Figure 1 shows Excel output estimating the following model: Salary = ? 0 + ? 1 M

Question

Figure 1 shows Excel output estimating the following model:

Salary= ?0 + ?1Male + ?2Experience + ?3Over50 + ?4MBA + ?

Where, Salary = a person’s salary in thousands of dollars, Male = 1 if the person is a male (0 otherwise),Experience = number of years experience, Over50 = 1 if the person is over 50 (0 otherwise) and MBA =1 if the person has an MBA (0 otherwise).

Ordinary Least Squares (OLS) produces coefficient estimates by

Select one:

a. maximizing SSE

b. minimizing SSE

c. maximizing SST

d. minimizing SSR

Figure 4 shows Excel output estimating the following model:

HousingPrice = ?0 + ?1SqFeet + ?2LotSize + ?3Baths + ?4BathSinks + ?

Where, HousingPrice = the price of a home in thousands of dollars, SqFeet = square footage of the home, Lotsize= size of the lot in thousand square feet, Baths = number of bathrooms and BathSinks = number of bathroom sinks.

Which of the independent variables show signs of high correlation with one or more of the other independent variables (i.e., high multicolinearity)?

Select one:

a. Square Feet

b. Lot Size

c. Square Feet and Lot Size

d. Baths and BathSinks

e. BathSinks

Explanation / Answer

Ordinary Least Squares (OLS) produces coefficient estimates by

b. minimizing SSE

Highly correlated

Which of the independent variables show signs of high correlation with one or more of the other independent variables (i.e., high multicolinearity)?

d. Baths and BathSinks

VIF Status of predictors VIF = 1 Not correlated 1 < VIF < 5 Moderately correlated VIF > 5 to 10

Highly correlated

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