Correlation_Coefficient.ipynb

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      "name": "python"
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      "cell_type": "markdown",
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        "<a href=\"https://colab.research.google.com/github/DeepthiTabithaBennet/Python_AppliedStatistics/blob/main/Correlation_Coefficient.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Written by Deepthi Tabitha Bennet\n",
        "\n",
        "import math "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
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      "source": [
        "# function that returns correlation coefficient.\n",
        "def correlationCoefficient(X, Y, n) :\n",
        "  sum_X = 0\n",
        "  sum_Y = 0\n",
        "  sum_XY = 0\n",
        "  squareSum_X = 0\n",
        "  squareSum_Y = 0\n",
        "\n",
        "  i = 0\n",
        "  while i < n :\n",
        "    # sum of elements of array X.\n",
        "    sum_X = sum_X + X[i]\n",
        "\t\t\n",
        "    # sum of elements of array Y.\n",
        "    sum_Y = sum_Y + Y[i]\n",
        "\t\t\n",
        "\t\t# sum of X[i] * Y[i].\n",
        "    sum_XY = sum_XY + X[i] * Y[i]\n",
        "\t\t\n",
        "\t\t# sum of square of array elements.\n",
        "    squareSum_X = squareSum_X + X[i] * X[i]\n",
        "    squareSum_Y = squareSum_Y + Y[i] * Y[i]\n",
        "\t\t\n",
        "    i = i + 1\n",
        "\t\n",
        "  # use formula for calculating correlation\n",
        "\t# coefficient.\n",
        "  corr = (float)(n * sum_XY - sum_X * sum_Y) / (float)(math.sqrt((n * squareSum_X - sum_X * sum_X)* (n * squareSum_Y - sum_Y * sum_Y)))\n",
        "  return corr\n",
        "\t\n",
        "# Driver function\n",
        "X = [15, 18, 21, 24, 27]\n",
        "Y = [25, 25, 27, 31, 32]\n",
        "\t\n",
        "# Find the size of array.\n",
        "n = len(X)\n",
        "\n",
        "# Function call to correlationCoefficient.\n",
        "print ('{0:.6f}'.format(correlationCoefficient(X, Y, n)))"
      ],
      "execution_count": null,
      "outputs": [
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          "name": "stdout",
          "text": [
            "0.953463\n"
          ]
        }
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