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 ofX,Y,Z; i.e.,X,Y, andZare 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)

QUESTION: 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.

B D G H M N P Q R Aggrav ated Burglary assault % of state popu average annual temperature, degrees F Per capita disposable personal income, $ Robbery Burglary Assault %Metro Temp PerCap Income 877.8 285. 76.0% Robbery 628 32448 32.4 263.9 96.2 94.8% 60.3 32997 1,030.1 330.5 51.1% 60.4 101.1 32509 605.4 232.3 97.8% 76.3 59.4 41932 476.1 189.1 87.0% 139.9 45.1 41137 10 358.5 135.4 94.8% 59.8 4 3842 662.3 100.0% 98.2 313.7 55.3 38891 12 710.5 312.3 96.4% 132.4 70.7 36601 13 823.2 209.3 82.3% 118.7 63.5 33111 14 411.9 161.0 66.2% 44.4 125.0 32348 452.1 204.0 88.3% 51.8 13.6 40584 653.0 211.3 77.6% 51.7 137.6 35171 513.5 204.6 58.3% 47.8 108.2 38702 600.4 248.1 67.1% 30.4 54.3 41139 SUMMARY OUTPUT 596.4 95.5 58.2% 55.6 31960 46.6 890.4 73.9 352.8 $3.4% 66.4 36241 Regression Statistics 488.1 68.7 58.8% 41 119.9 35617 538.9 272.0 97.4% 25.2 54.2 45652 R Square 0.688036 459.2 270.5 98.5% 47.9 48472 169.5 Adjusted R Square 0.6590161 569.4 274.8 81.8% 44.4 100.2 34853 419.0 127.5 77.2% 41.2 102.1 40924 Observations 48 835.6 67.8 156.5 45.5% 63.4 30608 643.0 298.7 74.3% 54.5 35625 80.5 400.3 190.2 90.7 35.4% 42.7 35379 476.3 160.5 64.2% 20.1 48.8 41135 826.0 360.6 90.3% 49.9 55.7 34855 373.0 112.7 62.5% 43.8 46653 185.8 403.1 135.6 100.0% 49.0 52.7 47893 1,029.9 449.9 135.8 66.7% 53.4 31574 Coefficients Standard Errol t Stat 287. P-value 234.7 92.9% 45.4 86.8 45620 Intercept 921.0 349.17239 217.53945 1.6050992 0.1157923 218.4 77.6% 59 138.6 33682 Assault 0.7225007 0.2245601 3.2174041 0.0024601 405.6 94.9 199.9 49.2% 40.4 49385 %Metr 790.2 -92.98622 104.8109 -0.887181 0.379917 123.2 79.5% 50.7 22.4 36175 Temp 866.1 300.8 66.7% 11.507228 2.9497605 3.901072 0.0003317 59.6 38648 124.2 PerCapIncome -0.01132 0.0035855 -3.15713 0.0029105 528.5 78.7 142.7 83.4% 48.4 34606 407.3 185.7 88.3% 48.8 61.0 40849 533.2 147.4 100.0% OREGON IS LINE 39 = 61.0 50.1 115.6 41149 857.8 373.6 84.2% 62.4 31843 65.0 399.1 236.2 47.3% 83.2 45.2 40864 785.1 436.9 77.1% 57.6 18 .8 35690 721.8 246.9 88.6% 64.8 39023 112.5 459. 130.4 89.3% 120.2 48.6 32206 528.7 87.1 34.3% 42.8 42.9 41062 322.5 109.7 87.2% 55.1 42480 11.6 837.0 166.4 89.8% 55.3 48.3 43003 521.7 226.7 61.4% 51.8 83.5 31328 424.0 161.6 35.1 73.8% 43.1 37876 335.5 157.2 30.4% 42 84.2 46876 12.9