MKT 555 Quiz 4 - CH. 5

The personal department... Subsequently, the workers... Y = -212 + 1.90MD + 2.0MA + 0.25PA (2.50) (2.36) (2.60) Notes: the number shown in parentheses below the coefficients are t-ratios for the corresponding variable. The adjusted R^2 was 0.75. Which of the following is closest to the table t-value to test whether a regression coefficient is statistically significant at the 0.05 significance level (one-tailed) for this problem?

1.65

The personal department... Subsequently, the workers... Y = -212 + 1.90MD + 2.0MA + 0.25PA (2.50) (2.36) (2.60) Notes: the number shown in parentheses below the coefficients are t-ratios for the corresponding variable. The adjusted R^2 was 0.75. What is the correct estimate for the number of units of work completed by a worker with a manual dexterity score of 100, a mental aptitude score of 80 and a personnel assessment score of 10? Use the regression estimated, as given, to make this calculation.

140.5

A multiple regression model using 200 data points (with three independent variables) has how many degrees of freedom for testing the statistical significance of individual slope coefficients?

196

Consider the following multiple regression model of domestic car sales (DCS) where: DCS = domestic car sales in units sold DCSP = domestic car sales price (in dollars) PR = prime rate as a percent Q2 = quarter 2 dummy variable Q3 = quarter 3 dummy variable Multiple regression - result formula DCS = 3,000 + -0.09*DCSP -20.0*PR + 293*Q2 + 149*Q3 Durbin Watson = 1.92 AIC = 492.5 MAPE 5.30% BIC 495.9 Adj R-square = 75.64% SEE = 100 What will be the approximate 95% confidence interval for the DCS prediction?

3,500 to 3,900

Consider the following multiple regression model of domestic car sales (DCS) where: DCS = domestic car sales in units sold DCSP = domestic car sales price (in dollars) PR = prime rate as a percent Q2 = quarter 2 dummy variable Q3 = quarter 3 dummy variable Multiple regression - resul formula DCS = 3,000 + -0.09*DCSP -20.0*PR + 293*Q2 + 149*Q3 Durbin Watson = 1.92 AIC = 492.5 MAPE 5.30% BIC 495.9 Adj. R-square 75.64% For the domestic car sales regression above, assume that: DCSP = $10,000 PR = 10 percent and that it is the first quarter of the year. What will DCS be predicted to be by the regression model?

3,700

The personal department... Subsequently, the workers... Y = -212 + 1.90MD + 2.0MA + 0.25PA (2.50) (2.36) (2.60) Notes: the number shown in parentheses below the coefficients are t-ratios for the corresponding variable. The adjusted R^2 was 0.75. What percent of the variation of units of work completed can be explained by this model?

75

The following is an estimated demand function.. Q = 875 + 6Xa + 15Y - 5P Where Q is quantity sold, XA is advertising expenditure (in thousands of dollars), Y is income (in thousands of dollars), and P is the good's price. The equation has been estimated from 10 years of quarterly data. The adj. R^2 was 0.92. The t-ratios are: for advertising 1.98, for income 2.12, for price -2.31. Suppose the values of the explanatory variables next period are: Advertising = $100,000, Income = $10,000, and Price = $100. Using the above regression, what is the predicted value of sales?

750.375

The following is an estimated demand function.. Q = 875 + 6Xa + 15Y - 5P Where Q is quantity sold, XA is advertising expenditure (in thousands of dollars), Y is income (in thousands of dollars), and P is the good's price. The equation has been estimated from 10 years of quarterly data. The adj. R^2 was 0.92. The t-ratios are: for advertising 1.98, for income 2.12, for price -2.31. For the above regression, approximately what percent of the variation in sales would be explained by this model?

92%

Dummy variables

All of the above

Forecasters who base model selection criteria on the maximization of R2 should

All of the above

Consider the following multiple regression model of domestic car sales (DCS) where: DCS = domestic car sales in units sold DCSP = domestic car sales price (in dollars) PR = prime rate as a percent Q2 = quarter 2 dummy variable Q3 = quarter 3 dummy variable Multiple regression - resul formula DCS = 3,000 + -0.09*DCSP -20.0*PR + 293*Q2 + 149*Q3 Durbin Watson = 1.92 AIC = 492.5 MAPE 5.30% BIC 495.9 Adj R-square = 75.64% SEE = 100 The domestic car sales model

All of the options are correct

Which of the following is probably not a potential cause of data seasonality?

All of the options could be a potential cause of data seasonality

The following is an estimated demand function.. Q = 875 + 6Xa + 15Y - 5P Where Q is quantity sold, XA is advertising expenditure (in thousands of dollars), Y is income (in thousands of dollars), and P is the good's price. The equation has been estimated from 10 years of quarterly data. The adj. R^2 was 0.92. The t-ratios are: for advertising 1.98, for income 2.12, for price -2.31. According to the common 95 percent level of significance for the regression above,

All variables are probably significant

In using quarterly time series data, which quarter can serve as the base period for interpretation of dummy variables?

