype": "text/x-python",
	"name": "python",
	"nbconvert_exporter": "python",
	"pygments_lexer": "ipython3",
	"version": "3.7.6"
	},
	"colab": {
	"provenance": [],
	"include_colab_link": true
	}
	},
	"nbformat": 4,
	"nbformat_minor": 0
	}
No results found