For the following five exercises, you will need to construct indicator variables to use categorical variables as explanatory variables in logistic regression. Be sure to review the material in Chapter 11 on models with categorical explanatory variables (pages 571-575) before attempting these exercises.

Question 17.35

17.35 Reduction in force using logistic regression.

In Exercise 17.31, hypothetical data are given for a reduction in force (RIF). If there is a statistically significant difference in the RIF proportions based on age group, the employer needs to justify the difference based on other (nondiscriminatory) variables.

rif

  1. Run the logistic analysis to predict the odds of being riffed using age group (over 40 years of age or not) as the explanatory variable. Summarize your results.
  2. What other variables would you add to the model in an attempt to explain the results that you described in part (a)? If these other variables can be shown to be characteristics that relate to job performance, and the age effect is no longer significant in a model that includes these variables, then the analysis provides statistical evidence that can be used to refute a claim of discrimination.

17.35

(a) The model is significant. For a person over 40: 0.085. For a person under 40: 0.030. (b) Answers will vary.