diff --git a/01_materials/notebooks/Stat_Inference.ipynb b/01_materials/notebooks/Stat_Inference.ipynb
index 5ec9eab2e..307c695aa 100644
--- a/01_materials/notebooks/Stat_Inference.ipynb
+++ b/01_materials/notebooks/Stat_Inference.ipynb
@@ -202,212 +202,7 @@
    "cell_type": "code",
    "execution_count": 6,
    "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>id</th>\n",
-       "      <th>neighbourhood</th>\n",
-       "      <th>room_type</th>\n",
-       "      <th>accommodates</th>\n",
-       "      <th>bathrooms</th>\n",
-       "      <th>bedrooms</th>\n",
-       "      <th>beds</th>\n",
-       "      <th>price</th>\n",
-       "      <th>replicate</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2148</th>\n",
-       "      <td>4.459734e+07</td>\n",
-       "      <td>Kitsilano</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>169</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>478</th>\n",
-       "      <td>1.320573e+07</td>\n",
-       "      <td>Renfrew-Collingwood</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>90</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1056</th>\n",
-       "      <td>2.456132e+07</td>\n",
-       "      <td>Victoria-Fraserview</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>120</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1959</th>\n",
-       "      <td>4.118488e+07</td>\n",
-       "      <td>West End</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>159</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3277</th>\n",
-       "      <td>6.880000e+17</td>\n",
-       "      <td>Mount Pleasant</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>246</td>\n",
-       "      <td>0</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",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1814</th>\n",
-       "      <td>3.824267e+07</td>\n",
-       "      <td>Renfrew-Collingwood</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>150</td>\n",
-       "      <td>19999</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3313</th>\n",
-       "      <td>6.940000e+17</td>\n",
-       "      <td>Downtown</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>250</td>\n",
-       "      <td>19999</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2935</th>\n",
-       "      <td>6.050000e+17</td>\n",
-       "      <td>Downtown</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>215</td>\n",
-       "      <td>19999</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4977</th>\n",
-       "      <td>9.960000e+17</td>\n",
-       "      <td>Mount Pleasant</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2600</td>\n",
-       "      <td>19999</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2814</th>\n",
-       "      <td>5.510000e+17</td>\n",
-       "      <td>Renfrew-Collingwood</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>206</td>\n",
-       "      <td>19999</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>800000 rows × 9 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                id        neighbourhood        room_type  accommodates  \\\n",
-       "2148  4.459734e+07            Kitsilano  Entire home/apt             4   \n",
-       "478   1.320573e+07  Renfrew-Collingwood  Entire home/apt             4   \n",
-       "1056  2.456132e+07  Victoria-Fraserview  Entire home/apt             3   \n",
-       "1959  4.118488e+07             West End  Entire home/apt             4   \n",
-       "3277  6.880000e+17       Mount Pleasant  Entire home/apt             2   \n",
-       "...            ...                  ...              ...           ...   \n",
-       "1814  3.824267e+07  Renfrew-Collingwood  Entire home/apt             4   \n",
-       "3313  6.940000e+17             Downtown  Entire home/apt             2   \n",
-       "2935  6.050000e+17             Downtown  Entire home/apt             4   \n",
-       "4977  9.960000e+17       Mount Pleasant  Entire home/apt             2   \n",
-       "2814  5.510000e+17  Renfrew-Collingwood  Entire home/apt             4   \n",
-       "\n",
-       "      bathrooms  bedrooms  beds  price  replicate  \n",
-       "2148        NaN       2.0   NaN    169          0  \n",
-       "478         1.0       1.0   2.0     90          0  \n",
-       "1056        1.0       1.0   2.0    120          0  \n",
-       "1959        NaN       2.0   NaN    159          0  \n",
-       "3277        1.0       1.0   1.0    246          0  \n",
-       "...         ...       ...   ...    ...        ...  \n",
-       "1814        1.0       1.0   2.0    150      19999  \n",
-       "3313        1.0       1.0   1.0    250      19999  \n",
-       "2935        1.0       1.0   2.0    215      19999  \n",
-       "4977        1.0       1.0   1.0   2600      19999  \n",
-       "2814        1.0       2.0   0.0    206      19999  \n",
-       "\n",
-       "[800000 rows x 9 columns]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Initialize an empty list to store the samples\n",
     "sample_list = []\n",
@@ -422,7 +217,7 @@
     "samples = pd.concat(sample_list)\n",
     "\n",
     "# Display the combined DataFrame\n",
-    "samples"
