Assembly line work can be tedious and repetitive. Therefore, it is not suited for everybody. Consequently, a production manager is developing a binary choice regression model to predict whether a newly hired worker will stay in the job for at least one year. Three explanatory variables will be used: (1) age, (2) gender, and (3) whether the new hire has worked on an assembly line before. Records have been obtained for the past 32 assembly line workers hired. A portion of the data is shown in the accompanying table.

a. Estimate a linear probability model in which being in the job one year later depends on age, gender, and whether the new hire has worked on an assembly line before. Use this model to predict the probability that (1) a 45-year-old female who has not worked on an assembly line before will still be in the job one year later and (2) a 35-year-old male who has worked on an assembly line will still be in the job one year later.
b. Estimate a logit model where being in the job one year later depends on age, gender, and whether the new hire has worked on an assembly line before. Use this model to predict the probability that (1) a 45-year-old female who has not worked on an assembly line before will still be in the job one year later and (2) a 35-year-old male who has worked on an assembly line will still be in the job one year later.
c. At α = 0.05, compare the significance of the parameters in both models. What do the significance results imply from a practical standpoint?

Still in Job One Year Later? (1=yes, 0-no) Gender (1-female -male) Worked on Assembly Line Before? (1=yes, 0.no) Age 35 26 38