diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index cdc1acb..80b0187 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -14,12 +14,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
     "# import numpy and pandas\n",
-    "\n"
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "import scipy.stats as sts\n"
    ]
   },
   {
@@ -35,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,19 +57,189 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "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>#</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Type 1</th>\n",
+       "      <th>Type 2</th>\n",
+       "      <th>Total</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>1</td>\n",
+       "      <td>Bulbasaur</td>\n",
+       "      <td>Grass</td>\n",
+       "      <td>Poison</td>\n",
+       "      <td>318</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>2</td>\n",
+       "      <td>Ivysaur</td>\n",
+       "      <td>Grass</td>\n",
+       "      <td>Poison</td>\n",
+       "      <td>405</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>3</td>\n",
+       "      <td>Venusaur</td>\n",
+       "      <td>Grass</td>\n",
+       "      <td>Poison</td>\n",
+       "      <td>525</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>3</td>\n",
+       "      <td>VenusaurMega Venusaur</td>\n",
+       "      <td>Grass</td>\n",
+       "      <td>Poison</td>\n",
+       "      <td>625</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",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   #                   Name Type 1  Type 2  Total  HP  Attack  Defense  \\\n",
+       "0  1              Bulbasaur  Grass  Poison    318  45      49       49   \n",
+       "1  2                Ivysaur  Grass  Poison    405  60      62       63   \n",
+       "2  3               Venusaur  Grass  Poison    525  80      82       83   \n",
+       "3  3  VenusaurMega Venusaur  Grass  Poison    625  80     100      123   \n",
+       "\n",
+       "   Sp. Atk  Sp. Def  Speed  Generation  Legendary  \n",
+       "0       65       65     45           1      False  \n",
+       "1       80       80     60           1      False  \n",
+       "2      100      100     80           1      False  \n",
+       "3      122      120     80           1      False  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here:\n",
-    "\n"
+    "# Your code here\n",
+    "pokemon.head(4)\n"
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 4,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(800, 13)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "The first thing we would like to do is compare the legendary Pokemon to the regular Pokemon. To do this, we should examine the data further. What is the count of legendary vs. non legendary Pokemons?"
+    "pokemon.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "#               0\n",
+       "Name            0\n",
+       "Type 1          0\n",
+       "Type 2        386\n",
+       "Total           0\n",
+       "HP              0\n",