+    "# samples"
    ]
   },
   {
@@ -451,115 +246,7 @@
    "cell_type": "code",
    "execution_count": 7,
    "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>replicate</th>\n",
-       "      <th>sample_mean</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>191.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>232.400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>243.025</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>202.800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>190.975</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19995</th>\n",
-       "      <td>19995</td>\n",
-       "      <td>265.250</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19996</th>\n",
-       "      <td>19996</td>\n",
-       "      <td>248.900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19997</th>\n",
-       "      <td>19997</td>\n",
-       "      <td>283.475</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19998</th>\n",
-       "      <td>19998</td>\n",
-       "      <td>226.475</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19999</th>\n",
-       "      <td>19999</td>\n",
-       "      <td>267.875</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>20000 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       replicate  sample_mean\n",
-       "0              0      191.000\n",
-       "1              1      232.400\n",
-       "2              2      243.025\n",
-       "3              3      202.800\n",
-       "4              4      190.975\n",
-       "...          ...          ...\n",
-       "19995      19995      265.250\n",
-       "19996      19996      248.900\n",
-       "19997      19997      283.475\n",
-       "19998      19998      226.475\n",
-       "19999      19999      267.875\n",
-       "\n",
-       "[20000 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "sample_estimates = (\n",
     "     samples\n",
@@ -569,7 +256,7 @@
     "    .reset_index(name=\"sample_mean\")\n",
     ")\n",
     "\n",
-    "sample_estimates\n"
+    "# sample_estimates"
    ]
   },
   {
@@ -588,7 +275,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -621,7 +308,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -630,13 +317,13 @@
        "248.71526"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "sample_estimates[\"sample_mean\"].mean()\n"
+    "sample_estimates[\"sample_mean\"].mean()"
    ]
   },
   {
@@ -701,580 +388,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "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>id</th>\n",
-       "      <th>neighbourhood</th>\n",
-       "      <th>room_type</th>\n",
-       "      <th>accommodates</th>\n",
-       "      <th>bathrooms</th>\n",
-       "      <th>bedrooms</th>\n",
-       "      <th>beds</th>\n",
-       "      <th>price</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2912</th>\n",
-       "      <td>5.970000e+17</td>\n",
-       "      <td>Riley Park</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>9</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>214</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>787</th>\n",
-       "      <td>1.986261e+07</td>\n",
-       "      <td>Mount Pleasant</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>9</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>109</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2696</th>\n",
-       "      <td>5.333958e+07</td>\n",
-       "      <td>Arbutus Ridge</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3017</th>\n",
-       "      <td>6.310000e+17</td>\n",
-       "      <td>West End</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>223</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1226</th>\n",
-       "      <td>2.745958e+07</td>\n",
-       "      <td>Downtown</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>1</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>126</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2620</th>\n",
-       "      <td>5.255417e+07</td>\n",
-       "      <td>Downtown Eastside</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>198</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4883</th>\n",
-       "      <td>9.790000e+17</td>\n",
-       "      <td>Downtown Eastside</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>7</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>834</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>608</th>\n",
-       "      <td>1.554257e+07</td>\n",
-       "      <td>Kitsilano</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>99</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1400</th>\n",
-       "      <td>3.124019e+07</td>\n",
-       "      <td>Arbutus Ridge</td>\n",
-       "      <td>Private room</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>133</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>149</th>\n",
-       "      <td>3.100878e+06</td>\n",
-       "      <td>Downtown</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>60</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3599</th>\n",
-       "      <td>7.590000e+17</td>\n",
