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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Lab | Hypothesis Testing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Objective**\n",
+    "\n",
+    "Welcome to the Hypothesis Testing Lab, where we embark on an enlightening journey through the realm of statistical decision-making! In this laboratory, we delve into various scenarios, applying the powerful tools of hypothesis testing to scrutinize and interpret data.\n",
+    "\n",
+    "From testing the mean of a single sample (One Sample T-Test), to investigating differences between independent groups (Two Sample T-Test), and exploring relationships within dependent samples (Paired Sample T-Test), our exploration knows no bounds. Furthermore, we'll venture into the realm of Analysis of Variance (ANOVA), unraveling the complexities of comparing means across multiple groups.\n",
+    "\n",
+    "So, grab your statistical tools, prepare your hypotheses, and let's embark on this fascinating journey of exploration and discovery in the world of hypothesis testing!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Challenge 1**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this challenge, we will be working with pokemon data. The data can be found here:\n",
+    "\n",
+    "- https://raw.githubusercontent.com/data-bootcamp-v4/data/main/pokemon.csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#libraries\n",
+    "import pandas as pd\n",
+    "import scipy.stats as st\n",
+    "import numpy as np\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Type 1</th>\n",
+       "      <th>Type 2</th>\n",
+       "      <th>HP</th>\n",
+       "      <th>Attack</th>\n",
+       "      <th>Defense</th>\n",
+       "      <th>Sp. Atk</th>\n",
+       "      <th>Sp. Def</th>\n",
+       "      <th>Speed</th>\n",
+       "      <th>Generation</th>\n",
+       "      <th>Legendary</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Bulbasaur</td>\n",
+       "      <td>Grass</td>\n",
+       "      <td>Poison</td>\n",
+       "      <td>45</td>\n",
+       "      <td>49</td>\n",
+       "      <td>49</td>\n",
+       "      <td>65</td>\n",
+       "      <td>65</td>\n",
+       "      <td>45</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Ivysaur</td>\n",
+       "      <td>Grass</td>\n",
+       "      <td>Poison</td>\n",
+       "      <td>60</td>\n",
+       "      <td>62</td>\n",
+       "      <td>63</td>\n",
+       "      <td>80</td>\n",
+       "      <td>80</td>\n",
+       "      <td>60</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Venusaur</td>\n",
+       "      <td>Grass</td>\n",
+       "      <td>Poison</td>\n",
+       "      <td>80</td>\n",
+       "      <td>82</td>\n",
+       "      <td>83</td>\n",
+       "      <td>100</td>\n",
+       "      <td>100</td>\n",
+       "      <td>80</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Mega Venusaur</td>\n",
+       "      <td>Grass</td>\n",
+       "      <td>Poison</td>\n",
+       "      <td>80</td>\n",
+       "      <td>100</td>\n",
+       "      <td>123</td>\n",
+       "      <td>122</td>\n",
+       "      <td>120</td>\n",
+       "      <td>80</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Charmander</td>\n",
+       "      <td>Fire</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>39</td>\n",
+       "      <td>52</td>\n",
+       "      <td>43</td>\n",
+       "      <td>60</td>\n",
+       "      <td>50</td>\n",
+       "      <td>65</td>\n",
+       "      <td>1</td>\n",
+       "      <td>False</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>795</th>\n",
+       "      <td>Diancie</td>\n",
+       "      <td>Rock</td>\n",
+       "      <td>Fairy</td>\n",
+       "      <td>50</td>\n",
+       "      <td>100</td>\n",
+       "      <td>150</td>\n",
+       "      <td>100</td>\n",
+       "      <td>150</td>\n",
+       "      <td>50</td>\n",
+       "      <td>6</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>796</th>\n",
+       "      <td>Mega Diancie</td>\n",
+       "      <td>Rock</td>\n",
+       "      <td>Fairy</td>\n",
+       "      <td>50</td>\n",
+       "      <td>160</td>\n",
+       "      <td>110</td>\n",
+       "      <td>160</td>\n",
+       "      <td>110</td>\n",
+       "      <td>110</td>\n",
+       "      <td>6</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>797</th>\n",
+       "      <td>Hoopa Confined</td>\n",
+       "      <td>Psychic</td>\n",
+       "      <td>Ghost</td>\n",
+       "      <td>80</td>\n",
+       "      <td>110</td>\n",
+       "      <td>60</td>\n",
+       "      <td>150</td>\n",
+       "      <td>130</td>\n",
+       "      <td>70</td>\n",
+       "      <td>6</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>798</th>\n",
+       "      <td>Hoopa Unbound</td>\n",
+       "      <td>Psychic</td>\n",
+       "      <td>Dark</td>\n",
+       "      <td>80</td>\n",
+       "      <td>160</td>\n",
+       "      <td>60</td>\n",
+       "      <td>170</td>\n",
+       "      <td>130</td>\n",
+       "      <td>80</td>\n",
+       "      <td>6</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>799</th>\n",
+       "      <td>Volcanion</td>\n",