+       "Attack          0\n",
+       "Defense         0\n",
+       "Sp. Atk         0\n",
+       "Sp. Def         0\n",
+       "Speed           0\n",
+       "Generation      0\n",
+       "Legendary       0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pokemon.isnull().sum()"
    ]
   },
   {
@@ -73,9 +247,44 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The first thing we would like to do is compare the legendary Pokemon to the regular Pokemon. To do this, we should examine the data further. What is the count of legendary vs. non legendary Pokemons?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Legendary\n",
+       "False    735\n",
+       "True      65\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Your code here:\n",
-    "\n"
+    "pokemon[\"Legendary\"].value_counts()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- There are 65 Legendary pokemon and 735 non-legendary pokemon"
    ]
   },
   {
@@ -87,12 +296,137 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "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>mean</th>\n",
+       "      <th>std</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Legendary</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>False</th>\n",
+       "      <td>417.213605</td>\n",
+       "      <td>106.760417</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>True</th>\n",
+       "      <td>637.384615</td>\n",
+       "      <td>60.937389</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                 mean         std\n",
+       "Legendary                        \n",
+       "False      417.213605  106.760417\n",
+       "True       637.384615   60.937389"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Your code here:\n",
-    "\n"
+    "pokemon.groupby(by=[\"Legendary\"])[\"Total\"].agg([\"mean\", \"std\"])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_43187/23249121.py:2: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
+      "  legend_total = pokemon.groupby(by=[\"Legendary\"]).get_group(True)[\"Total\"]\n",
+      "/tmp/ipykernel_43187/23249121.py:3: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
+      "  non_legend_total = pokemon.groupby(by=[\"Legendary\"]).get_group(False)[\"Total\"]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# total point distribution of legendary pokemons\n",
+    "legend_total = pokemon.groupby(by=[\"Legendary\"]).get_group(True)[\"Total\"]\n",
+    "non_legend_total = pokemon.groupby(by=[\"Legendary\"]).get_group(False)[\"Total\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Legendary\n",
+    "sns.histplot(x=legend_total, bins=20)\n",
+    "plt.title(\"Total point distribution of legendary pokemons\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Non Legendary\n",
+    "sns.histplot(x=non_legend_total, bins=30)\n",
+    "plt.title(\"Total point distribution of non legendary pokemons\")\n",
+    "plt.show()"
    ]
   },
   {
@@ -106,12 +440,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Statistic (t): 25.8336\n",
+      "P-Value: 0.0000\n",
