{ "cells": [ { "cell_type": "markdown", "id": "7f5be4f6-498f-45a3-a97f-a3617a73bfe5", "metadata": {}, "source": [ "# F distribution" ] }, { "cell_type": "markdown", "id": "4ce5ed6c-d5dd-418f-942c-1eb3602c366d", "metadata": {}, "source": [ "Let's investigate how shape of the F distribution curve look likes." ] }, { "cell_type": "code", "execution_count": 3, "id": "c8cc4961-b6b2-4a1a-baf7-89a09b29f272", "metadata": {}, "outputs": [], "source": [ "# import required libraries\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import scipy.stats as stats\n", "from IPython.display import display" ] }, { "cell_type": "markdown", "id": "80f36bec-c0af-4d41-8c1b-34dd40d860ba", "metadata": {}, "source": [ "Let's now investigate how shape of the F distribution curve changes with respect to degrees of freedom ($\\nu_1$ and $\\nu_2$). We will define a function first to simplify our calculations." ] }, { "cell_type": "code", "execution_count": 12, "id": "119b598b-baf0-4f33-a116-ade95cc03ae0", "metadata": {}, "outputs": [], "source": [ "# define a Python function with three parameters\n", "\n", "def f_dist_curve(dof1,dof2,ltype, col):\n", " \n", " fig, ax = plt.subplots(figsize = (6, 6))\n", " \n", " #use zip for parallel iteration\n", " for d1, d2, l, cl in zip(dof1, dof2, ltype, col):\n", " #generate 10000 random variables for given degrees of freedom\n", " x = stats.f.rvs(dfn = d1, dfd = d2, size = 10000)\n", " x1 = np.sort(x)\n", " plt.plot(x1, stats.f.pdf(x1, dfn = d1, dfd = d2), \n", " ls = l, c = cl, label = r'$\\nu_1=%.1f,\\nu_2=%.1f$' % (d1,d2)) \n", " \n", " plt.xlim(0, 5)\n", " plt.ylim(0, 0.8)\n", " plt.xlabel('$x$')\n", " plt.ylabel(r'$f(x|\\nu_1,\\nu_2)$')\n", " plt.title('F distribution curve')\n", " plt.legend(loc='upper right')\n", " plt.show()" ] }, { "cell_type": "markdown", "id": "dd15f1f5-1506-4b18-8f1b-34c62c9ae2f3", "metadata": {}, "source": [ "Now play with the function to see the changes on the normal distribution curve." ] }, { "cell_type": "code", "execution_count": 13, "id": "c31a6b1e-d89d-4dcd-9450-5d3e550ce7df", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": [ "None" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "## change the following values as you wish\n", "## define the input arguments (values of the parameters)\n", "dof1_values = [4, 4, 4, 4]\n", "dof2_values = [4, 10, 20, 30]\n", "linestyles = ['-', '--', '-.', ':']\n", "colors = ['blue', 'green','red','black']\n", "\n", "f_curve = f_dist_curve(dof1 = dof1_values, dof2 = dof2_values, ltype = linestyles, col = colors)\n", "\n", "display(f_curve)" ] }, { "cell_type": "markdown", "id": "1fc863a6-0d8f-45aa-878a-5196d98e261b", "metadata": {}, "source": [ "Calculate probabilities associated with chi-square distribution with 'stats.f.cdf(x, dfn, dfd)'." ] }, { "cell_type": "code", "execution_count": null, "id": "14955b9c-1f99-4b73-a6ff-7431202adfba", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.8.12" } }, "nbformat": 4, "nbformat_minor": 5 }