diff --git a/Schrick-Noah_Learning-Practice-5.ipynb b/Schrick-Noah_Learning-Practice-5.ipynb
index 0e57b77..a124100 100644
--- a/Schrick-Noah_Learning-Practice-5.ipynb
+++ b/Schrick-Noah_Learning-Practice-5.ipynb
@@ -638,22 +638,18 @@
    "metadata": {},
    "source": [
     "# Linear? \n",
-    "There does not appear to be a linear relationship between spending and frequency or spending and last update days ago.\n",
-    "An argument could be made for Frequency and Spending as spending gets larger, but both scatter plots do not seem to indicate a linear relationship."
+    "There does not appear to be a linear relationship between spending and last update days ago.\n",
+    "An argument could be made for Frequency and Spending as spending gets larger, but both scatter plots do not seem to indicate a linear relationship. The linear fit for frequency and spending would have a low R squared value."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 109,
    "metadata": {},
    "outputs": [],
    "source": [
     "# c. i\n",
-    "predictors = ['sequence_number', 'US', 'source_a', 'source_c', 'source_b', 'source_d', 'source_e',\n",
-    "              'source_m', 'source_o', 'source_h', 'source_r', 'source_s', 'source_t',\n",
-    "              'source_u', 'source_p', 'source_x', 'source_w', 'Freq', 'last_update_days_ago',\n",
-    "              '1st_update_days_ago', 'Web order', 'Gender=male', 'Address_is_res', 'Purchase',\n",
-    "              'Spending']\n",
+    "predictors = ['US','Freq', 'last_update_days_ago', 'Web order', 'Gender=male', 'Address_is_res']\n",
     "\n",
     "outcome = 'Spending'\n",
     "X = pd.get_dummies(tayko_df[predictors], drop_first=True)\n",
@@ -663,11 +659,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 111,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "intercept  10.17629741458822\n",
+      "              Predictor  coefficient\n",
+      "0                    US    -4.620293\n",
+      "1                  Freq    91.274450\n",
+      "2  last_update_days_ago    -0.010374\n",
+      "3             Web order    18.628731\n",
+      "4           Gender=male    -9.111366\n",
+      "5        Address_is_res   -75.815354\n"
+     ]
+    },
+    {
+     "ename": "ValueError",
+     "evalue": "operands could not be broadcast together with shapes (800,) (1200,) ",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[111], line 10\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[39mprint\u001b[39m(pd\u001b[39m.\u001b[39mDataFrame({\u001b[39m'\u001b[39m\u001b[39mPredictor\u001b[39m\u001b[39m'\u001b[39m: X\u001b[39m.\u001b[39mcolumns, \u001b[39m'\u001b[39m\u001b[39mcoefficient\u001b[39m\u001b[39m'\u001b[39m: tayko_lm\u001b[39m.\u001b[39mcoef_}))\n\u001b[1;32m      9\u001b[0m \u001b[39m# print performance measures\u001b[39;00m\n\u001b[0;32m---> 10\u001b[0m regressionSummary(valid_y, tayko_lm\u001b[39m.\u001b[39;49mpredict(train_X))\n",
+      "File \u001b[0;32m~/.local/lib/python3.10/site-packages/dmba/metric.py:71\u001b[0m, in \u001b[0;36mregressionSummary\u001b[0;34m(y_true, y_pred)\u001b[0m\n\u001b[1;32m     69\u001b[0m y_true \u001b[39m=\u001b[39m _toArray(y_true)\n\u001b[1;32m     70\u001b[0m y_pred \u001b[39m=\u001b[39m _toArray(y_pred)\n\u001b[0;32m---> 71\u001b[0m y_res \u001b[39m=\u001b[39m y_true \u001b[39m-\u001b[39;49m y_pred\n\u001b[1;32m     72\u001b[0m metrics \u001b[39m=\u001b[39m [\n\u001b[1;32m     73\u001b[0m     (\u001b[39m'\u001b[39m\u001b[39mMean Error (ME)\u001b[39m\u001b[39m'\u001b[39m, \u001b[39msum\u001b[39m(y_res) \u001b[39m/\u001b[39m \u001b[39mlen\u001b[39m(y_res)),\n\u001b[1;32m     74\u001b[0m     (\u001b[39m'\u001b[39m\u001b[39mRoot Mean Squared Error (RMSE)\u001b[39m\u001b[39m'\u001b[39m, math\u001b[39m.\u001b[39msqrt(mean_squared_error(y_true, y_pred))),\n\u001b[1;32m     75\u001b[0m     (\u001b[39m'\u001b[39m\u001b[39mMean Absolute Error (MAE)\u001b[39m\u001b[39m'\u001b[39m, \u001b[39msum\u001b[39m(\u001b[39mabs\u001b[39m(y_res)) \u001b[39m/\u001b[39m \u001b[39mlen\u001b[39m(y_res)),\n\u001b[1;32m     76\u001b[0m ]\n\u001b[1;32m     77\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mall\u001b[39m(yt \u001b[39m!=\u001b[39m \u001b[39m0\u001b[39m \u001b[39mfor\u001b[39;00m yt \u001b[39min\u001b[39;00m y_true):\n",
+      "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (800,) (1200,) "
+     ]
+    }
+   ],
    "source": [
-    "# c. ii"
+    "# c. ii\n",
+    "tayko_lm = LinearRegression()\n",
+    "tayko_lm.fit(train_X, train_y)\n",
+    "\n",
+    "# print coefficients\n",
+    "print('intercept ', tayko_lm.intercept_)\n",
+    "print(pd.DataFrame({'Predictor': X.columns, 'coefficient': tayko_lm.coef_}))\n",
+    "\n",
+    "# print performance measures\n",
+    "regressionSummary(valid_y, tayko_lm.predict(valid_X))"
    ]
   }
  ],