+       "      <td>Fire</td>\n",
+       "      <td>Water</td>\n",
+       "      <td>80</td>\n",
+       "      <td>110</td>\n",
+       "      <td>120</td>\n",
+       "      <td>130</td>\n",
+       "      <td>90</td>\n",
+       "      <td>70</td>\n",
+       "      <td>6</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>800 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Name   Type 1  Type 2  HP  Attack  Defense  Sp. Atk  Sp. Def  \\\n",
+       "0         Bulbasaur    Grass  Poison  45      49       49       65       65   \n",
+       "1           Ivysaur    Grass  Poison  60      62       63       80       80   \n",
+       "2          Venusaur    Grass  Poison  80      82       83      100      100   \n",
+       "3     Mega Venusaur    Grass  Poison  80     100      123      122      120   \n",
+       "4        Charmander     Fire     NaN  39      52       43       60       50   \n",
+       "..              ...      ...     ...  ..     ...      ...      ...      ...   \n",
+       "795         Diancie     Rock   Fairy  50     100      150      100      150   \n",
+       "796    Mega Diancie     Rock   Fairy  50     160      110      160      110   \n",
+       "797  Hoopa Confined  Psychic   Ghost  80     110       60      150      130   \n",
+       "798   Hoopa Unbound  Psychic    Dark  80     160       60      170      130   \n",
+       "799       Volcanion     Fire   Water  80     110      120      130       90   \n",
+       "\n",
+       "     Speed  Generation  Legendary  \n",
+       "0       45           1      False  \n",
+       "1       60           1      False  \n",
+       "2       80           1      False  \n",
+       "3       80           1      False  \n",
+       "4       65           1      False  \n",
+       "..     ...         ...        ...  \n",
+       "795     50           6       True  \n",
+       "796    110           6       True  \n",
+       "797     70           6       True  \n",
+       "798     80           6       True  \n",
+       "799     70           6       True  \n",
+       "\n",
+       "[800 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/pokemon.csv\")\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- We posit that Pokemons of type Dragon have, on average, more HP stats than Grass. Choose the propper test and, with 5% significance, comment your findings."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "H0: dragoon_mean >= grass_mean\n",
+    "H1: dragoon_mean < grass_mean\n",
+    "\n",
+    "H0: dragoon_mean - grass_mean >= 0\n",
+    "H1: dragoon_mean - grass_mean < 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hp_dragon = df[df[\"Type 1\"]==\"Dragon\"][\"HP\"]\n",
+    "hp_grass = df[df[\"Type 1\"]==\"Grass\"][\"HP\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hp_dragon_std = hp_dragon.std(ddof=1)\n",
+    "hp_grass_std = hp_grass.std(ddof=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(np.float64(83.3125), np.float64(67.27142857142857))"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hp_dragon_mean, hp_grass_mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "HP          7.174327\n",
+       "Attack     11.329291\n",
+       "Defense     7.795239\n",
+       "Sp. Atk    12.377972\n",
+       "Sp. Def     9.182699\n",
+       "Speed       8.995969\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hp_dragon_mean = hp_dragon.mean()\n",
+    "hp_grass_mean = hp_grass.mean()\n",
+    "\n",
+    "hp_dragon_std = hp_dragon.std(ddof=1)\n",
+    "hp_grass_std = hp_grass.std(ddof=1)\n",
+    "\n",
+    "n1 = len(hp_dragon)\n",
+    "n2 = len(hp_grass)\n",
+    "\n",
+    "statistic = (hp_dragon_mean - hp_grass_mean) / np.sqrt( (hp_grass_std**2/n1) + (hp_dragon_std**2/n2))\n",
+    "statistic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- We posit that Legendary Pokemons have different stats (HP, Attack, Defense, Sp.Atk, Sp.Def, Speed) when comparing with Non-Legendary. Choose the propper test and, with 5% significance, comment your findings.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>HP</th>\n",
+       "      <th>Attack</th>\n",
+       "      <th>Defense</th>\n",
+       "      <th>Sp. Atk</th>\n",
+       "      <th>Sp. Def</th>\n",
+       "      <th>Speed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>45</td>\n",
+       "      <td>49</td>\n",
+       "      <td>49</td>\n",
+       "      <td>65</td>\n",
+       "      <td>65</td>\n",
+       "      <td>45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>60</td>\n",
+       "      <td>62</td>\n",
+       "      <td>63</td>\n",
+       "      <td>80</td>\n",
+       "      <td>80</td>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>80</td>\n",
+       "      <td>82</td>\n",
+       "      <td>83</td>\n",
+       "      <td>100</td>\n",
+       "      <td>100</td>\n",
+       "      <td>80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>80</td>\n",
+       "      <td>100</td>\n",
+       "      <td>123</td>\n",
+       "      <td>122</td>\n",
+       "      <td>120</td>\n",
+       "      <td>80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>39</td>\n",
+       "      <td>52</td>\n",
+       "      <td>43</td>\n",
+       "      <td>60</td>\n",
