update permissions, minor edits

FinalProject_Group022_WI24.ipynb

@@ -8,7 +8,7 @@
     "\n",
     "# Permissions\n",
     "\n",
-    "* [  ] YES - make available\n",
+    "* [ X ] YES - make available\n",
     "* [  ] NO - keep private\n",
     "\n",
     "# Names\n",
@@ -2277,23 +2277,6 @@
     "print(res_log.summary())"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To get the two variables in the linear regression, we did two things: \n",
-    "\n",
-    "1. Get the percent change for the Violent Sum per month of each county:\n",
-    "- We wanted to do this because each county has a different population, so we wanted to see the trends relative to each county, and how much they changed per month. So, we used the `.pct_change()` function to calculate that. \n",
-    "\n",
-    "2. Get the percent change for the COVID-19 trends per month of each county:\n",
-    "- We had similar reasoning as above to do this for the COVID trends. However, since there were many factors that led into the effect of COVID, we categorized this as the average of the sum of four categories: `cases`,`deaths`, `positive_tests`, `hospitalized_covid_patients`. Then, we got the percent change per month of each county, similar to the crime percent change, and used both of them to calculate upward or downward trends in COVID-19 cases. \n",
-    "\n",
-    "\n",
-    "\n",
-    "After putting these into the OLS Regression, we see that we get a p-value of `0.567`. This shows that there doesn't seem to be much of a correlation between COVID trends and crime rates. "
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 232,
@@ -2321,6 +2304,23 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To get the two variables in the linear regression, we did two things: \n",
+    "\n",
+    "1. Get the percent change for the Violent Sum per month of each county:\n",
+    "- We wanted to do this because each county has a different population, so we wanted to see the trends relative to each county, and how much they changed per month. So, we used the `.pct_change()` function to calculate that. \n",
+    "\n",
+    "2. Get the percent change for the COVID-19 trends per month of each county:\n",
+    "- We had similar reasoning as above to do this for the COVID trends. However, since there were many factors that led into the effect of COVID, we categorized this as the average of the sum of four categories: `cases`,`deaths`, `positive_tests`, `hospitalized_covid_patients`. Then, we got the percent change per month of each county, similar to the crime percent change, and used both of them to calculate upward or downward trends in COVID-19 cases. \n",
+    "\n",
+    "\n",
+    "\n",
+    "After putting these into the OLS Regression, we see that we get a p-value of `0.567`. This shows that there doesn't seem to be much of a correlation between COVID trends and crime rates. "
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -2362,7 +2362,7 @@
     "- Marina Hu: worked on writing the ideal dataset portion for the project proposal, found some datasets as well, cleaned and analyzed and performed EDA on the COVID-19 cases data for data checkpoint, helped make a few slides for the video presentation, made/put together the final video through iMovies \n",
     "- Audrey Liang: worked on the background and research questions for the proposal, finding datasets, cleaned and analyzed the crime dataset as well as data visualizations, ran the OLS Regression and wrote Machine Learning Analysis in the Final Project and Slides. \n",
     "- Allison Huang: revised background to fit pivot, wrote ethics and privacy section, wrote abstract, created scatterplot matrix and corresponding analysis, cleaned final merged dataset, cleaned entire report (made each section cohesive/same format, cleaned visualizations/labels, reformatted some code and added comments, added background info)\n",
-    "- Samuel Lee: Found some of the datasets. Mainly worked on the hospitalization dataset and its EDA graph. Helped to clean the notebook and merged the 4 datasets into one. Also Helped the machine learning part and provided some ideas. Presented the conclusion part for the final video."
+    "- Samuel Lee: found some of the datasets, mainly worked on the hospitalization dataset and its EDA graph, helped to clean the notebook and merged the 4 datasets into one, also helped the machine learning part and provided some ideas, presented the conclusion part for the final video"
    ]
   }
  ],