Part 2 Regression Suppose a sociology researcher is interested in the effect that individuals’ names, job experience, age and gender have on earning potential. The “names” one may seem odd, but she has come to believe that individuals who have names early in the alphabet receive more attention, get better work assignments, etc., than those with names later in the alphabet. She collected a sample of 229 observations and has estimated a regression with these results: SALARY = 39,000 – 7900*FEMALE
Part 2 Regression Suppose a sociology researcher is interested in the effect that individuals’ names, job experience, age and gender have on earning potential. The “names” one may seem odd, but she has come to believe that individuals who have names early in the alphabet receive more attention, get better work assignments, etc., than those with names later in the alphabet. She collected a sample of 229 observations and has estimated a regression with these results: SALARY = 39,000 – 7900*FEMALE + 2350*NAMEA-G + 1850*YEARSEXP + 110*AGE Where: SALARY = Salary in $ FEMALE = 1 if individual identifies as female, 0 otherwise NAMEA-G = 1 if the individual’s FIRST NAME starts with the letters A,B,C,D,E,F, or G, 0 otherwise YEARSEXP = total number of years individual has worked for pay AGE = individual’s age in years Statistics from regression output R2 = .721 T-values FEMALE: t=2.487 NAMEA-G: t=1.65 YEARSEXP: t=9.7 AGE: t=1.89
1. What is your summary of the overall model? Is it one that you would trust from a statistical standpoint? Why? Be specific.
2. Using the model, what is the predicted effect of an additional year of experience? How confident are you in that?
3. What is the predicted value of salary for YOU, given these results? Tell me your values for a. Female circle one Yes No b. Your first name? ___________ c. How many years of work experience do you have? _______________
d. What is your age? ____________ 4. Now, report the regression equation and show your calculation of the predicted Salary for YOU, given the model results and your values for female, name, years of experience, and age.
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What is the summary of the overall model of the data shown in question 1? Is it one that you would trust from a statistical standpoint? specifically why ?
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