-       "      <td>Kensington-Cedar Cottage</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>8</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>273</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1066</th>\n",
-       "      <td>2.475420e+07</td>\n",
-       "      <td>Kitsilano</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>120</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>361</th>\n",
-       "      <td>8.429321e+06</td>\n",
-       "      <td>Fairview</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>79</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>984</th>\n",
-       "      <td>2.341930e+07</td>\n",
-       "      <td>Downtown</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>117</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3851</th>\n",
-       "      <td>8.160000e+17</td>\n",
-       "      <td>Hastings-Sunrise</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3560</th>\n",
-       "      <td>7.510000e+17</td>\n",
-       "      <td>Kitsilano</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>269</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2692</th>\n",
-       "      <td>5.331242e+07</td>\n",
-       "      <td>Strathcona</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>988</th>\n",
-       "      <td>2.348250e+07</td>\n",
-       "      <td>Fairview</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4777</th>\n",
-       "      <td>9.650000e+17</td>\n",
-       "      <td>Renfrew-Collingwood</td>\n",
-       "      <td>Private room</td>\n",
-       "      <td>5</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>640</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4419</th>\n",
-       "      <td>9.120000e+17</td>\n",
-       "      <td>Downtown</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>409</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1465</th>\n",
-       "      <td>3.287258e+07</td>\n",
-       "      <td>Kitsilano</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>136</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>548</th>\n",
-       "      <td>1.455531e+07</td>\n",
-       "      <td>Grandview-Woodland</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>95</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1594</th>\n",
-       "      <td>3.490318e+07</td>\n",
-       "      <td>West End</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>141</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3076</th>\n",
-       "      <td>6.440000e+17</td>\n",
-       "      <td>Kensington-Cedar Cottage</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>227</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>273</th>\n",
-       "      <td>6.315732e+06</td>\n",
-       "      <td>Fairview</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>70</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>142</th>\n",
-       "      <td>2.988185e+06</td>\n",
-       "      <td>West End</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>59</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4601</th>\n",
-       "      <td>9.360000e+17</td>\n",
-       "      <td>Downtown Eastside</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>4</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>495</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3795</th>\n",
-       "      <td>8.020000e+17</td>\n",
-       "      <td>Riley Park</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>298</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>310</th>\n",
-       "      <td>7.183413e+06</td>\n",
-       "      <td>Grandview-Woodland</td>\n",
-       "      <td>Private room</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>75</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1799</th>\n",
-       "      <td>3.788796e+07</td>\n",
-       "      <td>Downtown</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>6</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>150</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1619</th>\n",
-       "      <td>3.519493e+07</td>\n",
-       "      <td>West End</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>143</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3300</th>\n",
-       "      <td>6.910000e+17</td>\n",
-       "      <td>Downtown</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>249</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>240</th>\n",
-       "      <td>5.347437e+06</td>\n",
-       "      <td>Downtown</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>69</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>391</th>\n",
-       "      <td>9.636681e+06</td>\n",
-       "      <td>Downtown Eastside</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>80</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4775</th>\n",
-       "      <td>9.650000e+17</td>\n",
-       "      <td>Hastings-Sunrise</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>635</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2162</th>\n",
-       "      <td>4.510529e+07</td>\n",
-       "      <td>Kensington-Cedar Cottage</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>169</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3115</th>\n",
-       "      <td>6.550000e+17</td>\n",
-       "      <td>Downtown</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>230</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4255</th>\n",
-       "      <td>8.910000e+17</td>\n",
-       "      <td>Downtown Eastside</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>372</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2480</th>\n",
-       "      <td>5.073003e+07</td>\n",
-       "      <td>Oakridge</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>188</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2549</th>\n",
-       "      <td>5.156516e+07</td>\n",
-       "      <td>Grandview-Woodland</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>192</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                id             neighbourhood        room_type  accommodates  \\\n",
-       "2912  5.970000e+17                Riley Park  Entire home/apt             9   \n",
-       "787   1.986261e+07            Mount Pleasant  Entire home/apt             9   \n",
-       "2696  5.333958e+07             Arbutus Ridge  Entire home/apt             3   \n",
-       "3017  6.310000e+17                  West End  Entire home/apt             2   \n",
-       "1226  2.745958e+07                  Downtown  Entire home/apt             1   \n",
-       "2620  5.255417e+07         Downtown Eastside  Entire home/apt             4   \n",
-       "4883  9.790000e+17         Downtown Eastside  Entire home/apt             7   \n",
-       "608   1.554257e+07                 Kitsilano  Entire home/apt             5   \n",
-       "1400  3.124019e+07             Arbutus Ridge     Private room             2   \n",
-       "149   3.100878e+06                  Downtown  Entire home/apt             2   \n",
-       "3599  7.590000e+17  Kensington-Cedar Cottage  Entire home/apt             8   \n",
-       "1066  2.475420e+07                 Kitsilano  Entire home/apt             2   \n",
-       "361   8.429321e+06                  Fairview  Entire home/apt             4   \n",
-       "984   2.341930e+07                  Downtown  Entire home/apt             5   \n",
-       "3851  8.160000e+17          Hastings-Sunrise  Entire home/apt             4   \n",
-       "3560  7.510000e+17                 Kitsilano  Entire home/apt             4   \n",
-       "2692  5.331242e+07                Strathcona  Entire home/apt             4   \n",
-       "988   2.348250e+07                  Fairview  Entire home/apt             2   \n",
-       "4777  9.650000e+17       Renfrew-Collingwood     Private room             5   \n",
-       "4419  9.120000e+17                  Downtown  Entire home/apt             4   \n",
-       "1465  3.287258e+07                 Kitsilano  Entire home/apt             4   \n",
-       "548   1.455531e+07        Grandview-Woodland  Entire home/apt             2   \n",
-       "1594  3.490318e+07                  West End  Entire home/apt             6   \n",
-       "3076  6.440000e+17  Kensington-Cedar Cottage  Entire home/apt             2   \n",
-       "273   6.315732e+06                  Fairview  Entire home/apt             3   \n",
-       "142   2.988185e+06                  West End  Entire home/apt             4   \n",
-       "4601  9.360000e+17         Downtown Eastside  Entire home/apt             4   \n",
-       "3795  8.020000e+17                Riley Park  Entire home/apt             4   \n",
-       "310   7.183413e+06        Grandview-Woodland     Private room             2   \n",
-       "1799  3.788796e+07                  Downtown  Entire home/apt             6   \n",
-       "1619  3.519493e+07                  West End  Entire home/apt             3   \n",
-       "3300  6.910000e+17                  Downtown  Entire home/apt             4   \n",
-       "240   5.347437e+06                  Downtown  Entire home/apt             3   \n",
-       "391   9.636681e+06         Downtown Eastside  Entire home/apt             2   \n",
-       "4775  9.650000e+17          Hastings-Sunrise  Entire home/apt             4   \n",
-       "2162  4.510529e+07  Kensington-Cedar Cottage  Entire home/apt             4   \n",
-       "3115  6.550000e+17                  Downtown  Entire home/apt             5   \n",
-       "4255  8.910000e+17         Downtown Eastside  Entire home/apt             4   \n",
-       "2480  5.073003e+07                  Oakridge  Entire home/apt             3   \n",
-       "2549  5.156516e+07        Grandview-Woodland  Entire home/apt             4   \n",
-       "\n",
-       "      bathrooms  bedrooms  beds  price  \n",
-       "2912        3.0       4.0   5.0    214  \n",
-       "787         2.0       3.0   3.0    109  \n",
-       "2696        1.0       1.0   0.0    200  \n",
-       "3017        1.0       1.0   0.0    223  \n",
-       "1226        NaN       1.0   NaN    126  \n",
-       "2620        1.0       1.0   2.0    198  \n",
-       "4883        2.0       3.0   4.0    834  \n",
-       "608         1.0       2.0   3.0     99  \n",
-       "1400        1.0       1.0   2.0    133  \n",
-       "149         1.0       1.0   0.0     60  \n",
-       "3599        NaN       3.0   NaN    273  \n",
-       "1066        1.0       1.0   1.0    120  \n",
-       "361         1.0       1.0   1.0     79  \n",
-       "984         1.0       1.0   3.0    117  \n",
-       "3851        1.0       2.0   2.0    300  \n",
-       "3560        1.0       1.0   2.0    269  \n",
-       "2692        1.0       2.0   3.0    200  \n",
-       "988         1.0       1.0   1.0    118  \n",
-       "4777        1.0       2.0   3.0    640  \n",
-       "4419        1.5       2.0   2.0    409  \n",
-       "1465        2.0       2.0   2.0    136  \n",
-       "548         NaN       1.0   NaN     95  \n",
-       "1594        2.0       3.0   3.0    141  \n",
-       "3076        1.0       1.0   1.0    227  \n",
-       "273         1.0       1.0   2.0     70  \n",
-       "142         NaN       2.0   NaN     59  \n",
-       "4601        NaN       2.0   NaN    495  \n",
-       "3795        1.0       2.0   2.0    298  \n",
-       "310         1.0       1.0   1.0     75  \n",
-       "1799        2.0       2.0   3.0    150  \n",
-       "1619        1.0       1.0   2.0    143  \n",
-       "3300        1.0       1.0   1.0    249  \n",
-       "240         1.0       2.0   2.0     69  \n",
-       "391         NaN       1.0   NaN     80  \n",
-       "4775        1.0       2.0   4.0    635  \n",
-       "2162        1.0       2.0   3.0    169  \n",
-       "3115        2.0       3.0   3.0    230  \n",
-       "4255        1.0       0.0   0.0    372  \n",
-       "2480        NaN       2.0   NaN    188  \n",
-       "2549        1.0       2.0   2.0    192  "
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "np.random.seed(1234)\n",
     "one_sample = airbnb.sample(n=40)\n",
-    "one_sample"
+    "# one_sample"
    ]
   },
   {
@@ -1288,12 +408,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -1319,7 +439,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -1328,7 +448,7 @@
        "219.85"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1363,12 +483,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -1398,7 +518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -1407,7 +527,7 @@
        "175.425"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1429,214 +549,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "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>id</th>\n",
-       "      <th>neighbourhood</th>\n",
-       "      <th>room_type</th>\n",
-       "      <th>accommodates</th>\n",
-       "      <th>bathrooms</th>\n",
-       "      <th>bedrooms</th>\n",
-       "      <th>beds</th>\n",
-       "      <th>price</th>\n",
-       "      <th>replicate</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2480</th>\n",
-       "      <td>5.073003e+07</td>\n",
-       "      <td>Oakridge</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>188</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4419</th>\n",
-       "      <td>9.120000e+17</td>\n",
-       "      <td>Downtown</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>409</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>273</th>\n",
-       "      <td>6.315732e+06</td>\n",
-       "      <td>Fairview</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>70</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>548</th>\n",
-       "      <td>1.455531e+07</td>\n",
-       "      <td>Grandview-Woodland</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>2</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>95</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3795</th>\n",
-       "      <td>8.020000e+17</td>\n",
-       "      <td>Riley Park</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>298</td>\n",
-       "      <td>0</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",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3560</th>\n",
-       "      <td>7.510000e+17</td>\n",
-       "      <td>Kitsilano</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>269</td>\n",
-       "      <td>19999</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1465</th>\n",
-       "      <td>3.287258e+07</td>\n",
-       "      <td>Kitsilano</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>136</td>\n",
-       "      <td>19999</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3115</th>\n",
-       "      <td>6.550000e+17</td>\n",
-       "      <td>Downtown</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>5</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>230</td>\n",
-       "      <td>19999</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2480</th>\n",
-       "      <td>5.073003e+07</td>\n",
-       "      <td>Oakridge</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>3</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>188</td>\n",
-       "      <td>19999</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1465</th>\n",
-       "      <td>3.287258e+07</td>\n",
-       "      <td>Kitsilano</td>\n",
-       "      <td>Entire home/apt</td>\n",
-       "      <td>4</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>136</td>\n",
-       "      <td>19999</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>800000 rows × 9 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                id       neighbourhood        room_type  accommodates  \\\n",
-       "2480  5.073003e+07            Oakridge  Entire home/apt             3   \n",
-       "4419  9.120000e+17            Downtown  Entire home/apt             4   \n",
-       "273   6.315732e+06            Fairview  Entire home/apt             3   \n",
-       "548   1.455531e+07  Grandview-Woodland  Entire home/apt             2   \n",
-       "3795  8.020000e+17          Riley Park  Entire home/apt             4   \n",
-       "...            ...                 ...              ...           ...   \n",
-       "3560  7.510000e+17           Kitsilano  Entire home/apt             4   \n",
-       "1465  3.287258e+07           Kitsilano  Entire home/apt             4   \n",
-       "3115  6.550000e+17            Downtown  Entire home/apt             5   \n",
-       "2480  5.073003e+07            Oakridge  Entire home/apt             3   \n",
-       "1465  3.287258e+07           Kitsilano  Entire home/apt             4   \n",
-       "\n",
-       "      bathrooms  bedrooms  beds  price  replicate  \n",
-       "2480        NaN       2.0   NaN    188          0  \n",
-       "4419        1.5       2.0   2.0    409          0  \n",
-       "273         1.0       1.0   2.0     70          0  \n",
-       "548         NaN       1.0   NaN     95          0  \n",
-       "3795        1.0       2.0   2.0    298          0  \n",
-       "...         ...       ...   ...    ...        ...  \n",
-       "3560        1.0       1.0   2.0    269      19999  \n",
-       "1465        2.0       2.0   2.0    136      19999  \n",
-       "3115        2.0       3.0   3.0    230      19999  \n",
-       "2480        NaN       2.0   NaN    188      19999  \n",
-       "1465        2.0       2.0   2.0    136      19999  \n",
-       "\n",
-       "[800000 rows x 9 columns]"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Initialize an empty list to store the bootstrap samples\n",
     "bootstrap_samples = []\n",
@@ -1650,136 +565,28 @@
     "boot20000 = pd.concat(bootstrap_samples)\n",
     "\n",
     "# Display the combined DataFrame\n",
-    "boot20000"
+    "# boot20000"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 19,
    "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>replicate</th>\n",
-       "      <th>mean_price</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0</td>\n",
-       "      <td>208.250</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>205.800</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>2</td>\n",
-       "      <td>183.925</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>3</td>\n",
-       "      <td>211.600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>4</td>\n",
-       "      <td>239.475</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19995</th>\n",
-       "      <td>19995</td>\n",
-       "      <td>176.725</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19996</th>\n",
-       "      <td>19996</td>\n",
-       "      <td>217.450</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19997</th>\n",
-       "      <td>19997</td>\n",
-       "      <td>204.600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19998</th>\n",
-       "      <td>19998</td>\n",
-       "      <td>216.900</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>19999</th>\n",
-       "      <td>19999</td>\n",
-       "      <td>198.375</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>20000 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       replicate  mean_price\n",
-       "0              0     208.250\n",
-       "1              1     205.800\n",
-       "2              2     183.925\n",
-       "3              3     211.600\n",
-       "4              4     239.475\n",
-       "...          ...         ...\n",
-       "19995      19995     176.725\n",
-       "19996      19996     217.450\n",
-       "19997      19997     204.600\n",
-       "19998      19998     216.900\n",
-       "19999      19999     198.375\n",
-       "\n",
-       "[20000 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Calculate the mean price for each bootstrap sample (replicate)\n",
     "boot_means = boot20000.groupby('replicate')['price'].mean().reset_index(name='mean_price')\n",
-    "boot_means"
+    "# boot_means"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -1848,7 +655,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1859,7 +666,7 @@
        "Name: mean_price, dtype: float64"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1877,9 +684,9 @@
     "To wrap up our estimation, we'd report:\n",
     "\n",
     "- **Point Estimate**: The sample mean price-per-night of 40 Airbnb listings is $175.425.\n",
-    "- **95% Confidence Interval**: We estimate that the true mean price-per-night for all Airbnb listings in Vancouver is between $171.44 and $275.68.\n",
+    "- **95% Confidence Interval**: We estimate that the true mean price-per-night for all Airbnb listings in Vancouver is between 171.44 and 275.68.\n",
     "\n",
-    "Our interval includes the true population mean ($249.16), but in real-world scenarios, we wouldn’t know the true population mean because we only have one sample."
+    "Our interval includes the true population mean (249.16), but in real-world scenarios, we wouldn’t know the true population mean because we only have one sample."
    ]
   },
   {
@@ -1900,9 +707,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "base",
+   "display_name": "dsi",
    "language": "python",
-   "name": "python3"
+   "name": "dsi"
   },
   "language_info": {
    "codemirror_mode": {
@@ -1914,9 +721,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/01_materials/slides/Clustering.pdf b/01_materials/slides/Clustering.pdf
index 0bc578c69..dd551773c 100644
Binary files a/01_materials/slides/Clustering.pdf and b/01_materials/slides/Clustering.pdf differ
diff --git a/01_materials/slides/Stat_Inference.pdf b/01_materials/slides/Stat_Inference.pdf
index a85c56b96..2e04e27f8 100644
Binary files a/01_materials/slides/Stat_Inference.pdf and b/01_materials/slides/Stat_Inference.pdf differ
diff --git a/03_instructional_team/markdown_slides/Clustering.md b/03_instructional_team/markdown_slides/Clustering.md
index cffce22d9..0b331bdbb 100644
--- a/03_instructional_team/markdown_slides/Clustering.md
+++ b/03_instructional_team/markdown_slides/Clustering.md
@@ -94,16 +94,31 @@ Visualizing the relationship between flipper length and bill length, we can obse
 ---
 ##### WSSD
   1. Calculate the cluster centers by taking the mean of each variable for all data points in a cluster.
-  2. Measure the sum of squared distances between each data point and its cluster center.
+  For example, suppose we have a cluster containing 4 observations, and we are using two variables, $x$ and $y$ , to cluster the data. Then we would compute the coordinates, $\mu_x$ and $\mu_y$ of the cluster center by
+  $$
+  \mu_x = \frac{1}{4}(x_1+x_2+x_3+x_4) \quad \mu_y = \frac{1}{4}(y_1+y_2+y_3+y_4)
+  $$
+---
+##### WSSD
+2. Measure the sum of squared distances between each data point and its cluster center.
 ![bg right:45% w:600](./images/wssd.png)
 WSSD is computed by summing the squared Euclidean distances between each data point and the cluster center.
+$$
+\begin{split}
+\text{WSSD} = \left((x_1 - \mu_x)^2 + (y_1 - \mu_y)^2\right) + \left((x_2 - \mu_x)^2 + (y_2 - \mu_y)^2\right)\\
+ + \left((x_3 - \mu_x)^2 + (y_3 - \mu_y)^2\right)  +  \left((x_4 - \mu_x)^2 + (y_4 - \mu_y)^2\right)
+\end{split}
+$$
 ---
+
+##### WSSD
 - A larger WSSD indicates that the cluster is more spread out, as it means data points are farther from the cluster center.
 
 - To obtain the total WSSD, sum the WSSD values for all clusters, which involves adding up all squared distances for all observations.
 ![bg right:50% w:600](./images/all_wssd.png)
 
 ---
+
 ##### Clustering algorithm
 - The K-means algorithm starts by choosing $K$ and randomly assigning observations to each of the $K$ clusters. 
 - Here, each data point is assigned to 1 of 3 clusters:
diff --git a/03_instructional_team/markdown_slides/Stat_Inference.md b/03_instructional_team/markdown_slides/Stat_Inference.md
index 947fc6930..4c36eba52 100644
--- a/03_instructional_team/markdown_slides/Stat_Inference.md
+++ b/03_instructional_team/markdown_slides/Stat_Inference.md
@@ -42,7 +42,7 @@ Applying Statistical Concepts
 - Instead, we use a **sample**, a subset of the population, to estimate the population parameter.
 - **Sample estimate**: A numerical characteristic of the sample that approximates the population parameter.
 - **Statistical inference**: Using a sample to make conclusions about the broader population.
-![bg right:40% w:550](./images/population_vs_sample.png)
+![bg right:40% w:400](./images/population_vs_sample.png)
 
 ---
 ##### Example dataset