Download the "State Crime" Excel sheet; row 2 gives detailed descriptions of each variable, while row 4 gives short versions that should be used in your regressions. Estimate the following multiple regression models (remember that all of your independent variables will have to be in adjacent columns in Excel). Look at each set of results critically and consider how you would interpret the strengths and weaknesses of each model. Save your results from each model for use when completing the end-of-module assessment. Use "Burglary" as your dependent variable in each model. The notation f(X, Y, Z) means "a function of X, Y, Z; i.e., X, Y, and Z are your independent variables. Even though it isn't listed, each model will include an intercept. NOTE: when Excel reports a value like 2.4E-06, this is scientific notation for 2.4 * (10^-6), or 0.0000024.

Model A: Burglary = f(%Unemploy, Rain, %SameHouse)

Model B: Burglary = f(Violent, %Poverty, MedianAge)

Model C: Burglary = f(Violent, %Poverty, MedAgeMale)

Model D: Burglary = f(Violent, %Poverty, MedAgeFemale)

Model E: Burglary = f(Assault, %Metro, Temp, PerCapIncome)

Model F: Burglary = f(Assault, %Metro, Temp, %Unemploy)

Model G: Burglary = f(Assault, %Metro, Temp, %Bachelors)

The state of South Oregon currently has an assault rate of 155.6, a 79.1% metro rate (be sure to use 79.1% or 0.791, not 79.1), an average annual temperature of 51.8 degrees, and per capita income of 37,440. Recent economic changes have led to the per capita income rising to 39,670. This change will likely be associated with the Burglary rate changing from about 560.3 robberies (per 100,000 inhabitants) to about ___ robberies