diff --git a/your-code/__pycache__/data_check.cpython-313.pyc b/your-code/__pycache__/data_check.cpython-313.pyc
new file mode 100644
index 0000000..1b2f965
Binary files /dev/null and b/your-code/__pycache__/data_check.cpython-313.pyc differ
diff --git a/your-code/data_check.py b/your-code/data_check.py
new file mode 100644
index 0000000..46deb24
--- /dev/null
+++ b/your-code/data_check.py
@@ -0,0 +1,87 @@
+'''This file performs an initial exploration of the data and outputs important details, 
+    such as the number of rows and columns, data types, unique values, categorical columns, 
+    and checks for null and duplicated values.'''
+
+
+def initial_chk(data_frame):
+    '''This function performs an initial exploration of the data and outputs important details.
+
+    It checks:
+        - The number of columns and rows
+        - The data types of each column
+        - The count of unique values in each column
+        - Identifies potential categorical columns (columns with < 20 unique values)
+        - Outputs the unique values count for categorical columns
+
+    Parameters:
+        - data_frame (pd.DataFrame): The input DataFrame to explore.
+    
+    Returns:
+        - None: This function prints the results directly and does not return a value.
+    '''
+       
+    # Display the number of columns and rows
+    print(f"Number of columns: {data_frame.shape[1]} and rows: {data_frame.shape[0]}")
+
+    # Show data types of each column
+    print("\nData types:")
+    print(data_frame.dtypes)
+
+    # Display the unique values count for each column
+    print("\nUnique values count:")
+    unique_values_count = data_frame.nunique()
+    print(unique_values_count)
+
+    # Identify categorical columns (those with less than 20 unique values)
+    categorical_columns = unique_values_count[unique_values_count < 20].index
+    print(f"\nThese columns appear to be categorical (less than 20 unique values):\n{categorical_columns}")
+
+    # Show the unique value count for categorical columns
+    print("\nUnique value count for categorical columns:")
+    for col in categorical_columns:
+        print(f"\n{col}:")
+        print(data_frame[col].value_counts())
+
+
+def check_null(data_frame):
+    '''
+    Check for NaN values in each column and print the total per column.
+    
+    Parameters:
+        - data_frame (pd.DataFrame): The input DataFrame to check for null values.
+    
+    Returns:
+        - None: This function prints the count of null values for each column and does not return a value.
+    '''
+
+    print("\nCount of null values:")
+    print(data_frame.isnull().sum())
+
+def check_duplicated(data_frame):
+    '''
+    Check for duplicated values in the data frame and print the total.
+    
+    Parameters:
+        - data_frame (pd.DataFrame): The input DataFrame to check for duplicates.
+    
+    Returns:
+        - None: This function prints the count of duplicated rows and does not return a value.
+    '''
+
+    print("\nCount of duplicated values:")
+    print(data_frame.duplicated().sum())
+
+def check(data_frame):
+    '''
+    A function to call all the functions for the data exploration.
+
+    Parameters:
+        - data_frame (pd.DataFrame): The input DataFrame to explore.
+    
+    Returns:
+        - None: This function calls other functions and does not return a value.