+      "Reject the Null Hypothesis: There is \n",
+      "    sufficient evidence to conclude that the mean \n",
+      "    total scores for legendary and non-legendary pokemons \n",
+      "    are significantly different\n"
+     ]
+    }
+   ],
    "source": [
     "# Your code here:\n",
-    "\n"
+    "t_stat, p_value = sts.ttest_ind(legend_total, non_legend_total, equal_var=False)\n",
+    "print(f\"Test Statistic (t): {t_stat:.4f}\")\n",
+    "print(f\"P-Value: {p_value:.4f}\")\n",
+    "alpha = 0.05 # significance level\n",
+    "# Decision-Making\n",
+    "if p_value > alpha:\n",
+    "    print('''Fail to reject the Null Hypothesis: The mean \n",
+    "    total scores for legendary and non-legendary pokemons \n",
+    "    are not significantly different.''')\n",
+    "else:\n",
+    "    print('''Reject the Null Hypothesis: There is \n",
+    "    sufficient evidence to conclude that the mean \n",
+    "    total scores for legendary and non-legendary pokemons \n",
+    "    are significantly different''')"
    ]
   },
   {
@@ -122,13 +482,11 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# Your conclusions here:\n",
-    "\n"
+    "So the mean total points between two different \n",
+    "types of pokemons are significantly different. \n"
    ]
   },
   {
@@ -140,12 +498,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Type 1\n",
+       "Water       112\n",
+       "Normal       98\n",
+       "Grass        70\n",
+       "Bug          69\n",
+       "Psychic      57\n",
+       "Fire         52\n",
+       "Rock         44\n",
+       "Electric     44\n",
+       "Ground       32\n",
+       "Ghost        32\n",
+       "Dragon       32\n",
+       "Dark         31\n",
+       "Poison       28\n",
+       "Fighting     27\n",
+       "Steel        27\n",
+       "Ice          24\n",
+       "Fairy        17\n",
+       "Flying        4\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Your code here:\n",
-    "\n"
+    "pokemon[\"Type 1\"].value_counts()"
    ]
   },
   {
@@ -157,12 +545,78 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_43187/1370913403.py:2: FutureWarning: When grouping with a length-1 list-like, you will need to pass a length-1 tuple to get_group in a future version of pandas. Pass `(name,)` instead of `name` to silence this warning.\n",
+      "  water_pokemon = pokemon.groupby(by=[\"Type 1\"]).get_group(\"Water\")\n"
+     ]
+    }
+   ],
    "source": [
     "# Your code here:\n",
-    "\n"
+    "water_pokemon = pokemon.groupby(by=[\"Type 1\"]).get_group(\"Water\")\n",
+    "non_water_pokemon = pokemon[pokemon[\"Type 1\"] != \"Water\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    112.000000\n",
+       "mean     430.455357\n",
+       "std      113.188266\n",
+       "min      200.000000\n",
+       "25%      328.750000\n",
+       "50%      455.000000\n",
+       "75%      502.250000\n",
+       "max      770.000000\n",
+       "Name: Total, dtype: float64"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "water_pokemon[\"Total\"].describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "count    688.000000\n",