+       "      <td>50</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>795</th>\n",
+       "      <td>50</td>\n",
+       "      <td>100</td>\n",
+       "      <td>150</td>\n",
+       "      <td>100</td>\n",
+       "      <td>150</td>\n",
+       "      <td>50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>796</th>\n",
+       "      <td>50</td>\n",
+       "      <td>160</td>\n",
+       "      <td>110</td>\n",
+       "      <td>160</td>\n",
+       "      <td>110</td>\n",
+       "      <td>110</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>797</th>\n",
+       "      <td>80</td>\n",
+       "      <td>110</td>\n",
+       "      <td>60</td>\n",
+       "      <td>150</td>\n",
+       "      <td>130</td>\n",
+       "      <td>70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>798</th>\n",
+       "      <td>80</td>\n",
+       "      <td>160</td>\n",
+       "      <td>60</td>\n",
+       "      <td>170</td>\n",
+       "      <td>130</td>\n",
+       "      <td>80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>799</th>\n",
+       "      <td>80</td>\n",
+       "      <td>110</td>\n",
+       "      <td>120</td>\n",
+       "      <td>130</td>\n",
+       "      <td>90</td>\n",
+       "      <td>70</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>800 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     HP  Attack  Defense  Sp. Atk  Sp. Def  Speed\n",
+       "0    45      49       49       65       65     45\n",
+       "1    60      62       63       80       80     60\n",
+       "2    80      82       83      100      100     80\n",
+       "3    80     100      123      122      120     80\n",
+       "4    39      52       43       60       50     65\n",
+       "..   ..     ...      ...      ...      ...    ...\n",
+       "795  50     100      150      100      150     50\n",
+       "796  50     160      110      160      110    110\n",
+       "797  80     110       60      150      130     70\n",
+       "798  80     160       60      170      130     80\n",
+       "799  80     110      120      130       90     70\n",
+       "\n",
+       "[800 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stats = \"HP\", \"Attack\",\t\"Defense\",\t\"Sp. Atk\",\t\"Sp. Def\",\t\"Speed\"\n",
+    "df_stats = df[list(stats)]\n",
+    "df_stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>HP</th>\n",
+       "      <th>Attack</th>\n",
+       "      <th>Defense</th>\n",
+       "      <th>Sp. Atk</th>\n",
+       "      <th>Sp. Def</th>\n",
+       "      <th>Speed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>45</td>\n",
+       "      <td>49</td>\n",
+       "      <td>49</td>\n",
+       "      <td>65</td>\n",
+       "      <td>65</td>\n",
+       "      <td>45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>60</td>\n",
+       "      <td>62</td>\n",
+       "      <td>63</td>\n",
+       "      <td>80</td>\n",
+       "      <td>80</td>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>80</td>\n",
+       "      <td>82</td>\n",
+       "      <td>83</td>\n",
+       "      <td>100</td>\n",
+       "      <td>100</td>\n",
+       "      <td>80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>80</td>\n",
+       "      <td>100</td>\n",
+       "      <td>123</td>\n",
+       "      <td>122</td>\n",
+       "      <td>120</td>\n",
+       "      <td>80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>39</td>\n",
+       "      <td>52</td>\n",
+       "      <td>43</td>\n",
+       "      <td>60</td>\n",
+       "      <td>50</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>787</th>\n",
+       "      <td>85</td>\n",
+       "      <td>100</td>\n",
+       "      <td>122</td>\n",
+       "      <td>58</td>\n",
+       "      <td>75</td>\n",
+       "      <td>54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>788</th>\n",
+       "      <td>55</td>\n",
+       "      <td>69</td>\n",
+       "      <td>85</td>\n",
+       "      <td>32</td>\n",
+       "      <td>35</td>\n",
+       "      <td>28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>789</th>\n",
+       "      <td>95</td>\n",
+       "      <td>117</td>\n",
+       "      <td>184</td>\n",
+       "      <td>44</td>\n",
+       "      <td>46</td>\n",
+       "      <td>28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>790</th>\n",
+       "      <td>40</td>\n",
+       "      <td>30</td>\n",
+       "      <td>35</td>\n",
+       "      <td>45</td>\n",
+       "      <td>40</td>\n",
+       "      <td>55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>791</th>\n",
+       "      <td>85</td>\n",
+       "      <td>70</td>\n",
+       "      <td>80</td>\n",
+       "      <td>97</td>\n",
+       "      <td>80</td>\n",
+       "      <td>123</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>735 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     HP  Attack  Defense  Sp. Atk  Sp. Def  Speed\n",
+       "0    45      49       49       65       65     45\n",
+       "1    60      62       63       80       80     60\n",
+       "2    80      82       83      100      100     80\n",
+       "3    80     100      123      122      120     80\n",
