A regression model to predict Y the state burglary rate per

A regression model to predict Y, the state burglary rate per 100,000 people for 2005, used the following four state predictors: X1 median age in 2005, X2 number of 2005 bankruptcies, X. 2004 federal expenditures per capita (a leading predictor), and X 2005 high school graduation percentage Click here for the Excel Data File (a) Using the sample size of 50 people, calculate the tcalc and p-value in the table given below. (Negative values should be indicated by a minus sign. Leave no cells blank be certain to enter \"O\" wherever required. Round your answers to 4 decimal places.) ticalo Predictor Coefficient SE p-value Intercept 4,198.5808 799.3395 AgeMed 27.3540 12.5687 Bankrupt 17.4893 12.4033 FedSpend -0.0124 0.0176 HS Grad% 29.0314 7.1268 (b-1) What is the critical values of student\'s t in Appendix D for two-tailed test at a 01. (Round your answer to 3 decimal places.) t-value (b-2) Choose the correct option. O Only HSGrad% ffers significantly from zero O Only Fedspend differ significantly from zero O Only Bankrupt differs significantly from zero.

Solution

a)

Note that

tcalc = coefficient / SE

Thus,

intercept: t = 5.252562647   p = 0
agemed: t = -2.176358732   p = 0.0347
bankrupt: t = 1.410052164   p = 0.1653
fedspend: t = -0.704545455   p = 0.4846
hsgrad: t = -4.073553348   p = 0.0002

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b-1.

As df = n - 4 = 46, then at 0.01 level,

tcrit = 2.687013492

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b-2.

Only hsgrad has p < 0.01.

Thus,

OPTION A: Only HSGrad% differs significantly from 0.