Criminal justice agencies are often interested in determining whether certain characteristics or variables of offenders can predict certain outcomes. For instance, recidivism, which is defined as returns to prison after being released, is often looked at in terms of what variables predict that outcome. Variables such as race, gender, criminal history, treatment received, and others can influence recidivism.
For this Individual Project, you are working as an analyst for a State Correctional Agency. You have been asked to investigate whether or not the number of drug arrests an individual has predicts his or her number of prison incarcerations. These data will be used to shape a sentencing policy.
This assignment has 3 steps.
Step 1: This assignment will require you to complete the linear regression using Microsoft Excel and to interpret the results of that analysis. Before you do that, watch the following video and Web site on residuals and residual plots because there will be questions about this later:
   Video: Residual Plots 
https://www.khanacademy.org/math/ap-statistics/bivariate-data-ap/assessing-fit-least-squares-regression/v/residual-plots
   Web site: Residual Analysis in Regression 
http://stattrek.com/regression/residual-analysis.aspx?Tutorial=AP