Mendenhall and Sincich (1993) present data that can be used to investigate allegations of gender discrimination in the hiring practices of a particular firm. These data are given on Page 732 in the text. In this data, Y is a dummy variable that equals 1 if a potential employee was hired and 0 otherwise; x1 is the number of years of education of the potential employee; x2 is the number of years of experience of the potential employee; and x3 is a dummy variable that equals 1 if the potential employee was a male and 0 if the potential employee was a female.

1. Evaluate the significance of each independent variable and make a conclusion based on P-value:
   1. What is the p-value for Education?
   2. What is the p-value for Experience? c. What is the p-value for Gender?
   3.  

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4 Effect Summary Source LogWorth Experience Gender Education Remove Add Edit FDR Converged in Gradient, 7 iterations ▷ Iterations 4 Whole Model Test Model Difference Full Reduced RSquare (U) AICC BIC 3.940 3.116 1.872 -LogLikelihood 10.214942 7.367422 17.582364 Observations (or Sum Wgts) ▷ Fit Details 4 Lack Of Fit Source Lack Of Fit Saturated Fitted DF ChiSquare Prob>ChiSq 3 20.42988 0.0001* 0.5810 24.474 28.0637 28 DF -LogLikelihood ChiSquare 22 5.9811280 11.96226 25 1.3862944 Prob>ChiSq 7.3674223 3 0.9582 4 Parameter Estimates Term Estimate Std Error ChiSquare Prob>ChiSq Intercept -11.446418 5.0102814 Education 1.1548804 0.6022945 Experience 0.90984864 0.4292935 Gender[0] -2.8018397 1.3013912 For log odds of 1/0 5.22 3.68 4.49 4.64 0.0223* 0.0552 PValue 0.00011 0.00077 0.01343 0.0341* 0.0313* 4 Effect Summary Source LogWorth Experience Gender Education Remove Add Edit FDR Converged in Gradient, 7 iterations ▷ Iterations 4 Whole Model Test Model Difference Full Reduced RSquare (U) AICC BIC 3.940 3.116 1.872 -LogLikelihood 10.214942 7.367422 17.582364 Observations (or Sum Wgts) ▷ Fit Details 4 Lack Of Fit Source Lack Of Fit Saturated Fitted DF ChiSquare Prob>ChiSq 3 20.42988 0.0001* 0.5810 24.474 28.0637 28 DF -LogLikelihood ChiSquare 22 5.9811280 11.96226 25 1.3862944 Prob>ChiSq 7.3674223 3 0.9582 4 Parameter Estimates Term Estimate Std Error ChiSquare Prob>ChiSq Intercept -11.446418 5.0102814 Education 1.1548804 0.6022945 Experience 0.90984864 0.4292935 Gender[0] -2.8018397 1.3013912 For log odds of 1/0 5.22 3.68 4.49 4.64 0.0223* 0.0552 PValue 0.00011 0.00077 0.01343 0.0341* 0.0313*

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1 Evaluate the significance of each independent variable Nominal Logistic Fit for Hired Effect Summa... View the full answer