+       "4    39      52       43       60       50     65\n",
+       "..   ..     ...      ...      ...      ...    ...\n",
+       "787  85     100      122       58       75     54\n",
+       "788  55      69       85       32       35     28\n",
+       "789  95     117      184       44       46     28\n",
+       "790  40      30       35       45       40     55\n",
+       "791  85      70       80       97       80    123\n",
+       "\n",
+       "[735 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "leg = df_stats[df[\"Legendary\"]== True]\n",
+    "leg\n",
+    "non_leg = df_stats[df[\"Legendary\"] == False]\n",
+    "non_leg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "t-stat: 11.47504444631443\n",
+      "p-value: 1.049016311882451e-18\n"
+     ]
+    }
+   ],
+   "source": [
+    "t_stat, p_value = st.ttest_ind(leg, non_leg, equal_var=False)  # Welch\n",
+    "print(\"t-stat:\", t_stat)\n",
+    "print(\"p-value:\", p_value)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HP: t=8.981, p=0.0000\n",
+      "Attack: t=10.438, p=0.0000\n",
+      "Defense: t=7.637, p=0.0000\n",
+      "Sp. Atk: t=13.417, p=0.0000\n",
+      "Sp. Def: t=10.016, p=0.0000\n",
+      "Speed: t=11.475, p=0.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "for col in stats:\n",
+    "    leg = df[df[\"Legendary\"] == True][col]\n",
+    "    non_leg = df[df[\"Legendary\"] == False][col]\n",
+    "    \n",
+    "    t_stat, p_value = st.ttest_ind(leg, non_leg, equal_var=False)\n",
+    "    print(f\"{col}: t={t_stat:.3f}, p={p_value:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Challenge 2**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this challenge, we will be working with california-housing data. The data can be found here:\n",
+    "- https://raw.githubusercontent.com/data-bootcamp-v4/data/main/california_housing.csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>housing_median_age</th>\n",
+       "      <th>total_rooms</th>\n",
+       "      <th>total_bedrooms</th>\n",
+       "      <th>population</th>\n",
+       "      <th>households</th>\n",
+       "      <th>median_income</th>\n",
+       "      <th>median_house_value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-114.31</td>\n",
+       "      <td>34.19</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5612.0</td>\n",
+       "      <td>1283.0</td>\n",
+       "      <td>1015.0</td>\n",
+       "      <td>472.0</td>\n",
+       "      <td>1.4936</td>\n",
+       "      <td>66900.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-114.47</td>\n",
+       "      <td>34.40</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>7650.0</td>\n",
+       "      <td>1901.0</td>\n",
+       "      <td>1129.0</td>\n",
+       "      <td>463.0</td>\n",
+       "      <td>1.8200</td>\n",
+       "      <td>80100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-114.56</td>\n",
+       "      <td>33.69</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>720.0</td>\n",
+       "      <td>174.0</td>\n",
+       "      <td>333.0</td>\n",
+       "      <td>117.0</td>\n",
+       "      <td>1.6509</td>\n",
+       "      <td>85700.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-114.57</td>\n",
+       "      <td>33.64</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>1501.0</td>\n",
+       "      <td>337.0</td>\n",
+       "      <td>515.0</td>\n",
+       "      <td>226.0</td>\n",
+       "      <td>3.1917</td>\n",
+       "      <td>73400.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-114.57</td>\n",
+       "      <td>33.57</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>1454.0</td>\n",
+       "      <td>326.0</td>\n",
+       "      <td>624.0</td>\n",
+       "      <td>262.0</td>\n",
+       "      <td>1.9250</td>\n",
+       "      <td>65500.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
+       "0    -114.31     34.19                15.0       5612.0          1283.0   \n",
+       "1    -114.47     34.40                19.0       7650.0          1901.0   \n",
+       "2    -114.56     33.69                17.0        720.0           174.0   \n",
+       "3    -114.57     33.64                14.0       1501.0           337.0   \n",
+       "4    -114.57     33.57                20.0       1454.0           326.0   \n",
+       "\n",
+       "   population  households  median_income  median_house_value  \n",
+       "0      1015.0       472.0         1.4936             66900.0  \n",
+       "1      1129.0       463.0         1.8200             80100.0  \n",
+       "2       333.0       117.0         1.6509             85700.0  \n",
+       "3       515.0       226.0         3.1917             73400.0  \n",
+       "4       624.0       262.0         1.9250             65500.0  "
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv(\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/california_housing.csv\")\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**We posit that houses close to either a school or a hospital are more expensive.**\n",
+    "\n",
+    "- School coordinates (-118, 34)\n",
+    "- Hospital coordinates (-122, 37)\n",
+    "\n",
+    "We consider a house (neighborhood) to be close to a school or hospital if the distance is lower than 0.50.\n",