+    '''
+    
+    initial_chk(data_frame)
+    check_null(data_frame)
+    check_duplicated(data_frame)
diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index cdc1acb..de1af21 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -14,12 +14,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
     "# import numpy and pandas\n",
-    "\n"
+    "import scipy.stats as std\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import data_check as ch\n"
    ]
   },
   {
@@ -35,12 +38,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
     "# Run this code:\n",
-    "\n",
     "pokemon = pd.read_csv('../pokemon.csv')"
    ]
   },
@@ -53,12 +55,287 @@
   },
   {
    "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",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Charmander</td>\n",
+       "      <td>Fire</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>309</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",
+       "  </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",
+       "4  4             Charmander   Fire     NaN    309  39      52       43   \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  \n",
+       "4       60       50     65           1      False  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Your code here:\n",
-    "\n"
+    "pokemon.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of columns: 13 and rows: 800\n",
+      "\n",
+      "Data types:\n",
+      "#              int64\n",
+      "Name          object\n",
+      "Type 1        object\n",
+      "Type 2        object\n",
+      "Total          int64\n",
+      "HP             int64\n",
+      "Attack         int64\n",
+      "Defense        int64\n",
+      "Sp. Atk        int64\n",
+      "Sp. Def        int64\n",
+      "Speed          int64\n",
+      "Generation     int64\n",
+      "Legendary       bool\n",
+      "dtype: object\n",
+      "\n",
+      "Unique values count:\n",
+      "#             721\n",
+      "Name          800\n",
+      "Type 1         18\n",
+      "Type 2         18\n",
+      "Total         200\n",
+      "HP             94\n",
+      "Attack        111\n",
+      "Defense       103\n",
+      "Sp. Atk       105\n",
+      "Sp. Def        92\n",
+      "Speed         108\n",
+      "Generation      6\n",
+      "Legendary       2\n",
+      "dtype: int64\n",
+      "\n",
+      "These columns appear to be categorical (less than 20 unique values):\n",
+      "Index(['Type 1', 'Type 2', 'Generation', 'Legendary'], dtype='object')\n",
+      "\n",
+      "Unique value count for categorical columns:\n",
+      "\n",
+      "Type 1:\n",
+      "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\n",
+      "\n",
+      "Type 2:\n",
+      "Type 2\n",
+      "Flying      97\n",
+      "Ground      35\n",
+      "Poison      34\n",
+      "Psychic     33\n",
+      "Fighting    26\n",
+      "Grass       25\n",
+      "Fairy       23\n",
+      "Steel       22\n",
+      "Dark        20\n",
+      "Dragon      18\n",
+      "Ice         14\n",
+      "Rock        14\n",
+      "Water       14\n",
+      "Ghost       14\n",
+      "Fire        12\n",
+      "Electric     6\n",
+      "Normal       4\n",
+      "Bug          3\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Generation:\n",
+      "Generation\n",
+      "1    166\n",
+      "5    165\n",
+      "3    160\n",
+      "4    121\n",
+      "2    106\n",
+      "6     82\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Legendary:\n",
+      "Legendary\n",
+      "False    735\n",
+      "True      65\n",
+      "Name: count, dtype: int64\n",
+      "\n",
+      "Count of null values:\n",
+      "#               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\n",
+      "\n",
+      "Count of duplicated values:\n",
+      "0\n"
+     ]
+    }
+   ],
+   "source": [
+    "ch.check(pokemon)"
    ]
   },
   {
@@ -70,12 +347,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Legendary\n",
+       "False    735\n",
+       "True      65\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Your code here:\n",
-    "\n"
+    "legendary_counts = pokemon[\"Legendary\"].value_counts()\n",
+    "legendary_counts\n"
    ]
   },
   {
@@ -87,11 +379,70 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "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": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Your code here:\n",
