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+{"cells":[{"cell_type":"markdown","metadata":{"id":"9yKDqwpZIB4b"},"source":["# Lab | Hypothesis Testing"]},{"cell_type":"markdown","metadata":{"id":"ubGOQIPaIB4d"},"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":{"id":"AvXjTD9fIB4f"},"source":["**Challenge 1**"]},{"cell_type":"markdown","metadata":{"id":"Nf4jTOWTIB4f"},"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":5,"metadata":{"id":"6y2L200kIB4g","executionInfo":{"status":"ok","timestamp":1756140778028,"user_tz":-120,"elapsed":1044,"user":{"displayName":"Santiago Larrea","userId":"01956628674237458372"}}},"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":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"ZXrDfQTdIB4i","executionInfo":{"status":"ok","timestamp":1756140779623,"user_tz":-120,"elapsed":247,"user":{"displayName":"Santiago Larrea","userId":"01956628674237458372"}},"outputId":"b7035cb0-6657-42be-b015-29b941310c58"},"outputs":[{"output_type":"execute_result","data":{"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]"],"text/html":["\n","  <div id=\"df-68ec2241-dad2-4b2d-af6f-6d4ddff3b9ca\" class=\"colab-df-container\">\n","    <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>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-68ec2241-dad2-4b2d-af6f-6d4ddff3b9ca')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" 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Def\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27,\n        \"min\": 20,\n        \"max\": 230,\n        \"num_unique_values\": 92,\n        \"samples\": [\n          154,\n          45,\n          44\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Speed\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29,\n        \"min\": 5,\n        \"max\": 180,\n        \"num_unique_values\": 108,\n        \"samples\": [\n          113,\n          50,\n          100\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Generation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 6,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1,\n          2,\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Legendary\",\n      \"properties\": {\n        \"dtype\": \"boolean\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          true,\n          false\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":6}],"source":["df = pd.read_csv(\"https://raw.githubusercontent.com/data-bootcamp-v4/data/main/pokemon.csv\")\n","df"]},{"cell_type":"markdown","metadata":{"id":"vljVDHeZIB4j"},"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":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"1SKZjl2PIB4j","executionInfo":{"status":"ok","timestamp":1756140781207,"user_tz":-120,"elapsed":43,"user":{"displayName":"Santiago Larrea","userId":"01956628674237458372"}},"outputId":"1e3fe284-d2ee-4fa6-ede4-f45f1009904f"},"outputs":[{"output_type":"stream","name":"stdout","text":["t-statistic: 3.3349632905124063\n","p-value (bilateral): 0.0015987219490841199\n","p-value (unilateral Dragon > Grass): 0.0007993609745420599\n","We reject the null hypothesis: Dragons tend to have more HP than Grass.\n"]}],"source":["#code here\n","\n","#H0 Pokemons Dragon more HP than Grass\n","#H1 H0 is not true\n","\n","alpha = 0.05\n","\n","\n","\n","dragon_hp = df[df['Type 1'] == 'Dragon']['HP']\n","grass_hp  = df[df['Type 1'] == 'Grass']['HP']\n","\n","\n","stat, p_value = st.ttest_ind(dragon_hp, grass_hp, equal_var=False)\n","\n","print(\"t-statistic:\", stat)\n","print(\"p-value (bilateral):\", p_value)\n","\n","p_value_one_sided = p_value / 2 if stat > 0 else 1 - (p_value / 2)\n","print(\"p-value (unilateral Dragon > Grass):\", p_value_one_sided)\n","\n","alpha = 0.05\n","if p_value_one_sided < alpha:\n","    print(\"We reject the null hypothesis: Dragons tend to have more HP than Grass.\")\n","else:\n","    print(\"We are not able to reject the null hypothesis.\")"]},{"cell_type":"markdown","metadata":{"id":"ne2LGNxhIB4k"},"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":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"U1I9gBhGIB4l","executionInfo":{"status":"ok","timestamp":1756140816326,"user_tz":-120,"elapsed":46,"user":{"displayName":"Santiago Larrea","userId":"01956628674237458372"}},"outputId":"bd5de97b-d489-4323-c925-c692dab96d5c"},"outputs":[{"output_type":"stream","name":"stdout","text":["HP: t = 8.98, p = 0.0000\n","  → Reject H0: Significant difference.