+       "mean     435.859012\n",
+       "std      121.091682\n",
+       "min      180.000000\n",
+       "25%      330.000000\n",
+       "50%      450.000000\n",
+       "75%      515.000000\n",
+       "max      780.000000\n",
+       "Name: Total, dtype: float64"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "non_water_pokemon[\"Total\"].describe()"
    ]
   },
   {
@@ -172,14 +626,82 @@
     "Perform a hypothesis test comparing the mean of total points for water Pokemon to all non-water Pokemon. Assume the variances are equal. "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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ooKBAO3fulDFG/fv31+WXX97W8wEAADSrVVdm1qxZo/79+ysQCEiSrrjiCs2YMUP33nuvhg0bpvPOO08fffRRuwwKAADQmFbFzLx583TXXXcpLi6uwbH4+HhNmTJFc+fObbPhAAAATqRVMfPPf/5TV111VZPHx40bp6KiopMeCgAAoKVaFTPffPNNo3+SfVxUVJT++9//nvRQAAAALdWqmDn77LNVXFzc5PFt27bJ7/ef9FAAAAAt1aqYufrqq/X73/9e3333XYNjtbW1mj17tq699to2Gw4AAOBEWvWn2b/73e+0dOlS9e7dW9OnT1efPn3k8XhUUlKil156SceOHdOjjz7aXrMCAAA00KqYSU5O1saNG3X33Xdr1qxZMsZIkjwej6688kq9/PLLSk5ObpdBAQAAGtPqN81LT0/XBx98oP379+uLL76QMUa9evVSt27d2mM+AACAZkX0DsCS1K1bNw0bNqwtZwEAAGi1iD6bCQAAwC2IGQAAYDViBgAAWI2YAQAAViNmAACA1YgZAABgNWIGAABYjZgBAABWI2YAAIDViBkAAGC1iD/OAPYqKytTdXW102O0WElJidMjAABcjJj5iSkrK1Pfvv1UW3vE6VFa7ftgndMjAABciJj5iamurlZt7RENv2O24vwZTo/TIhXFH2v78ld19OhRp0cBALgQMfMTFefPUEJaH6fHaJFAxR6nRwAAuBg3AAMAAKsRMwAAwGrEDAAAsBoxAwAArEbMAAAAqxEzAADAasQMAACwGjEDAACsxpvmAe3Ips+VsmlWAPghYgZoB7U1+yR5NGnSJKdHaTU+AwuAbYgZoB18f+SgJKMLbn1Y3TP7Oj1Oi/AZWABsRcwA7ahLUhqfgQUA7YwbgAEAgNWIGQAAYDViBgAAWI2YAQAAViNmAACA1YgZAABgNUdjZv369Ro/frxSUlLk8Xj07rvvhh03xig3N1cpKSmKiYnRmDFjtGPHDmeGBQAAruRozBw+fFiDBg3S/PnzGz3+zDPPaO7cuZo/f742b94sn8+nK664QgcPHuzgSQEAgFs5+qZ5WVlZysrKavSYMUbz5s3To48+qokTJ0qSFi5cqOTkZC1evFhTpkzpyFEBAIBLufaemdLSUlVWVmrcuHGhfV6vV5dccok2btzY5POCwaACgUDYAwAAnLpcGzOVlZWSpOTk5LD9ycnJoWONycvLU3x8fOiRmprarnMCAABnuTZmjvN4PGHbxpgG+35o1qxZqqmpCT3Ky8vbe0QAAOAg137QpM/nk/S/KzR+vz+0v6qqqsHVmh/yer3yer3tPh8AAHAH116ZyczMlM/nU0FBQWhfXV2dCgsLNXLkSAcnAwAAbuLolZlDhw7piy++CG2XlpZq69atSkhIUFpamu677z7NmTNHvXr1Uq9evTRnzhx17txZt956q4NTAwAAN3E0Zj777DONHTs2tJ2TkyNJmjx5st544w099NBDqq2t1T333KP9+/dr+PDhWrVqlWJjY50aGQAAuIyjMTNmzBgZY5o87vF4lJubq9zc3I4bCgAAWMW198wAAAC0BDEDAACsRswAAACrETMAAMBqxAwAALAaMQMAAKxGzAAAAKsRMwAAwGrEDAAAsBoxAwAArEbMAAAAqxEzAADAasQMAACwGjEDAACsRswAAACrETMAAMBqxAwAALAaMQMAAKxGzAAAAKsRMwAAwGrEDAAAsBoxAwAArEbMAAAAqxEzAADAasQMAACwGjEDAACsRswAAACrETMAAMBqxAwAALAaMQMAAKxGzAAAAKsRMwAAwGrEDAAAsBoxAwAArEbMAAAAqxEzAADAasQMAACwGjEDAACsRswAAACrETMAAMBqxAwAALAaMQMAAKxGzAAAAKsRMwAAwGrEDAAAsBoxAwAArEbMAAAAqxEzAADAasQMAACwGjEDAACsRswAAACrETMAAMBqro6Z3NxceTyesIfP53N6LAAA4CJRTg9wIuedd55Wr14d2j799NMdnAYAALiN62MmKiqKqzEAAKBJro+Z3bt3KyUlRV6vV8OHD9ecOXN0zjnnNHl+MBhUMBgMbQcCgXadr6ysTNXV1e36NdpSSUmJ0yMAkH3/W0xMTFRaWprTYwCNcnXMDB8+XIsWLVLv3r31zTff6Mknn9TIkSO1Y8cOnXXWWY0+Jy8vT48//niHzFdWVqa+ffuptvZIh3y9tvR9sM7pEYCfpNqafZI8mjRpktOjtEpMTGft3FlC0MCVXB0zWVlZoX8PHDhQI0aMUM+ePbVw4ULl5OQ0+pxZs2aFHQsEAkpNTW2X+aqrq1Vbe0TD75itOH9Gu3yNtlZR/LG2L39VR48edXoU4Cfp+yMHJRldcOvD6p7Z1+lxWiRQsUefvP64qquriRm4kqtj5sfOPPNMDRw4ULt3727yHK/XK6/X24FTSXH+DCWk9enQrxmpQMUep0cAIKlLUpo1/90A3M7Vf5r9Y8FgUCUlJfL7/U6PAgAAXMLVMfPAAw+osLBQpaWl+uSTT3TDDTcoEAho8uTJTo8GAABcwtW/Zvrqq690yy23qLq6Wt27d9dFF12kTZs2KT093enRAACAS7g6ZpYsWeL0CAAAwOVc/WsmAACAEyFmAACA1YgZAABgNWIGAABYzdU3AAMA3IPPk4JbETMAgGbxeVJwO2IGANAsPk8KbkfMAABahM+TgltxAzAAALAaMQMAAKxGzAAAAKsRMwAAwGrEDAAAsBoxAwAArEbMAAAAqxEzAADAasQMAACwGjEDAACsRswAAACrETMAAMBqxAwAALAaMQMAAKxGzAAAAKsRMwAAwGrEDAAAsBoxAwAArEbMAAAAqxEzAADAasQMAACwGjEDAACsFuX0AAAAtJeSkhKnR2iVxMREpaWlOT2GdYgZAMApp7ZmnySPJk2a5PQorRIT01k7d5YQNK1EzAAATjnfHzkoyeiCWx9W98y+To/TIoGKPfrk9cdVXV1NzLQSMQMAOGV1SUpTQlofp8dAO+MGYAAAYDViBgAAWI2YAQAAViNmAACA1YgZAABgNWIGAABYjZgBAABWI2YAAIDVeNM8AABchM+Taj1iBgAAF+DzpCJHzAAA4AJ8nlTkiBkAAFyEz5NqPW4ABgAAViNmAACA1YgZAABgNWIGAABYjZgBAABWI2YAAIDVrIiZl19+WZmZmTrjjDM0ZMgQffTRR06PBAAAXML1MfP222/rvvvu06OPPqrPP/9c//d//6esrCyVlZU5PRoAAHAB18fM3Llz9etf/1p33nmn+vXrp3nz5ik1NVULFixwejQAAOACrn4H4Lq6OhUVFem3v/1t2P5x48Zp48aNjT4nGAwqGAyGtmtqaiRJgUCgzec7dOiQJOnb/+zS0WBtm79+ewhU/EeSVLN3t6KjPA5P0zLM3DGYuWMwc8dg5o4RqPzfb0kOHTrU5v8/e/z1jDEnPtm42N69e40k849//CNs/1NPPWV69+7d6HNmz55tJPHgwYMHDx48ToFHeXn5CXvB1VdmjvN4wgvVGNNg33GzZs1STk5OaLu+vl7ffvutzjrrrCafE6lAIKDU1FSVl5crLi6uTV/7VMfaRY61ixxrFznWLnKsXWSMMTp48KBSUlJOeK6rYyYxMVGnn366Kisrw/ZXVVUpOTm50ed4vV55vd6wfV27dm2vESVJcXFx/IBGiLWLHGsXOdYucqxd5Fi71ouPj2/Rea6+AbhTp04aMmSICgoKwvYXFBRo5MiRDk0FAADcxNVXZiQpJydHt912m4YOHaoRI0bo1VdfVVlZmaZOner0aAAAwAVcHzM33XST9u3bpyeeeEIVFRUaMGCAPvjgA6Wnpzs9mrxer2bPnt3g11o4MdYucqxd5Fi7yLF2kWPt2p/HmJb8zRMAAIA7ufqeGQAAgBMhZgAAgNWIGQAAYDViBgAAWI2Y+YG8vDwNGzZMsbGxSkpK0oQJE7Rr166wc4wxys3NVUpKimJiYjRmzBjt2LEj7JxgMKgZM2YoMTFRZ555pn72s5/pq6++6shvpcMtWLBA559/fuhNoUaMGKG///3voeOsW8vl5eXJ4/HovvvuC+1j/ZqWm5srj8cT9vD5fKHjrF3z9u7dq0mTJumss85S586ddcEFF6ioqCh0nPVrXEZGRoOfO4/Ho2nTpkli3TrcSXx00innyiuvNPn5+Wb79u1m69at5pprrjFpaWnm0KFDoXOefvppExsba9555x1TXFxsbrrpJuP3+00gEAidM3XqVHP22WebgoICs2XLFjN27FgzaNAgc/ToUSe+rQ6xfPly8/7775tdu3aZXbt2mUceecRER0eb7du3G2NYt5b69NNPTUZGhjn//PPNzJkzQ/tZv6bNnj3bnHfeeaaioiL0qKqqCh1n7Zr27bffmvT0dJOdnW0++eQTU1paalavXm2++OKL0DmsX+OqqqrCfuYKCgqMJLN27VpjDOvW0YiZZlRVVRlJprCw0BhjTH19vfH5fObpp58OnfPdd9+Z+Ph488orrxhjjDlw4ICJjo42S5YsCZ2zd+9ec9ppp5kVK1Z07DfgsG7dupk///nPrFsLHTx40PTq1csUFBSYSy65JBQzrF/zZs+ebQYNGtToMdaueQ8//LAZPXp0k8dZv5abOXOm6dmzp6mvr2fdHMCvmZpRU1MjSUpISJAklZaWqrKyUuPGjQud4/V6dckll2jjxo2SpKKiIn3//fdh56SkpGjAgAGhc051x44d05IlS3T48GGNGDGCdWuhadOm6ZprrtHll18etp/1O7Hdu3crJSVFmZmZuvnmm/Xll19KYu1OZPny5Ro6dKhuvPFGJSUlafDgwXrttddCx1m/lqmrq9Obb76pO+64Qx6Ph3VzADHTBGOMcnJyNHr0aA0YMECSQh94+eMPuUxOTg4dq6ysVKdOndStW7cmzzlVFRcXq0uXLvJ6vZo6daqWLVum/v37s24tsGTJEm3ZskV5eXkNjrF+zRs+fLgWLVqklStX6rXXXlNlZaVGjhypffv2sXYn8OWXX2rBggXq1auXVq5cqalTp+ree+/VokWLJPGz11LvvvuuDhw4oOzsbEmsmxNc/3EGTpk+fbq2bdumDRs2NDjm8XjCto0xDfb9WEvOsV2fPn20detWHThwQO+8844mT56swsLC0HHWrXHl5eWaOXOmVq1apTPOOKPJ81i/xmVlZYX+PXDgQI0YMUI9e/bUwoULddFFF0li7ZpSX1+voUOHas6cOZKkwYMHa8eOHVqwYIF+9atfhc5j/Zr3l7/8RVlZWUpJSQnbz7p1HK7MNGLGjBlavny51q5dqx49eoT2H/8LiR9Xc1VVVajAfT6f6urqtH///ibPOVV16tRJ5557roYOHaq8vDwNGjRIL774Iut2AkVFRaqqqtKQIUMUFRWlqKgoFRYW6g9/+IOioqJC3z/r1zJnnnmmBg4cqN27