+    "\n",
+    "Hint:\n",
+    "- Write a function to calculate euclidean distance from each house (neighborhood) to the school and to the hospital.\n",
+    "- Divide your dataset into houses close and far from either a hospital or school.\n",
+    "- Choose the propper test and, with 5% significance, comment your findings.\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0        4.034476\n",
+       "1        3.890617\n",
+       "2        3.792690\n",
+       "3        3.787149\n",
+       "4        3.794443\n",
+       "           ...   \n",
+       "16995    8.851147\n",
+       "16996    8.939855\n",
+       "16997    9.848208\n",
+       "16998    9.816394\n",
+       "16999    8.882100\n",
+       "Length: 17000, dtype: float64"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "lon0, lat0 = -118.34, 34.0\n",
+    "\n",
+    "df_distance_school = np.sqrt((df['longitude'] - lon0)**2 + (df['latitude'] - lat0)**2)\n",
+    "df_distance_school"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0        8.535789\n",
+       "1        8.316850\n",
+       "2        8.482464\n",
+       "3        8.492915\n",
+       "4        8.520851\n",
+       "           ...   \n",
+       "16995    4.048271\n",
+       "16996    4.150434\n",
+       "16997    5.210614\n",
+       "16998    5.173480\n",
+       "16999    4.056107\n",
+       "Length: 17000, dtype: float64"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lon1, lat1 = -122.37, 37.0\n",
+    "\n",
+    "df_distance_hospital = np.sqrt((df['longitude'] - lon1)**2 + (df['latitude'] - lat1)**2)\n",
+    "df_distance_hospital"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0        False\n",
+       "1        False\n",
+       "2        False\n",
+       "3        False\n",
+       "4        False\n",
+       "         ...  \n",
+       "16995    False\n",
+       "16996    False\n",
+       "16997    False\n",
+       "16998    False\n",
+       "16999    False\n",
+       "Length: 17000, dtype: bool"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "price_close_school = df[df_distance_school <= 0.5]['median_house_value']\n",
+    "df_school_close"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0         66900.0\n",
+       "1         80100.0\n",
+       "2         85700.0\n",
+       "3         73400.0\n",
+       "4         65500.0\n",
+       "           ...   \n",
+       "16995    111400.0\n",
+       "16996     79000.0\n",
+       "16997    103600.0\n",
+       "16998     85800.0\n",
+       "16999     94600.0\n",
+       "Name: median_house_value, Length: 11743, dtype: float64"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_school_far = df[df_distance_school > 0.5]['median_house_value']\n",
+    "df_school_far"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0         66900.0\n",
+       "1         80100.0\n",
+       "2         85700.0\n",
+       "3         73400.0\n",
+       "4         65500.0\n",
+       "           ...   \n",
+       "16995    111400.0\n",
+       "16996     79000.0\n",
+       "16997    103600.0\n",
+       "16998     85800.0\n",
+       "16999     94600.0\n",
+       "Name: median_house_value, Length: 16545, dtype: float64"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_hospital_close = df[df_distance_hospital < 0.5]['median_house_value']\n",
+    "df_hospital_close"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13131    289500.0\n",
+       "13132    321100.0\n",
+       "13133    308800.0\n",
+       "13229    293300.0\n",
+       "13230    356600.0\n",
+       "           ...   \n",
+       "15384    500001.0\n",
+       "15587    500001.0\n",
+       "15685    456300.0\n",
+       "15686    286100.0\n",
+       "15971    354300.0\n",
+       "Name: median_house_value, Length: 455, dtype: float64"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_hospital_far = df[df_distance_hospital > 0.5]['median_house_value']\n",
+    "df_hospital_far"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(-181.21516936688832)"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s_mean_close = df_school_close.mean()\n",
+    "s_mean_far = df_school_far.mean()\n",
+    "\n",
+    "s_std_close = df_school_close.std(ddof=1)\n",
+    "s_std_far = df_school_far.std(ddof=1)\n",
+    "\n",
+    "n_close = len(price_close_school)\n",
+    "n_far = len(df_school_far)\n",
+    "\n",
+    "t_stat = (s_mean_close - s_mean_far) / np.sqrt((s_std_close**2 / n_close) + (s_std_far**2 / n_far))\n",
+    "t_stat\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(-91.69177535787794)"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "h_mean_close = df_hospital_close.mean()\n",
+    "h_mean_far = df_hospital_far.mean()\n",
+    "\n",
+    "h_std_close = df_hospital_close.std(ddof=1)\n",
+    "h_std_far = df_hospital_far.std(ddof=1)\n",
+    "\n",
+    "n_close = len(price_close_school)\n",
+    "n_far = len(df_school_far)\n",
+    "\n",
+    "t_stat = (h_mean_close - h_mean_far) / np.sqrt((h_std_close**2 / n_close) + (h_std_far**2 / n_far))\n",
+    "t_stat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:base] *",
+   "language": "python",