\n","\n","Attack: t = 10.44, p = 0.0000\n","  → Reject H0: Significant difference.\n","\n","Defense: t = 7.64, p = 0.0000\n","  → Reject H0: Significant difference.\n","\n","Sp. Atk: t = 13.42, p = 0.0000\n","  → Reject H0: Significant difference.\n","\n","Sp. Def: t = 10.02, p = 0.0000\n","  → Reject H0: Significant difference.\n","\n","Speed: t = 11.48, p = 0.0000\n","  → Reject H0: Significant difference.\n","\n"]}],"source":["#code here\n","stats_cols = [\"HP\", \"Attack\", \"Defense\", \"Sp. Atk\", \"Sp. Def\", \"Speed\"]\n","\n","legendary = df[df['Legendary'] == True]\n","non_legendary = df[df['Legendary'] == False]\n","\n","alpha = 0.05\n","\n","for col in stats_cols:\n","    stat, p_value = st.ttest_ind(\n","        legendary[col],\n","        non_legendary[col],\n","        equal_var=False\n","    )\n","    print(f\"{col}: t = {stat:.2f}, p = {p_value:.4f}\")\n","    if p_value < alpha:\n","        print(\"  → Reject H0: Significant difference.\\n\")\n","    else:\n","        print(\"  → Fail to reject H0: No significant difference.\\n\")"]},{"cell_type":"markdown","metadata":{"id":"vnFngSMSIB4l"},"source":["**Challenge 2**"]},{"cell_type":"markdown","metadata":{"id":"_sJdAdhdIB4l"},"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":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"IswnQug2IB4m","executionInfo":{"status":"ok","timestamp":1756140827891,"user_tz":-120,"elapsed":409,"user":{"displayName":"Santiago Larrea","userId":"01956628674237458372"}},"outputId":"b9e747c4-af76-43cb-92bd-a39b88c34ff7"},"outputs":[{"output_type":"execute_result","data":{"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  "],"text/html":["\n","  <div id=\"df-44faade2-c0bd-42cc-ae83-9a85fed8a817\" class=\"colab-df-container\">\n","    <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>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-44faade2-c0bd-42cc-ae83-9a85fed8a817')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            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lon2)**2 + (lat1 - lat2)**2)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"d-gt-0xgIB4m"},"outputs":[],"source":["school = (-118, 34)\n","hospital = (-122, 37)\n","\n","# Distancias\n","df['dist_school'] = euclidean_distance(df['longitude'], df['latitude'], school[0], school[1])\n","df['dist_hospital'] = euclidean_distance(df['longitude'], df['latitude'], hospital[0], hospital[1])\n","\n","# Consideramos \"close\" si está a menos de 0.5 de alguno\n","df['close'] = ((df['dist_school'] < 0.5) | (df['dist_hospital'] < 0.5))\n","\n","close_prices = df[df['close']]['median_house_value']\n","far_prices   = df[~df['close']]['median_house_value']\n","\n","stat, p_value = st.ttest_ind(close_prices, far_prices, equal_var=False)\n","\n","print(\"t-statistic:\", stat)\n","print(\"p-value:\", p_value)\n","\n","alpha = 0.05\n","if p_value < alpha:\n","    print(\"Reject H0: Houses close to school/hospital are significantly more expensive.\")\n","else:\n","    print(\"Fail to reject H0: No significant price difference found.\")"]}],"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":"3.10.9"},"colab":{"provenance":[]}},"nbformat":4,"nbformat_minor":0}
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diff --git a/lab-hypothesis-testing.ipynb b/lab-hypothesis-testing.ipynb
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--- a/lab-hypothesis-testing.ipynb
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@@ -1,520 +0,0 @@
-{
- "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": 3,
-   "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": 3,
-     "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": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#code here"
-   ]
-  },
-  {
-   "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": [],
-   "source": [
-    "#code here"
-   ]
-  },
-  {
-   "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": 5,
-   "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": 5,
-     "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": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "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": "3.10.9"
-  }
- },
- "nbformat": 4,
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