d/OzdwJ+v1/9+/cP29evXz+VlZVJ4r95LfGf//xHq1ev1p133hnax7p1PGLmB4wxmj59upYuXao1a9YoMzMz7HhmZqZ8Pp8KCgpC++rq6lRYWKiRI0dKkoYMGaLo6OiwcyoqKrR9+/bQOT8VxhgFg0HW7QQuu+wyFRcXa+vWraHH0KFD9ctf/lJbt27VOeecw/q1QjAYVElJifx+Pz97JzBq1KgGbz/xr3/9K/RBvqzfieXn5yspKUnXXHNNaB/r5gAHbjp2rbvvvtvEx8ebdevWhf3J3ZEjR0LnPP300yY+Pt4sXbrUFBcXm1tuuaXRP7fr0aOHWb16tdmyZYu59NJLT/k/t5s1a5ZZv369KS0tNdu2bTOPPPKIOe2008yqVauMMaxba/3wr5mMYf2ac//995t169aZL7/80mzatMlce+21JjY21uzZs8cYw9o159NPPzVRUVHmqaeeMrt37zZvvfWW6dy5s3nzzTdD57B+TTt27JhJS0szDz/8cINjrFvHImZ+QFKjj/z8/NA59fX1Zvbs2cbn8xmv12suvvhiU1xcHPY6tbW1Zvr06SYhIcHExMSYa6+91pSVlXXwd9Ox7rjjDpOenm46depkunfvbi677LJQyBjDurXWj2OG9Wva8ffviI6ONikpKWbixIlmx44doeOsXfPee+89M2DAAOP1ek3fvn3Nq6++Gnac9WvaypUrjSSza9euBsdYt47lMcYYBy8MAQAAnBTumQEAAFYjZgAAgNWIGQAAYDViBgAAWI2YAQAAViNmAACA1YgZAABgNWIGAABYjZgB8JOUkZGhefPmOT0GgDZAzABwlMfjafaRnZ19wue/++67HTIrAHeKcnoAAD9tFRUVoX+//fbb+v3vfx/2Sc4xMTFOjAXAIlyZAeAon88XesTHx8vj8YTtW7x4sXr27KlOnTqpT58++utf/xp6bkZGhiTpuuuuk8fjCW3/+9//1s9//nMlJyerS5cuGjZsmFavXu3AdwegIxAzAFxr2bJlmjlzpu6//35t375dU6ZM0e233661a9dKkjZv3ixJys/PV0VFRWj70KFDuvrqq7V69Wp9/vnnuvLKKzV+/HiVlZU59r0AaD/8mgmAaz333HPKzs7WPffcI0nKycnRpk2b9Nxzz2ns2LHq3r27JKlr167y+Xyh5w0aNEiDBg0KbT/55JNatmyZli9frunTp3fsNwGg3XFlBoBrlZSUaNSoUWH7Ro0apZKSkmafd/jwYT300EPq37+/unbtqi5dumjnzp1cmQFOUVyZAeBqHo8nbNsY02Dfjz344INauXKlnnvuOZ177rmKiYnRDTfcoLq6uvYcFYBDuDIDwLX69eunDRs2hO3buHGj+vXrF9qOjo7WsWPHws756KOPlJ2dreuuu04DBw6Uz+fTnj17OmJkAA7gygwA13rwwQf1i1/8QhdeeKEuu+wyvffee1q6dGnYXyZlZGToww8/1KhRo+T1etWtWzede+65Wrp0qcaPHy+Px6PHHntM9fX1Dn4nANoTV2YAuNaECRP04osv6tlnn9V5552nP/3pT8rPz9eYMWNC5zz//PMqKChQamqqBg8eLEl64YUX1K1bN40cOVLjx4/XlVdeqQsvvNCh7wJAe/MYY4zTQwAAAESKKzMAAMBqxAwAALAaMQMAAKxGzAAAAKsRMwAAwGrEDAAAsBoxAwAArEbMAAAAqxEzAADAasQMAACwGjEDAACs9v8QlggRwtzmygAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(water_pokemon[\"Total\"], bins=10)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(non_water_pokemon[\"Total\"], bins=10)\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Statistic (t): -0.4639\n",
+      "P-Value: 0.6434\n",
+      "Fail to reject the Null Hypothesis: The mean \n",
+      "    total scores for water pokemons and non-water pokemons \n",
+      "    are not significantly different.\n"
+     ]
+    }
+   ],
    "source": [
     "# Your code here:\n",
-    "\n"
+    "t_stat_2, p_value_2 = sts.ttest_ind(water_pokemon[\"Total\"], non_water_pokemon\n",
+    "[\"Total\"], equal_var=False)\n",
+    "print(f\"Test Statistic (t): {t_stat_2:.4f}\")\n",
+    "print(f\"P-Value: {p_value_2:.4f}\")\n",
+    "alpha = 0.05 # significance level\n",
+    "# Decision-Making\n",
+    "if p_value_2 > alpha:\n",
+    "    print('''Fail to reject the Null Hypothesis: The mean \n",
+    "    total scores for water pokemons and non-water pokemons \n",
+    "    are not significantly different.''')\n",
+    "else:\n",
+    "    print('''Reject the Null Hypothesis: There is \n",
+    "    sufficient evidence to conclude that the mean \n",
+    "    total scores for water pokemons and non-water pokemons \n",
+    "    are significantly different.''')"