+    "legendary_stats = pokemon.groupby(\"Legendary\")[\"Total\"].agg([\"mean\", \"std\"])\n",
+    "legendary_stats\n",
     "\n"
    ]
   },
@@ -106,12 +457,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Null Hypothesis (H₀): There is no significant difference between the means of the two groups\n",
+      "Alternate Hypothesis (H₁): There means of points between the two groups is not equal\n",
+      "Test Statistic (t): 25.83\n",
+      "P-Value: 0.000000000000000000000000000000000000000000000093579543359574\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "# Your code here:\n",
-    "\n"
+    "\n",
+    "# Hypotheses\n",
+    "null_hypothesis = \"There is no significant difference between the means of the two groups\"  # H₀\n",
+    "alt_hypothesis = \"There means of points between the two groups is not equal\"  # H₁\n",
+    "\n",
+    "print(f\"Null Hypothesis (H₀): {null_hypothesis}\")\n",
+    "print(f\"Alternate Hypothesis (H₁): {alt_hypothesis}\")\n",
+    "\n",
+    "legendary_t = pokemon[pokemon[\"Legendary\"] == True][\"Total\"]\n",
+    "legendary_f = pokemon[pokemon[\"Legendary\"] == False][\"Total\"]\n",
+    "\n",
+    "# Two-sample t-test for independent samples\n",
+    "t_stat, p_value = std.ttest_ind(legendary_t, legendary_f, equal_var=False)\n",
+    "print(f\"Test Statistic (t): {t_stat:.2f}\")\n",
+    "print(f\"P-Value: {p_value:.60f}\")\n",
+    "print()\n"
    ]
   },
   {
@@ -123,12 +501,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reject the Null Hypothesis: There is sufficient evidence to conclude that the mean fares are different for legendary and non-legendary Pokemons.\n"
+     ]
+    }
+   ],
    "source": [
     "# Your conclusions here:\n",
-    "\n"
+    "\n",
+    "# Significance level\n",
+    "alpha = 0.05\n",
+    "\n",
+    "# Decision-Making\n",
+    "if p_value > alpha:\n",
+    "    print(\"Fail to Reject the Null Hypothesis: The mean points for legendary and non-legendary Pokemons are not significantly different.\")\n",
+    "else:\n",
+    "    print(\"Reject the Null Hypothesis: There is sufficient evidence to conclude that the mean fares are different for legendary and non-legendary Pokemons.\")\n"
    ]
   },
   {
@@ -140,12 +534,44 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "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": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Your code here:\n",
-    "\n"
+    "\n",
+    "type_counts = pokemon[\"Type 1\"].value_counts()\n",
+    "type_counts"
    ]
   },
   {
@@ -157,12 +583,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Water Pokémon Mean: 430.46 and Std Dev: 113.19\n",
+      "Non-Water Pokémon Mean: 435.86 and Std Dev: 121.09\n"
+     ]
+    }
+   ],
    "source": [
     "# Your code here:\n",
-    "\n"
+    "\n",
+    "water_pokemon = pokemon[pokemon[\"Type 1\"] == \"Water\"][\"Total\"]\n",
+    "non_water_pokemon = pokemon[pokemon[\"Type 1\"] != \"Water\"][\"Total\"]\n",
+    "\n",
+    "water_mean = water_pokemon.mean()\n",
+    "water_std = water_pokemon.std()\n",
+    "\n",
+    "non_water_mean = non_water_pokemon.mean()\n",
+    "non_water_std = non_water_pokemon.std()\n",
+    "\n",
+    "print(f\"Water Pokémon Mean: {water_mean:.2f} and Std Dev: {water_std:.2f}\")\n",
+    "print(f\"Non-Water Pokémon Mean: {non_water_mean:.2f} and Std Dev: {non_water_std:.2f}\")\n"
    ]
   },
   {
@@ -174,12 +620,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Null Hypothesis (H₀): There is no significant difference between the means of the two groups\n",
+      "Alternate Hypothesis (H₁): There means of points between the two groups is not equal\n",
+      "Test Statistic (t): -0.44\n",
+      "P-Value: 0.66\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "# Your code here:\n",
-    "\n"
+    "\n",
+    "# Hypotheses\n",
+    "null_hypothesis = \"There is no significant difference between the means of the two groups\"  # H₀\n",
+    "alt_hypothesis = \"There means of points between the two groups is not equal\"  # H₁\n",
+    "\n",
+    "print(f\"Null Hypothesis (H₀): {null_hypothesis}\")\n",
+    "print(f\"Alternate Hypothesis (H₁): {alt_hypothesis}\")\n",
+    "\n",
+    "# Two-sample t-test for independent samples\n",
+    "t_stat, p_value = std.ttest_ind(water_pokemon, non_water_pokemon, equal_var=True)\n",
