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Question

HW #8

Due: 11/06/15

#1) Data 7-03 has data on the list price of 82 new 1995 American-made cars and a variety of characteristics of the car. The dependent variable is the PRICE, which is the list price of the vehicle in thousand of dollars.

The explanatory variables are:

Hatch = 1 for a hatchback, 0 for a sedan

Wbase = wheelbase in inches.

Length = length of the car in inches

Width = width of the car in inches

Height = height of the car in inches.

Weight = weight of the car in inches

Cyl = number of cylinders in the engine.

Liters = engine displacement

Gasmpg = estimated gas mile per gallon (City and freeway average)

Trans = 1 for automatic, o otherwise.

Without running the regression yet, identify which pair/ pairs of independent variables may be highly correlated. Explain why you think that is so.
Estimate the model and show the results. Give an interpretation for the coefficients of the dummy variables “hatch” and “trans.” Do the signs of the coefficients agree with your expectations? Explain.
What step would you take in order to correct for multicollinearity? Explain why you chose this solution.

2) DATA7-2 tabulates data on salaries and employment characteristics 49

employees in a certain company. The dependent variable is WAGE (in $).

The explanatory variables are:

EDUC = Years of education beyond 8th grade when hired (Range 1 - 11)

EXPER = Number of years at the company (Range 1 - 23)

AGE = Age of employee (25 - 64)

GENDER = 1 for male and 0 for female

RACE = 1 For white, 0 for non-white

Without running the regression yet, identify which pair/ pairs of independent variables may be highly correlated. Explain why you think that is so.

b. Run a correlation matrix for the explanatory variables. Based on your results, would you expect to see multicollinearity between some of the variables? Which ones?

State and explain the signs you expect for each explanatory variable.