nearly done significance grid writeup

notebooks/FinalMilestone.ipynb

@@ -35,6 +35,7 @@
     "import folium\n",
     "from folium import plugins\n",
     "from pygam.pygam import LinearGAM, s, f, te, PoissonGAM\n",
+    "from scipy.stats import mannwhitneyu\n",
     "\n",
     "pd.options.display.max_columns = None\n",
     "pd.options.display.max_rows = None"
@@ -119229,7 +119230,9 @@
    "cell_type": "markdown",
    "id": "106d387d",
    "metadata": {},
-   "source": []
+   "source": [
+    "Now we are ready to perform a statistical test with the data properly prepared and predicted. The aim of this test is to see if there is a significant different between the hot and cold model for the count prediction of each bin within the grid. We do this with a Mann-Whitney U test, a non parametric statistical test used to compare two independent samples. [More info on this test is found here](https://www.statology.org/mann-whitney-u-test/). The 'mannwhitneyu' funcion compares the hot model prediction counts and cold model prediciton counts, and the 'greater' parameter specifies that we are testing if the hot predictions are greater than the cold predictions. It then returns the corresponding U-statistic and p-value. We analyze the results in the following cells."
+   ]
   },
   {
    "cell_type": "code",
@@ -119335,7 +119338,6 @@
     }
    ],
    "source": [
-    "from scipy.stats import mannwhitneyu\n",
     "#pred_diffs_df = X_test[[\"Long_bin\", \"Lat_bin\"]]\n",
     "def mann_whitney_bin(row):\n",
     "    target_long, target_lat = row[\"Long_bin\"], row[\"Lat_bin\"]\n",