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Predictive Modeling of Serious Crime Using County Demographic Data: Stats Final Exam Part 2

Final exam solving predictive modeling problems for crime analysis using demographic data.

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    Stats Final Exam Part 2 Given the data set called County Demographic Information , construct a predictive model for the variable “ Total Serious Crime ” using some or all of the other variables in the set of data. The model should be mathematically valid , accurate and reliable. Total Serious Crime is Variable #8 Other Variables: #2 Land Area #3 Total Population #4 Percent of Population aged 18 - 34 #5 Percent of Population 65 or over #6 Number of Active Physicians #7 Number of Hospital Beds #9 Percent of High School Graduates #10 Percent of Population with College Degrees #11 Percent of Population below poverty level #12 Unemployment Percent #13 Per Capita Income #14 Total Personal Income #15 Geographic Region Note: I am omitting the data set to simplify this problem; the following analyses use the data set described above, and you can assume the math is calculated correctly. I am testing to see if you can identify what analytical techniques may be validly employed and how effective are they building a model. Variables 2 to 14 are numeric variables and variable 15 is categorical .

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    Analysis #1 In the given data set, we were asked to determine if an accurate predictive model for Variable #8, Serious Crime could be found using the attached data. Since Variable 15 was determined to be categorical , regression was not appropriate to use; so I used Analysis of Variance (ANOVA) to examine if there was a significant relationship between Variable 8 and 15. The results (using Systat 13.0) are printed above. Variables Levels VAR(15) (4 levels) 1.000 2.000 3.000 4.000 Dependent Variable VAR(8) N 440 Multiple R 0.110 Squared Multiple R 0.012 Estimates of Effects B = (X'X) - 1 X'Y Factor Level VAR(8) CONSTANT 28,017.368 VAR(15) 1 - 4,931.339 VAR(15) 2 - 6,236.627 VAR(15) 3 - 1,026.394 Analysis of Variance Source Type III SS df Mean Squares F - Ratio p - Value VAR(15) 1.795E+010 3 5.985E+009 1.774 0.151 Error 1.471E+012 436 3.374E+009 ANOVA results suggest that Variable 15 is significantly related to Variable 8, but Variable 15 can only explain approximately 15.1% of the variation in Variable 8. Therefore, I conclude that variable 15 is significantly related to variable 8 although variable 15 is only a minor factor in predicting variable 8.

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