    ]
   },
   {
@@ -190,13 +712,11 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# Your conclusions here:\n",
-    "\n"
+    "- The mean total scores for water pokemons and non-water pokemons \n",
+    "are not significantly different.\n"
    ]
   },
   {
@@ -210,12 +730,121 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 49,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(79.00125)"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# Your code here:\n",
-    "\n"
+    "pokemon[\"Attack\"].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(73.8425)"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pokemon[\"Defense\"].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(x=pokemon[\"Attack\"])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(x=pokemon[\"Defense\"])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Statistic (t): 4.3256\n",
+      "P-Value: 0.00002\n",
+      "Reject the Null Hypothesis: There is \n",
+      "    sufficient evidence to conclude that the mean \n",
+      "    values for attack and defense\n",
+      "    are significantly different.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Your code here\n",
+    "# Perform paired t-test\n",
+    "t_stat_3, p_value_3 = sts.ttest_rel(pokemon[\"Attack\"], pokemon[\"Defense\"])\n",
+    "print(f\"Test Statistic (t): {t_stat_3:.4f}\")\n",
+    "print(f\"P-Value: {p_value_3:.5f}\")\n",
+    "\n",
+    "# Decision-Making\n",
+    "if p_value_3 > alpha:\n",
+    "    print('''Fail to reject the Null Hypothesis: The mean \n",
+    "    values for attack and defense\n",
+    "    are not significantly different.''')\n",
+    "else:\n",
+    "    print('''Reject the Null Hypothesis: There is \n",
+    "    sufficient evidence to conclude that the mean \n",
+    "    values for attack and defense\n",
+    "    are significantly different.''')\n"
    ]
   },
   {
@@ -226,13 +855,12 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# Your conclusions here:\n",
-    "\n"
+    "- the mean \n",
+    "    values for attack and defense\n",
+    "    are significantly different.\n"
    ]
   },
   {
@@ -244,12 +872,80 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 52,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Your code here:\n",
-    "\n"
+    "sns.histplot(x=pokemon[\"Sp. Atk\"])\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.histplot(x=pokemon[\"Sp. Def\"])\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Statistic (t): 0.8540\n",
+      "P-Value: 0.39337\n",
+      "Fail to reject the Null Hypothesis: The mean \n",
+      "    values for special attack and special defense\n",
+      "    are not significantly different.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Perform paired t-test\n",
+    "t_stat_4, p_value_4 = sts.ttest_rel(pokemon[\"Sp. Atk\"], pokemon[\"Sp. Def\"])\n",
+    "print(f\"Test Statistic (t): {t_stat_4:.4f}\")\n",
+    "print(f\"P-Value: {p_value_4:.5f}\")\n",
+    "\n",
+    "# Decision-Making\n",
+    "if p_value_4 > alpha:\n",
+    "    print('''Fail to reject the Null Hypothesis: The mean \n",
+    "    values for special attack and special defense\n",
+    "    are not significantly different.''')\n",
+    "else:\n",
+    "    print('''Reject the Null Hypothesis: There is \n",
+    "    sufficient evidence to conclude that the mean \n",
+    "    values for special attack and special defense\n",
+    "    are significantly different.''')"
    ]
   },
   {
@@ -260,13 +956,12 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# Your conclusions here:\n",
-    "\n"
+    "- The mean \n",
+    "    values for special attack and special defense\n",
+    "    are significantly different."
