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# "multicollinearity" problem

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Suppose you want to estimate a model of women's earnings at age 50. You have data for a sample of employed women, provided by the alumni associations of Mills College and Smith College, on:
? A woman's salary at age 50
? Her age
? Her high school GPA
? Her college GPA
? Her college major
? Her job tenure (how many years she has been with employer)
? The fraction of her household income that she earns
The question below violate assumptions of the classical model

1.Say that your regression results include a large, positive coefficient on ECON MAJOR, and you tell all your fellow students about it. Why might this coefficient be an overestimate of the average gain in future salary that your fellow students should expect just by switching their major to economics?

2. If you notice that virtually all the Econ majors in your sample are from Mills College, what problem will you have with the following regression model?
SALARY= &#946;0 + &#946;1 COLLEGE GPA + &#946;2 MILLS GRAD + &#946;3 ECON MAJOR + &#949;

3.Suppose that you run two versions of your model. In the first, SALARY is expressed in thousands of dollars. In the other, it is expressed in dollars. Will this affect the size of your coefficients? The size of the standard errors? The adjusted R2? Explain