+   "name": "conda-base-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/anaconda_projects/db/project_filebrowser.db b/anaconda_projects/db/project_filebrowser.db
new file mode 100644
index 0000000..6fab372
Binary files /dev/null and b/anaconda_projects/db/project_filebrowser.db differ
diff --git a/lab-hypothesis-testing.ipynb b/lab-hypothesis-testing.ipynb
index 0cc26d5..9814711 100644
--- a/lab-hypothesis-testing.ipynb
+++ b/lab-hypothesis-testing.ipynb
@@ -51,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -278,7 +278,7 @@
        "[800 rows x 11 columns]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -295,13 +295,91 @@
     "- We posit that Pokemons of type Dragon have, on average, more HP stats than Grass. Choose the propper test and, with 5% significance, comment your findings."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "H0: dragoon_mean >= grass_mean\n",
+    "H1: dragoon_mean < grass_mean\n",
+    "\n",
+    "H0: dragoon_mean - grass_mean >= 0\n",
+    "H1: dragoon_mean - grass_mean < 0"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hp_dragon = df[df[\"Type 1\"]==\"Dragon\"][\"HP\"]\n",
+    "hp_grass = df[df[\"Type 1\"]==\"Grass\"][\"HP\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#code here"
+    "hp_dragon_std = hp_dragon.std(ddof=1)\n",
+    "hp_grass_std = hp_grass.std(ddof=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(np.float64(83.3125), np.float64(67.27142857142857))"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hp_dragon_mean, hp_grass_mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "HP          7.174327\n",
+       "Attack     11.329291\n",
+       "Defense     7.795239\n",
+       "Sp. Atk    12.377972\n",
+       "Sp. Def     9.182699\n",
+       "Speed       8.995969\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hp_dragon_mean = hp_dragon.mean()\n",
+    "hp_grass_mean = hp_grass.mean()\n",
+    "\n",
+    "hp_dragon_std = hp_dragon.std(ddof=1)\n",
+    "hp_grass_std = hp_grass.std(ddof=1)\n",
+    "\n",
+    "n1 = len(hp_dragon)\n",
+    "n2 = len(hp_grass)\n",
+    "\n",
+    "statistic = (hp_dragon_mean - hp_grass_mean) / np.sqrt( (hp_grass_std**2/n1) + (hp_dragon_std**2/n2))\n",
+    "statistic"
    ]
   },
   {
@@ -315,9 +393,382 @@
    "cell_type": "code",
    "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>HP</th>\n",
+       "      <th>Attack</th>\n",
+       "      <th>Defense</th>\n",
+       "      <th>Sp. Atk</th>\n",
+       "      <th>Sp. Def</th>\n",
+       "      <th>Speed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>45</td>\n",
+       "      <td>49</td>\n",
+       "      <td>49</td>\n",
+       "      <td>65</td>\n",
+       "      <td>65</td>\n",
+       "      <td>45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>60</td>\n",
+       "      <td>62</td>\n",
+       "      <td>63</td>\n",
+       "      <td>80</td>\n",
+       "      <td>80</td>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>80</td>\n",
+       "      <td>82</td>\n",
+       "      <td>83</td>\n",
+       "      <td>100</td>\n",
+       "      <td>100</td>\n",
+       "      <td>80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>80</td>\n",
+       "      <td>100</td>\n",
+       "      <td>123</td>\n",
+       "      <td>122</td>\n",
+       "      <td>120</td>\n",
+       "      <td>80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>39</td>\n",
+       "      <td>52</td>\n",
+       "      <td>43</td>\n",
+       "      <td>60</td>\n",
+       "      <td>50</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>795</th>\n",
+       "      <td>50</td>\n",
+       "      <td>100</td>\n",
+       "      <td>150</td>\n",
+       "      <td>100</td>\n",
+       "      <td>150</td>\n",
+       "      <td>50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>796</th>\n",
+       "      <td>50</td>\n",
+       "      <td>160</td>\n",
+       "      <td>110</td>\n",
+       "      <td>160</td>\n",
+       "      <td>110</td>\n",
+       "      <td>110</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>797</th>\n",
+       "      <td>80</td>\n",
+       "      <td>110</td>\n",
+       "      <td>60</td>\n",
+       "      <td>150</td>\n",
+       "      <td>130</td>\n",
+       "      <td>70</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>798</th>\n",
+       "      <td>80</td>\n",
+       "      <td>160</td>\n",
+       "      <td>60</td>\n",
+       "      <td>170</td>\n",
+       "      <td>130</td>\n",
+       "      <td>80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>799</th>\n",
+       "      <td>80</td>\n",
+       "      <td>110</td>\n",
+       "      <td>120</td>\n",
+       "      <td>130</td>\n",
+       "      <td>90</td>\n",
+       "      <td>70</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>800 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     HP  Attack  Defense  Sp. Atk  Sp. Def  Speed\n",
+       "0    45      49       49       65       65     45\n",
+       "1    60      62       63       80       80     60\n",
+       "2    80      82       83      100      100     80\n",
+       "3    80     100      123      122      120     80\n",
+       "4    39      52       43       60       50     65\n",
+       "..   ..     ...      ...      ...      ...    ...\n",