    ]
   },
   {
@@ -280,15 +975,98 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Your code here:\n",
-    "    \n",
+    "difference = pokemon[\"Defense\"] - pokemon[\"Attack\"]\n",
+    "mu = 0\n",
     "    "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# distribution\n",
+    "sns.histplot(x=difference)\n",
+    "plt.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Statistic (t): -4.3256\n",
+      "P-Value: 0.0000\n",
+      "\n",
+      "Reject the Null Hypothesis: There is evidence to say that the \n",
+      "    difference between the means is not 0.\n"
+     ]
+    }
+   ],
+   "source": [
+    "t_stat_5, p_value_5 = sts.ttest_1samp(difference, mu)\n",
+    "print(f\"Test Statistic (t): {t_stat_5:.4f}\")\n",
+    "print(f\"P-Value: {p_value_5:.4f}\")\n",
+    "print()\n",
+    "if p_value_5 > alpha:\n",
+    "    print('''Fail to Reject the Null Hypothesis: \n",
+    "    Not enough evidence to say the difference between the means is 0.''')\n",
+    "else:\n",
+    "    print('''Reject the Null Hypothesis: There is evidence to say that the \n",
+    "    difference between the means is not 0.''')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(5.158749999999998)"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pokemon[\"Attack\"].mean() - pokemon[\"Defense\"].mean()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -302,12 +1080,139 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 67,
    "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>Type 1</th>\n",
+       "      <th>Bug</th>\n",
+       "      <th>Dark</th>\n",
+       "      <th>Dragon</th>\n",
+       "      <th>Electric</th>\n",
+       "      <th>Fairy</th>\n",
+       "      <th>Fighting</th>\n",
+       "      <th>Fire</th>\n",
+       "      <th>Flying</th>\n",
+       "      <th>Ghost</th>\n",
+       "      <th>Grass</th>\n",
+       "      <th>Ground</th>\n",
+       "      <th>Ice</th>\n",
+       "      <th>Normal</th>\n",
+       "      <th>Poison</th>\n",
+       "      <th>Psychic</th>\n",
+       "      <th>Rock</th>\n",
+       "      <th>Steel</th>\n",
+       "      <th>Water</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Legendary</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>False</th>\n",
+       "      <td>69</td>\n",
+       "      <td>29</td>\n",
+       "      <td>20</td>\n",
+       "      <td>40</td>\n",
+       "      <td>16</td>\n",
+       "      <td>27</td>\n",
+       "      <td>47</td>\n",
+       "      <td>2</td>\n",
+       "      <td>30</td>\n",
+       "      <td>67</td>\n",
+       "      <td>28</td>\n",
+       "      <td>22</td>\n",
+       "      <td>96</td>\n",
+       "      <td>28</td>\n",
+       "      <td>43</td>\n",
+       "      <td>40</td>\n",
+       "      <td>23</td>\n",
+       "      <td>108</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>True</th>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>12</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>14</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Type 1     Bug  Dark  Dragon  Electric  Fairy  Fighting  Fire  Flying  Ghost  \\\n",
+       "Legendary                                                                      \n",
+       "False       69    29      20        40     16        27    47       2     30   \n",
+       "True         0     2      12         4      1         0     5       2      2   \n",
+       "\n",
+       "Type 1     Grass  Ground  Ice  Normal  Poison  Psychic  Rock  Steel  Water  \n",
+       "Legendary                                                                   \n",
+       "False         67      28   22      96      28       43    40     23    108  \n",
+       "True           3       4    2       2       0       14     4      4      4  "
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Your code here:\n",
-    "\n"
+    "cross_table = pd.crosstab(pokemon[\"Legendary\"], pokemon[\"Type 1\"])\n",
+    "cross_table"
    ]
   },
   {
@@ -317,16 +1222,6 @@
     "Perform a chi-squared test using the `chi2_contingency` function in `scipy.stats`. You can read the documentation of the function [here](https://docs.scipy.org/doc/scipy-0.15.1/reference/generated/scipy.stats.chi2_contingency.html)."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Your code here:\n",
-    "\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -336,20 +1231,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Your answer here:\n",
-    "\n"
+    "_, p_value_6, _, _ = sts.chi2_contingency(cross_table)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 69,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(5.118547414721704e-12)"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p_value_6"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- We should reject the null hypothesis as p_value is less than 0.05"
+   ]
   }
  ],
  "metadata": {
@@ -368,7 +1283,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "version": "3.13.2"
   }
  },
  "nbformat": 4,