Explaining and Analyzing U$.$S$.$ School Enrollment from $2005-2017$ Project Goal: Merge school enrollment data from over $70,000$ U$.$S$.$ schools $(2005-2017)$ with crime and economic predictors to determine which factors best explain changes in enrollment. $1)$ On OneDrive, locate the folder U$.$S$.$ School and District Enrollment Data and download either the csv or xlsx version of the original school enrollment data ELSI$\_$School$\_$Enroll$\_2005\_2017\_$Orig. $2)$ You will need to rename the variables $($using Excel$)$ prior to importing the file into SAS for further processing. Video instructions for renaming have been provided via screencast Instructions: Cleaning School Enrollment Data $($Part $1).$ You will also need to clean the data by removing unusual missing value codes such as $""$ prior to meging the file. $3)$ Next, you will need to use SAS convert the file from wide panel format into long panel format by using a macro with a do loop $($extra credit prior to the deadline date$).$ After the extra credit due date, video instructions will be provided in screencast Instructions: Converting School Enrollment Data Using a Macro $($Part $2)4)$ In order to merge with the crime predictors $(1990-2015)$ file, we will need FIPS codes at the county or place level. Unfortunately, the original enrollment file does not containe FIPS codes below the state level. So$,$ you will need to merge with another type of file, known as a crosswalk, to obtain the needed codes. From the same OneDrive folder, download All$\_$School$\_$Coordinates$\_2016\_$ELSI and merge with this file by SchoolID to identify county$-$level FIPS codes for each school. $5)$ Before merging the school enrollment data with the crime predictors data, both files must be converted $($aggregated$)$ to the county$-$year level. From the crime$\_$predictors$\_1990\_2015$ data, we only need a few variables $($violent$\_$crime$\_$rate, employ$\_$county, percap$\_$income$\_$county, total$\_$officers$).$ Dont forget to use proc means to aggregate both files to the county$-$year level prior to merging. $6)$ Merge the school data with the crime predictors data and then run regressions to determine which of the crime predictors variables $($economic$,$ law enforcement, violent crime$)$ best explain overall school enrollment at the county$-$year level. $7)$ Copy and paste $($or save$/$copy$)$ your final regression output into MS$-$Word and add a few sentences that explain your results. Did any of the variables significantly explain or correlate with school enrollment? Were the Beta coefficients positive or negative? How much overall variation in school enrollment was explained by your best regression model $($via the R$2$ statistic$)?8)$ Upload your SAS code and MS$-$Word results with explanation to Blackboard via the assignment link.