+       "795  50     100      150      100      150     50\n",
+       "796  50     160      110      160      110    110\n",
+       "797  80     110       60      150      130     70\n",
+       "798  80     160       60      170      130     80\n",
+       "799  80     110      120      130       90     70\n",
+       "\n",
+       "[800 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "stats = \"HP\", \"Attack\",\t\"Defense\",\t\"Sp. Atk\",\t\"Sp. Def\",\t\"Speed\"\n",
+    "df_stats = df[list(stats)]\n",
+    "df_stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>HP</th>\n",
+       "      <th>Attack</th>\n",
+       "      <th>Defense</th>\n",
+       "      <th>Sp. Atk</th>\n",
+       "      <th>Sp. Def</th>\n",
+       "      <th>Speed</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>45</td>\n",
+       "      <td>49</td>\n",
+       "      <td>49</td>\n",
+       "      <td>65</td>\n",
+       "      <td>65</td>\n",
+       "      <td>45</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>60</td>\n",
+       "      <td>62</td>\n",
+       "      <td>63</td>\n",
+       "      <td>80</td>\n",
+       "      <td>80</td>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>80</td>\n",
+       "      <td>82</td>\n",
+       "      <td>83</td>\n",
+       "      <td>100</td>\n",
+       "      <td>100</td>\n",
+       "      <td>80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>80</td>\n",
+       "      <td>100</td>\n",
+       "      <td>123</td>\n",
+       "      <td>122</td>\n",
+       "      <td>120</td>\n",
+       "      <td>80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>39</td>\n",
+       "      <td>52</td>\n",
+       "      <td>43</td>\n",
+       "      <td>60</td>\n",
+       "      <td>50</td>\n",
+       "      <td>65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>787</th>\n",
+       "      <td>85</td>\n",
+       "      <td>100</td>\n",
+       "      <td>122</td>\n",
+       "      <td>58</td>\n",
+       "      <td>75</td>\n",
+       "      <td>54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>788</th>\n",
+       "      <td>55</td>\n",
+       "      <td>69</td>\n",
+       "      <td>85</td>\n",
+       "      <td>32</td>\n",
+       "      <td>35</td>\n",
+       "      <td>28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>789</th>\n",
+       "      <td>95</td>\n",
+       "      <td>117</td>\n",
+       "      <td>184</td>\n",
+       "      <td>44</td>\n",
+       "      <td>46</td>\n",
+       "      <td>28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>790</th>\n",
+       "      <td>40</td>\n",
+       "      <td>30</td>\n",
+       "      <td>35</td>\n",
+       "      <td>45</td>\n",
+       "      <td>40</td>\n",
+       "      <td>55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>791</th>\n",
+       "      <td>85</td>\n",
+       "      <td>70</td>\n",
+       "      <td>80</td>\n",
+       "      <td>97</td>\n",
+       "      <td>80</td>\n",
+       "      <td>123</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>735 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     HP  Attack  Defense  Sp. Atk  Sp. Def  Speed\n",
+       "0    45      49       49       65       65     45\n",
+       "1    60      62       63       80       80     60\n",
+       "2    80      82       83      100      100     80\n",
+       "3    80     100      123      122      120     80\n",
+       "4    39      52       43       60       50     65\n",
+       "..   ..     ...      ...      ...      ...    ...\n",
+       "787  85     100      122       58       75     54\n",
+       "788  55      69       85       32       35     28\n",
+       "789  95     117      184       44       46     28\n",
+       "790  40      30       35       45       40     55\n",
+       "791  85      70       80       97       80    123\n",
+       "\n",
+       "[735 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#code here"
+    "leg = df_stats[df[\"Legendary\"]== True]\n",
+    "leg\n",
+    "non_leg = df_stats[df[\"Legendary\"] == False]\n",
+    "non_leg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "t-stat: 11.47504444631443\n",
+      "p-value: 1.049016311882451e-18\n"
+     ]
+    }
+   ],
+   "source": [
+    "t_stat, p_value = st.ttest_ind(leg, non_leg, equal_var=False)  # Welch\n",
+    "print(\"t-stat:\", t_stat)\n",
+    "print(\"p-value:\", p_value)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "HP: t=8.981, p=0.0000\n",
+      "Attack: t=10.438, p=0.0000\n",
+      "Defense: t=7.637, p=0.0000\n",
+      "Sp. Atk: t=13.417, p=0.0000\n",
+      "Sp. Def: t=10.016, p=0.0000\n",
+      "Speed: t=11.475, p=0.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "for col in stats:\n",
+    "    leg = df[df[\"Legendary\"] == True][col]\n",
+    "    non_leg = df[df[\"Legendary\"] == False][col]\n",
+    "    \n",
+    "    t_stat, p_value = st.ttest_ind(leg, non_leg, equal_var=False)\n",
+    "    print(f\"{col}: t={t_stat:.3f}, p={p_value:.4f}\")"
    ]
   },
   {
@@ -337,7 +788,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -453,7 +904,7 @@
        "4       624.0       262.0         1.9250             65500.0  "
