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@samlaf
Last active November 21, 2020 02:31
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"from scipy.stats import norm\n",
"import numpy as np\n",
"from ipywidgets import interact"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1-sided $\\mu$ test\n",
"#### $H_0: \\mu=0$\n",
"#### $H_1: \\mu=mu_1 > 0$"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "2712523a50634a5f8eda7802de4af100",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(FloatSlider(value=3.0, description='mu', max=6.0, step=0.25), Output()), _dom_classes=('…"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"from scipy.stats import norm\n",
"import numpy as np\n",
"from ipywidgets import interact\n",
"@interact(mu=(0.,6.,0.25))\n",
"def plot_test(mu):\n",
" MINX = -3\n",
" MAXX = 10\n",
" dt = 0.1\n",
" x = np.arange(MINX,MAXX,dt)\n",
" alpha = 0.05\n",
" N01 = norm.pdf(x,0,1) #H_0\n",
"\n",
" Nmu1 = norm.pdf(x,mu,1) # H_1\n",
" rejection_point = norm.ppf(1-alpha)\n",
" rejectreg_right = np.arange(rejection_point, MAXX, dt)\n",
" power = 1 - norm.cdf(rejection_point, mu)\n",
" \n",
" plt.plot(x, N01, label=\"$H_0: N(0,1)$\")\n",
" plt.plot(x, Nmu1, label=\"$H_1: N(\\mu_1,1)$\")\n",
" plt.fill_between(rejectreg_right, norm.pdf(rejectreg_right), color='blue', alpha=0.3, label=\"significance = 0.05\")\n",
" bottom = np.minimum(norm.pdf(rejectreg_right), norm.pdf(rejectreg_right,mu))\n",
" plt.fill_between(rejectreg_right, norm.pdf(rejectreg_right,mu,1), bottom, \n",
" color='orange', alpha=0.3, label=\"power = {:0.3f}\".format(power))\n",
" plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# $\\sigma^2$ test"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"MAXX = 3\n",
"dt = 0.1\n",
"x = np.arange(-MAXX,MAXX,dt)\n",
"alpha = 0.05"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"N01 = norm.pdf(x,0,1)\n",
"N02 = norm.pdf(x,0,0.25)\n",
"rejection_point = norm.ppf(1-alpha/2)\n",
"rejectreg_right = np.arange(rejection_point, MAXX, dt)\n",
"rejectreg_left = - rejectreg_right"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7f80f7d13df0>"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(x, N01, label=\"$H_0: N(0,1)$\")\n",
"plt.plot(x, N02, label=\"$H_1: N(0,0.25)$\")\n",
"plt.fill_between(rejectreg_right, norm.pdf(rejectreg_right), color='blue', alpha=0.3, label=\"reject region\")\n",
"plt.fill_between(rejectreg_left, norm.pdf(rejectreg_left), color='blue', alpha=0.3)\n",
"plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7fedc2a5ac70>"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
},
{
"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": [
"lik_ratio = N02/N01\n",
"plt.plot(x, lik_ratio, label=\"likelihood ratio\")\n",
"plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"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": "3.8.3"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
ipywidgets
numpy
scipy
matplotlib
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