+    "print(f\"Test Statistic (t): {t_stat:.2f}\")\n",
+    "print(f\"P-Value: {p_value:.2f}\")\n",
+    "print()"
    ]
   },
   {
@@ -191,12 +661,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fail to Reject the Null Hypothesis: The mean points for water and non-water Pokemons are not significantly different.\n"
+     ]
+    }
+   ],
    "source": [
     "# Your conclusions here:\n",
-    "\n"
+    "\n",
+    "# Decision-Making\n",
+    "if p_value > alpha:\n",
+    "    print(\"Fail to Reject the Null Hypothesis: The mean points for water and non-water Pokemons are not significantly different.\")\n",
+    "else:\n",
+    "    print(\"Reject the Null Hypothesis: There is sufficient evidence to conclude that the mean fares are different for water and non-water Pokemons.\")"
    ]
   },
   {
@@ -210,11 +693,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Null Hypothesis (H₀): There is no significant difference between Attack and Defense scores\n",
+      "Alternate Hypothesis (H₁): There is a significant difference between Attack and Defense scores\n",
+      "T-statistic: 4.3256\n",
+      "P-value: 0.0000171403\n"
+     ]
+    }
+   ],
    "source": [
     "# Your code here:\n",
+    "\n",
+    "# Hypotheses\n",
+    "null_hypothesis = \"There is no significant difference between Attack and Defense scores\"  # H₀\n",
+    "alt_hypothesis = \"There is a significant difference between Attack and Defense scores\"  # H₁\n",
+    "\n",
+    "print(f\"Null Hypothesis (H₀): {null_hypothesis}\")\n",
+    "print(f\"Alternate Hypothesis (H₁): {alt_hypothesis}\")\n",
+    "\n",
+    "# Two-sample t-test for dependent samples\n",
+    "t_stat, p_value = std.ttest_rel(pokemon[\"Attack\"], pokemon[\"Defense\"])\n",
+    "\n",
+    "print(f\"T-statistic: {t_stat:.4f}\")\n",
+    "print(f\"P-value: {p_value:.10f}\")\n",
     "\n"
    ]
   },
@@ -227,12 +734,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reject the null hypothesis: There is a significant difference between Attack and Defense scores.\n"
+     ]
+    }
+   ],
    "source": [
     "# Your conclusions here:\n",
-    "\n"
+    "if p_value > alpha:\n",
+    "    print(\"Fail to reject the null hypothesis: No significant difference between Attack and Defense scores.\")\n",
+    "else:\n",
+    "    print(\"Reject the null hypothesis: There is a significant difference between Attack and Defense scores.\")"
    ]
   },
   {
@@ -244,12 +762,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Null Hypothesis (H₀): The mean Special Attack and Special Defense are equal\n",
+      "Alternate Hypothesis (H₁):  The mean Special Attack and Special Defense are different\n",
+      "T-statistic: 0.8540\n",
+      "P-value: 0.3933685998\n"
+     ]
+    }
+   ],
    "source": [
     "# Your code here:\n",
-    "\n"
+    "\n",
+    "# Hypotheses\n",
+    "null_hypothesis = \"The mean Special Attack and Special Defense are equal\"  # H₀\n",
+    "alt_hypothesis = \" The mean Special Attack and Special Defense are different\"  # H₁\n",
+    "\n",
+    "print(f\"Null Hypothesis (H₀): {null_hypothesis}\")\n",
+    "print(f\"Alternate Hypothesis (H₁): {alt_hypothesis}\")\n",
+    "\n",
+    "# Two-sample t-test for dependent samples\n",
+    "t_stat, p_value = std.ttest_rel(pokemon[\"Sp. Atk\"], pokemon[\"Sp. Def\"])\n",
+    "\n",
+    "print(f\"T-statistic: {t_stat:.4f}\")\n",
+    "print(f\"P-value: {p_value:.10f}\")"
    ]
   },
   {
@@ -261,12 +802,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fail to reject the null hypothesis: No significant difference between Special Attack and Special Defense scores.\n"
+     ]
+    }
+   ],
    "source": [
     "# Your conclusions here:\n",
-    "\n"
+    "\n",
+    "if p_value > alpha:\n",
+    "    print(\"Fail to reject the null hypothesis: No significant difference between Special Attack and Special Defense scores.\")\n",
+    "else:\n",
+    "    print(\"Reject the null hypothesis: There is a significant difference between Special Attack and Special Defense scores.\")"
    ]
   },
   {
@@ -280,12 +833,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Null Hypothesis (H₀): The mean difference between Attack and Defense is zero\n",
+      "Alternate Hypothesis (H₁): The mean difference is not zero\n",
+      "T-statistic: 4.3256\n",
+      "P-value: 0.0000171403\n",
+      "Reject the null hypothesis: There is a significant difference between Attack and Defense.\n"