       ]
      },
-     "execution_count": 5,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -483,10 +934,261 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0        4.034476\n",
+       "1        3.890617\n",
+       "2        3.792690\n",
+       "3        3.787149\n",
+       "4        3.794443\n",
+       "           ...   \n",
+       "16995    8.851147\n",
+       "16996    8.939855\n",
+       "16997    9.848208\n",
+       "16998    9.816394\n",
+       "16999    8.882100\n",
+       "Length: 17000, dtype: float64"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "lon0, lat0 = -118.34, 34.0\n",
+    "\n",
+    "df_distance_school = np.sqrt((df['longitude'] - lon0)**2 + (df['latitude'] - lat0)**2)\n",
+    "df_distance_school"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0        8.535789\n",
+       "1        8.316850\n",
+       "2        8.482464\n",
+       "3        8.492915\n",
+       "4        8.520851\n",
+       "           ...   \n",
+       "16995    4.048271\n",
+       "16996    4.150434\n",
+       "16997    5.210614\n",
+       "16998    5.173480\n",
+       "16999    4.056107\n",
+       "Length: 17000, dtype: float64"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lon1, lat1 = -122.37, 37.0\n",
+    "\n",
+    "df_distance_hospital = np.sqrt((df['longitude'] - lon1)**2 + (df['latitude'] - lat1)**2)\n",
+    "df_distance_hospital"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0        False\n",
+       "1        False\n",
+       "2        False\n",
+       "3        False\n",
+       "4        False\n",
+       "         ...  \n",
+       "16995    False\n",
+       "16996    False\n",
+       "16997    False\n",
+       "16998    False\n",
+       "16999    False\n",
+       "Length: 17000, dtype: bool"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "price_close_school = df[df_distance_school <= 0.5]['median_house_value']\n",
+    "df_school_close"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0         66900.0\n",
+       "1         80100.0\n",
+       "2         85700.0\n",
+       "3         73400.0\n",
+       "4         65500.0\n",
+       "           ...   \n",
+       "16995    111400.0\n",
+       "16996     79000.0\n",
+       "16997    103600.0\n",
+       "16998     85800.0\n",
+       "16999     94600.0\n",
+       "Name: median_house_value, Length: 11743, dtype: float64"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_school_far = df[df_distance_school > 0.5]['median_house_value']\n",
+    "df_school_far"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0         66900.0\n",
+       "1         80100.0\n",
+       "2         85700.0\n",
+       "3         73400.0\n",
+       "4         65500.0\n",
+       "           ...   \n",
+       "16995    111400.0\n",
+       "16996     79000.0\n",
+       "16997    103600.0\n",
+       "16998     85800.0\n",
+       "16999     94600.0\n",
+       "Name: median_house_value, Length: 16545, dtype: float64"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_hospital_close = df[df_distance_hospital < 0.5]['median_house_value']\n",
+    "df_hospital_close"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13131    289500.0\n",
+       "13132    321100.0\n",
+       "13133    308800.0\n",
+       "13229    293300.0\n",
+       "13230    356600.0\n",
+       "           ...   \n",
+       "15384    500001.0\n",
+       "15587    500001.0\n",
+       "15685    456300.0\n",
+       "15686    286100.0\n",
+       "15971    354300.0\n",
+       "Name: median_house_value, Length: 455, dtype: float64"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_hospital_far = df[df_distance_hospital > 0.5]['median_house_value']\n",
+    "df_hospital_far"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(-181.21516936688832)"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "s_mean_close = df_school_close.mean()\n",
+    "s_mean_far = df_school_far.mean()\n",
+    "\n",
+    "s_std_close = df_school_close.std(ddof=1)\n",
+    "s_std_far = df_school_far.std(ddof=1)\n",
+    "\n",
+    "n_close = len(price_close_school)\n",
+    "n_far = len(df_school_far)\n",
+    "\n",
+    "t_stat = (s_mean_close - s_mean_far) / np.sqrt((s_std_close**2 / n_close) + (s_std_far**2 / n_far))\n",
+    "t_stat\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(-91.69177535787794)"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "h_mean_close = df_hospital_close.mean()\n",
+    "h_mean_far = df_hospital_far.mean()\n",
+    "\n",
+    "h_std_close = df_hospital_close.std(ddof=1)\n",
+    "h_std_far = df_hospital_far.std(ddof=1)\n",
+    "\n",
+    "n_close = len(price_close_school)\n",
+    "n_far = len(df_school_far)\n",
+    "\n",
+    "t_stat = (h_mean_close - h_mean_far) / np.sqrt((h_std_close**2 / n_close) + (h_std_far**2 / n_far))\n",
+    "t_stat"
+   ]
   },
   {
    "cell_type": "code",
@@ -498,9 +1200,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python [conda env:base] *",
    "language": "python",
-   "name": "python3"
+   "name": "conda-base-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -512,9 +1214,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.13.5"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }