diff --git a/Schrick-Noah_Lecture-Work.ipynb b/Schrick-Noah_Lecture-Work.ipynb
index 8ffa337..3ef03f0 100644
--- a/Schrick-Noah_Lecture-Work.ipynb
+++ b/Schrick-Noah_Lecture-Work.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -12,12 +12,13 @@
     "# Noah L. Schrick - 1492657\n",
     "\n",
     "import pandas as pd\n",
+    "import numpy as np\n",
     "import matplotlib.pyplot as plt"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -229,7 +230,7 @@
        "CAT. MEDV         0  "
       ]
      },
-     "execution_count": 3,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -253,7 +254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -570,7 +571,7 @@
        "CAT. MEDV    -0.44  -0.47  0.79       1.00  "
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -579,6 +580,153 @@
     "# Correlation Matrix\n",
     "bostonHousing_df.corr().round(2)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RM_bin  CHAS\n",
+       "3       0       25.300000\n",
+       "4       0       15.407143\n",
+       "5       0       17.200000\n",
+       "        1       22.218182\n",
+       "6       0       21.769170\n",
+       "        1       25.918750\n",
+       "7       0       35.964444\n",
+       "        1       44.066667\n",
+       "8       0       45.700000\n",
+       "        1       35.950000\n",
+       "Name: MEDV, dtype: float64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create bins of size 1 for variables\n",
+    "\n",
+    "bostonHousing_df['RM_bin'] = pd.cut(bostonHousing_df.RM, range(0, 10), labels=False)\n",
+    "\n",
+    "# Compute average of MEDV by (binned) RM and CHAS. \n",
+    "# Group the data using groupby, then restrict the analysis to MEDV and determine the mean for each group.\n",
+    "\n",
+    "bostonHousing_df.groupby(['RM_bin', 'CHAS'])['MEDV'].mean()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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>CHAS</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>All</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>RM_bin</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>25.300000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>25.300000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>15.407143</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>15.407143</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>17.200000</td>\n",
+       "      <td>22.218182</td>\n",
+       "      <td>17.551592</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>21.769170</td>\n",
+       "      <td>25.918750</td>\n",
+       "      <td>22.015985</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>35.964444</td>\n",
+       "      <td>44.066667</td>\n",
+       "      <td>36.917647</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>45.700000</td>\n",
+       "      <td>35.950000</td>\n",
+       "      <td>44.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>All</th>\n",
+       "      <td>22.093843</td>\n",
+       "      <td>28.440000</td>\n",
+       "      <td>22.532806</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "CHAS            0          1        All\n",
+       "RM_bin                                 \n",
+       "3       25.300000        NaN  25.300000\n",
+       "4       15.407143        NaN  15.407143\n",
+       "5       17.200000  22.218182  17.551592\n",
+       "6       21.769170  25.918750  22.015985\n",
+       "7       35.964444  44.066667  36.917647\n",
+       "8       45.700000  35.950000  44.200000\n",
+       "All     22.093843  28.440000  22.532806"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create bins of size 1 for RM\n",
+    "bostonHousing_df['RM_bin'] = pd.cut(bostonHousing_df.RM, range(0, 10), labels=False)\n",
+    "\n",
+    "# use pivot_table() to reshape data and generate pivot table\n",
+    "pd.pivot_table(bostonHousing_df, values='MEDV', index=['RM_bin'], columns=['CHAS'], aggfunc=np.mean, margins=True)"
+   ]
   }
  ],
  "metadata": {
@@ -587,6 +735,18 @@
    "language": "python",
    "name": "python3"
   },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.9"
+  },
   "orig_nbformat": 4
  },
  "nbformat": 4,