Reading 12: Multiple Regression and Issues in Regression A

change, product, percent, domestic, class

Q43. Werner Baltz, CFA, has regressed 30 years of data to forecast future sales for National Motor Company based on the percent change in gross domestic product (GDP) and the change in price of a U.S. gallon of fuel at retail. The results are presented below. Note: results must be multiplied by $1,000,000:

Coefficient Estimates

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Standard Error

Predictor

Coefficient

of the Coefficient

Intercept

13.710

∆1 GDP

30.22

12.120

∆2$ Fuel

−412.39

183.981

Analysis of Variance Table (ANOVA)

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Source

Degrees of Freedom

Sum of Squares

Mean Square

Regression

291.30

145.65

Error

132.12

Total

423.42

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In 2002, if GDP rises 2.2% and the price of fuels falls $0.15, Baltz’s model will predict Company sales in 2002 to be (in $ millions) closest to:

A) $128.

B) $206.

C) $82.

Q44. Baltz proceeds to test the hypothesis that none of the independent variables has significant explanatory power. He concludes that, at a 5% level of significance:

A) at least one of the independent variables has explanatory power, because the calculated F-statistic exceeds its critical value.

B) none of the independent variables has explanatory power, because the calculated F-statistic does not exceed its critical value.

C) all of the independent variables have explanatory power, because the calculated F-statistic exceeds its critical value.

Q45. Baltz then tests the individual variables, at a 5% level of significance, to determine whether sales are explained by individual changes in GDP and fuel prices. Baltz concludes that:

A) neither GDP nor fuel price changes explain changes in sales.

B) both GDP and fuel price changes explain changes in sales.

C) only GDP changes explain changes in sales.