Any of the above

how would you model the effect of rain on attendance to a soccer game

Create a single rain dummy variable

The personal department... Subsequently, the workers... Y = -212 + 1.90MD + 2.0MA + 0.25PA (2.50) (2.36) (2.60) Notes: the number shown in parentheses below the coefficients are t-ratios for the corresponding variable. The adjusted R^2 was 0.75. Which of the following statements is the correct interpretation of the mental aptitude regression coefficient?

If we increase mental aptitude by one unit, holding the predictor variables constant, units of work completed will increase by an average of 2.0

Consider the following multiple regression model of domestic car sales (DCS) where: DCS = domestic car sales in units sold DCSP = domestic car sales price (in dollars) PR = prime rate as a percent Q2 = quarter 2 dummy variable Q3 = quarter 3 dummy variable Multiple regression - resul formula DCS = 3,266.66 + -0.098*DCSP -21.178*PR + 29.38*Q2 + 149Q3 Durbin Watson 1.62 MAPE 5.30% Adj. R-square 75.64% For the domestic car sales regression above, what does the "third quick check" show?

It shows that more than three-quarters of the variation in DCS is explained by the regression model.

Using the significance levels reported by ForecastXTM< at what level can we reject a one-sided null relating to a slope coefficients statistical significance such that we are 95% confidence?

MAYBE: none of the above - should be 0.05??

The F-statistic in the multiple regression model

NOT: All of the options are correct, non of the above, tests the significance of the R-sqaured statistic, is used to test for the presence of serial correlation, MAYBE: either hypothesis, null, goodness of fit

Quarterly seasonal dummy variables take on values

NOT: none of the above MAYBE: 1 to 3 or 3 (Should be 0 to 1)

The value of the F-statistic applied to multiple regression can be rewritten in terms of the estimated

None of the above

Consider the following multiple regression model of domestic car sales (DCS) where: DCS = domestic car sales in units sold DCSP = domestic car sales price (in dollars) PR = prime rate as a percent Q2 = quarter 2 dummy variable Q3 = quarter 3 dummy variable Multiple regression - resul formula DCS = 3,266.66 + -0.098*DCSP -21.178*PR + 29.38*Q2 + 149*Q3 Durbin Watson = 1.62 MAPE 5.30% Adj. R-square 75.64% For the domestic car sales regression, which variable coefficients pass the "second quick check" (i.e., statistical significance)?

Not enough information is included to make a decision about statistical significance

Consider the following multiple regression model of domestic car sales (DCS) where: DCS = domestic car sales in units sold DCSP = domestic car sales price (in dollars) PR = prime rate as a percent Q2 = quarter 2 dummy variable Q3 = quarter 3 dummy variable Multiple regression - resul formula DCS = 3,266.66 + -0.098*DCSP -21.178*PR + 29.38*Q2 + 149Q3 Durbin Watson 1.92 AIC 492.5 MAPE 5.30% BIC 495.9 Adj. R-square 75.64% In the domestic car sales regression above, what evidence do you have of any pattern in the error terms?

The Durbin-Watson statistic indicates little patterns in the error terms

Which of the following is not correct? Seasonality in a time series data set containing quarterly observations can be handled by

Using four dummy variables, one for each season

Consider the following multiple regression model of domestic car sales (DCS) where: DCS = domestic car sales in units sold DCSP = domestic car sales price (in dollars) PR = prime rate as a percent Q2 = quarter 2 dummy variable Q3 = quarter 3 dummy variable Multiple regression - resul formula DCS = 3,000 + -0.09*DCSP -20.0*PR + 293*Q2 + 149*Q3 Durbin Watson = 1.92 AIC = 492.5 MAPE 5.30% BIC 495.9 Adj R-square = 75.64% SEE = 100 In the domestic car sales function, there is evidence of seasonality. How does the regression model show this evidence?

With the Q2 and Q3 variables

Consider the following multiple regression model of domestic car sales (DCS) where: DCS = domestic car sales in units sold DCSP = domestic car sales price (in dollars) PR = prime rate as a percent Q2 = quarter 2 dummy variable Q3 = quarter 3 dummy variable Multiple regression - result formula DCS = 3,266.66 + -0.098*DCSP -21.178*PR + 29.38*Q2 + 149*Q3 Durbin Watson 1.62 MAPE 5.30 Adj. R-square 75.64% Does the regression pass the "first quick check (i.e., economic logic)?"

Yes, because the signs of all the regression coefficients make economic sense

The inclusion of seasonal dummy variables to a multiple regression model may help eliminate

autocorrelation if the data are characterized by seasonal fluctuations

(Graph) Which identified point, if removed, will have the largest effect on fitted regression line as shown in the above figure (the dashed line is the regression line)?

d (furthest point from the line)

The personal department... Subsequently, the workers... Y = -212 + 1.90MD + 2.0MA + 0.25PA (2.50) (2.36) (2.60) Notes: the number shown in parentheses below the coefficients are t-ratios for the corresponding variable. The adjusted R^2 was 0.75. Which variables are making a significance contribution to the prediction of units of work completed at a 95% confidence level?

mental aptitude & manual dexterity (bc t-stats over 2 indicate statistically significant)

Including male and female dummy variables in the same regression to represent sex will likely result in

perfect multicollinearity