+     ]
+    }
+   ],
    "source": [
     "# Your code here:\n",
-    "    \n",
+    "\n",
+    "# Hypotheses\n",
+    "null_hypothesis = \"The mean difference between Attack and Defense is zero\"  # H₀\n",
+    "alt_hypothesis = \"The mean difference is not zero\"  # H₁\n",
+    "\n",
+    "print(f\"Null Hypothesis (H₀): {null_hypothesis}\")\n",
+    "print(f\"Alternate Hypothesis (H₁): {alt_hypothesis}\")\n",
+    "\n",
+    "diff = pokemon[\"Attack\"] - pokemon[\"Defense\"]\n",
+    "\n",
+    "# one-sample t-test\n",
+    "t_stat, p_value = std.ttest_1samp(diff, 0)\n",
+    "\n",
+    "print(f\"T-statistic: {t_stat:.4f}\")\n",
+    "print(f\"P-value: {p_value:.10f}\")  \n",
+    "\n",
+    "if p_value < alpha:\n",
+    "    print(\"Reject the null hypothesis: There is a significant difference between Attack and Defense.\")\n",
+    "else:\n",
+    "    print(\"Fail to reject the null hypothesis: No significant difference between Attack and Defense.\")\n",
     "    "
    ]
   },
@@ -302,12 +886,76 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "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>Water Type</th>\n",
+       "      <th>False</th>\n",
+       "      <th>True</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>627</td>\n",
+       "      <td>108</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>True</th>\n",
+       "      <td>61</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Water Type  False  True \n",
+       "Legendary               \n",
+       "False         627    108\n",
+       "True           61      4"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Your code here:\n",
-    "\n"
+    "\n",
+    "pokemon['Is_Water_Type'] = pokemon['Type 1'] == 'Water'\n",
+    "\n",
+    "# Create a contingency table comparing Legendary status and Water type\n",
+    "contingency_table = pd.crosstab(pokemon['Legendary'], pokemon['Is_Water_Type'], \n",
+    "                                rownames=['Legendary'], colnames=['Water Type'])\n",
+    "\n",
+    "contingency_table"
    ]
   },
   {
@@ -319,12 +967,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Null Hypothesis (H₀): There is no association between whether a Pokémon is legendary or not and whether its Type 1 is water or not\n",
+      "Alternate Hypothesis (H₁):  There is an association between whether a Pokémon is legendary or not and whether its Type 1 is water or not\n",
+      "Chi-squared statistic: 2.9429200762850503\n",
+      "p-value: 0.0862546724955095\n",
+      "Degrees of freedom: 1\n",
+      "Expected frequencies table:\n",
+      "[[632.1 102.9]\n",
+      " [ 55.9   9.1]]\n"
+     ]
+    }
+   ],
    "source": [
     "# Your code here:\n",
-    "\n"
+    "\n",
+    "# Hypotheses\n",
+    "null_hypothesis = \"There is no association between whether a Pokémon is legendary or not and whether its Type 1 is water or not\"  # H₀\n",
+    "alt_hypothesis = \" There is an association between whether a Pokémon is legendary or not and whether its Type 1 is water or not\"  # H₁\n",
+    "\n",
+    "print(f\"Null Hypothesis (H₀): {null_hypothesis}\")\n",
+    "print(f\"Alternate Hypothesis (H₁): {alt_hypothesis}\")\n",
+    "\n",
+    "chi2, p, dof, expected = std.chi2_contingency(contingency_table)\n",
+    "\n",
+    "print(f\"Chi-squared statistic: {chi2}\")\n",
+    "print(f\"p-value: {p}\")\n",
+    "print(f\"Degrees of freedom: {dof}\")\n",
+    "print(f\"Expected frequencies table:\\n{expected}\")"
    ]
   },
   {
@@ -336,20 +1012,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "We reject the null hypothesis: There is an association between whether a Pokémon is legendary or not and whether its Type 1 is water or not.\n"
+     ]
+    }
+   ],
    "source": [
     "# Your answer here:\n",
-    "\n"
+    "\n",
+    "if p_value > alpha:\n",
+    "    print(f\"Fail to reject the null hypothesis: There is no association between whether a Pokémon is legendary or not and whether its Type 1 is water or not.\")\n",
+    "else:\n",
+    "    print(f\"We reject the null hypothesis: There is an association between whether a Pokémon is legendary or not and whether its Type 1 is water or not.\")\n"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -368,7 +1049,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "version": "3.13.2"
   }
  },
  "nbformat": 4,