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

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

D) at least one of the independent variables has explanatory power, because the calculated F-statistic does not exceed its critical value. 

aianjie

 

The correct answer is A

From the ANOVA table, the calculated F-statistic is (mean square regression / mean square error) = 145.65 / 4.89 = 29.7853. From the F distribution table (2 df numerator, 27 df denominator) the F-critical value may be interpolated to be 3.36. Because 29.7853 is greater than 3.36, Baltz rejects the null hypothesis and concludes that at least one of the independent variables has explanatory power.

 

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14、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) only GDP changes explain changes in sales.

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

D) only fuel price changes explain changes in sales.

aianjie

 

The correct answer is C

From the ANOVA table, the calculated t-statistics are (30.22 / 12.12) = 2.49 for GDP and (?412.39 / 183.981) = ?2.24 for fuel prices. These values are both outside the t-critical value at 27 degrees of freedom of ±2.052. Therefore, Baltz is able to reject the null hypothesis that these coefficients are equal to zero, and concludes that each variable is important in explaining sales.

 

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15、Consider the following regression equation:

Salesi = 20.5 + 1.5 R&Di + 2.5 ADVi – 3.0 COMPi

where Sales is dollar sales in millions, R&D is research and development expenditures in millions, ADV is dollar amount spent on advertising in millions, and COMP is the number of competitors in the industry.

Which of the following is NOT a correct interpretation of this regression information?

A) If a company spends $1 more on R&D (holding everything else constant), sales are expected to increase by $1.5 million.

B) If R&D and advertising expenditures are $1 million each and there are 5 competitors, expected sales are $9.5 million.

C) One more competitor will mean $3 million less in sales (holding everything else constant).

D) Increasing advertising dollars by $1 million (holding everything else constant), will result in $2.5 million additional sales.

aianjie

 

The correct answer is A

If a company spends $1 million more on R&D (holding everything else constant), sales are expected to increase by $1.5 million. Always be aware of the units of measure for the different variables.

 

aianjie

 

16、Consider the following regression equation:

Salesi = 10.0 + 1.25 R&Di + 1.0 ADVi – 2.0 COMPi + 8.0 CAPi

where Sales is dollar sales in millions, R&D is research and development expenditures in millions, ADV is dollar amount spent on advertising in millions, COMP is the number of competitors in the industry, and CAP is the capital expenditures for the period in millions of dollars.

Which of the following is NOT a correct interpretation of this regression information?

A) If a company spends $1 million more on capital expenditures (holding everything else constant), Sales are expected to increase by $8.0 million.

B) One more competitor will mean $2 million less in Sales (holding everything else constant).

C) If R&D and advertising expenditures are $1 million each, there are 5 competitors, and capital expenditures are $2 million, expected Sales are $8.25 million.

D) Increasing advertising dollars by $1 million (holding everything else constant), will result in $1 million additional Sales.

aianjie

 

The correct answer is C

Predicted sales = $10 + 1.25 + 1 – 10 + 16 = $18.25 million.

 

aianjie

 

17、Henry Hilton, CFA, is undertaking an analysis of the bicycle industry. He hypothesizes that bicycle sales (SALES) are a function of three factors: the population under 20 (POP), the level of disposable income (INCOME), and the number of dollars spent on advertising (ADV). All data are measured in millions of units. Hilton gathers data for the last 20 years. Which of the follow regression equations correctly represents Hilton’s hypothesis?

A) SALES = α x β1 POP x β2 INCOME x β3 ADV x ε.

B) INCOME = α + β1 POP + β2 SALES + β3 ADV + ε.

C) SALES = α + β1 POP + β2 INCOME + β3 ADV + ε.

D) INCOME = α + β1 POP + β2 ADV + ε.

aianjie

 

The correct answer is C

SALES is the dependent variable. POP, INCOME, and ADV should be the independent variables (on the right hand side) of the equation (in any order). Regression equations are additive.