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1) Based on a sample of the salaries of professors at a major university, you have performed a multiple regression relating salary to years of...

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1) Based on a sample of the salaries of professors at a major university, you have performed a multiple regression relating salary to years of service and gender. The estimated multiple linear regression model is: Salary = $45,000 + $3000(Years) + $4000(Gender) + $1000[(Years)(Gender)] where Gender = 1 if the professor is male and Gender = 0 if the professor is female. Using the multiple linear regression equation, you would estimate the average diFerence in the salaries of a male professor with three years of service and female professor with three years of service to be: a) $3000 b) $5000 c) $7000 d) $4000 2) Based on a sample of the salaries of professors at a major university, you have performed a multiple regression relating salary to years of service and gender. The estimated multiple linear regression model is: Salary = $45,000 + $3000(Years) + $4000(Gender) + $1000[(Years)(Gender)] where Gender = 1 if the professor is male and Gender = 0 if the professor is female. Using the multiple linear regression equation, you would estimate the average salary of male professors with three years of experience to be: a) $54000 b) $61000 c) $53000 d) $58000 3) A real estate study was conducted in the Washington DC area
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to determine if certain variables are related to the sales price of a townhouse. As part of his investigation he ran the following multiple regression model: Sales Price = β 0 + β 1 (Square Feet) + β 2 (Distance) + ε i where the deviations ε i were assumed to be independent and normally distributed with mean 0 and standard deviation σ . The two explanatory variables are the square footage of the townhouse and the distance each townhouse is to a Metro stop. The data were obtained by selecting a random sample of 50 townhouses from the population of townhouses in the Washington DC area. This model was ±t to the data using the method of least squares. The following results were obtained from statistical software: Source df Sum of Squares Model 2 86,528 Error 47 2,722 Variable Parameter Est. Standard Error of Parameter Est. Constant –16.11 90.06 Square Feet 0.55935 0.07127 Distance –41.065 8.730 Based on the above analyses we can conclude: a) the variables Square Feet and Distance are both statistically signi±cant at level 0.05 as a predictor of the variable Sales Price in a multiple regression model. b) the variable Square Feet is not useful as a predictor of the variable Sales Price and should be omitted from the analysis c) only the variable Square Feet is statistically signi±cant at level 0.05 as a predictor of the variable Sales Price d) the variable Distance is not useful as a predictor of the variable Sales Price, and should be omitted from the analysis 4) A real estate study was conducted in the Washington DC area to determine if certain variables are related to the sales price of a townhouse. As part of his investigation he ran the following
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1) Based on a sample of the salaries of professors at a major university, you have performed a multiple regression relating
salary to years of service and gender. The estimated multiple
linear...

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