Problem 1. For this problem, use salaiydata.csv, which contains data on 43 employees in the human-resources division of a major retail firm involved in a discrimination lawsuit. They variable is the...

Problem 1. For this problem, use salaiydata.csv, which contains data on 43 employees in the human-resources division of a major retail firm involved in a discrimination lawsuit. They variable is the salary paid to each employee, measured in dollars. The predictor variables are: Sex: An indicator variable (1=man and O=woman); Education: Years of post-secondary education (that is, beyond high school) at the time of hire; Experience: Number of months of previous work experience; Months: Number of months with the company. Part A: Compute the average salary for men and the average salary for women. Is this suggestive of discrimination? If so, do men or women seem to be the victims? Part B: What salary would you forecast for a man with 5 years of post-secondary education, 10 months of ex-perience and 15 months with the company? What salary would you forecast for a woman with the same background? (These forecasts need only be point estimates, and do not have to incorporate error bars.) Part C: In light of this data, assess the evidence for and against the hypothesis that there is gender discrimination in this company's compensation practices. If you see evidence of discrimination, do men or women seem to be the victims, and is this consistent with the answer in Part A?
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