From 642ba2b80af208b0169f0ac184a21ef030e77acb Mon Sep 17 00:00:00 2001
From: s444501 <krywas@st.amu.edu.pl>
Date: Fri, 13 May 2022 22:06:56 +0200
Subject: [PATCH] misc. changes

---
 bootstrap-t.ipynb | 59 +++++++++++++++++++++++++++++++++++++++--------
 1 file changed, 49 insertions(+), 10 deletions(-)

diff --git a/bootstrap-t.ipynb b/bootstrap-t.ipynb
index cd93786..af28d86 100644
--- a/bootstrap-t.ipynb
+++ b/bootstrap-t.ipynb
@@ -1,8 +1,31 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Bootstrapowa wersja testu t.\n",
+    "Implementacja powinna obejmować test dla jednej próby, dla dwóch prób niezależnych oraz dla dwóch prób zależnych.\n",
+    "W każdej sytuacji oczekiwanym wejście jest zbiór danych w odpowiednim formacie, a wyjściem p-wartość oraz ostateczna decyzja.\n",
+    "Dodatkowo powinien być rysowany odpowiedni rozkład statystyki testowej."
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "markdown",
+   "source": [
+    "Zbiór danych          - ???\n",
+    "Hipoteza zerowa       - ???\n",
+    "Hipoteza alternatywna - ???"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 50,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -19,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 51,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -29,14 +52,14 @@
    "source": [
     "def generate_bootstraps(data, n_bootstraps=100):\n",
     "    data_size = data.shape[0]\n",
-    "    for b in range(n_bootstraps):\n",
-    "        indicies =  np.random.choice(len(data), size=data_size)\n",
-    "        yield data.iloc[indicies, :]"
+    "    for _ in range(n_bootstraps):\n",
+    "        indices =  np.random.choice(len(data), size=data_size)\n",
+    "        yield data.iloc[indices, :]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 52,
    "outputs": [],
    "source": [
     "def get_t_stat(data1, data2):\n",
@@ -57,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 53,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
@@ -80,7 +103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 54,
    "outputs": [],
    "source": [
     "def make_decision(data, columns, alpha=0.05):\n",
@@ -104,13 +127,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 55,
+   "outputs": [],
+   "source": [
+    "def draw_distribution():\n",
+    "    \"\"\"Funkcja rysuje rozkład statystyki testowej\"\"\"\n",
+    "    pass"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "t: 6.903407918031469, df: 998, cv: 1.6463818766348755, p: 9.018563673635072e-12\n",
+      "t: 6.891235313595221, df: 998, cv: 1.6463818766348755, p: 9.78683800667568e-12\n",
       "\n",
       "Reject the null hypothesis that the means are equal.\n",
       "Reject the null hypothesis that the means are equal.\n"