Suppose you are investigating allegations of gender discrimination in the hiring practices of a particular company. An equal-rights group claims that females are less likely to be hired than males with the same background, experience and other qualifications. Data collected on 28 former applicants will be used to fit a logistic model, with hiring as the response variable, and three explanatory variables: education (years of higher education), experience (in years) and gender (1 for male applicants, and 0 for female applicants).

> fitl summary (fit1) Call: 91m(formula = hiring ~ education + experience, family = binomial (link = logit); data = discrimination) Deviance Residuals: Min 10 Median 30 Max -1. 5031 -0.6810 -0. 3902 0. 5550 1.9886 Coefficients: Estimate Std. Error z value Pr(>|z)) (Intercept) -5. 4419 2.3361 -2.330 0. 0198 education 0. 5404 0. 3287 1. 644 0. 1002 experience 0. 3717 0. 1670 2. 226 0. 0260 Signif. codes: ( ' *#*' 0.001 '##' 0.01 '#' 0.05 ' .' 0.1 ' ' 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 35.165 on 27 degrees of freedom Residual deviance: 26.056 on 25 degrees of freedom AIC: 32.056 Number of Fisher Scoring iterations: 5NNNNNNNNNHL ONOVAWNGO HIDONOW AWNH HHOOOHOOOOHOOHOO HOOOHO HAGG > discrimination hiring education experience gender NOHOMONHNHO AWNNUNOHANWHWOOK OHHOOHHOHOHOHOOOHOOHOOHPHO> discrimination fit Summary (fit) Coefficients: Estimate Std. Error z value Pr(>|z1) (Intercept) -14.2483 6.0805 -2. 343 0. 0191 education 1. 1549 0. 6023 1. 917 0. 0552 experience 0.9098 0. 4293 2. 119 0.0341 # gender 5. 6037 2. 6028 2.153 0.0313 # Signif. codes: 0 *#*#' 0.001 "##' 0.01 .#' 0.05 . .' 0.1 . ' 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 35.165 on 27 degrees of freedom Residual deviance: 14.735 on 24 degrees of freedom AIC: 22.735 Number of Fisher Scoring iterations: 7