03 Jun Question Question 1.1. If the sum of squared error
Question
Question 1.1. If the sum of squared error variation of a variable Y is 28.95 and the sum of squared total variation of the regression is 290.60 the R square value for the regression is: (Points : 3) 11.9 99.5 88.1 78.3 90.0
Question 2.2. What happens to the regression F value if R2 increases holding everything else constant? (Points : 3) The F value decreases. The F value increases. The F value remains the same. It may increase or decrease since it cannot be determined.
Question 3.3. Given the Correlation Matrix below what two major problems can you foresee from the effort to forecast Sales using Price and Advertising as independent variables in multiple regression? Correlation Matrix
Variables Sales Price AdvertisingSales 1.00 .00
Price -.87 1.00 .01 .00
Advertising .45 -.79 1.00 .04 .02 .00
Income .36 .25 .21 .07 .04 .09 (Points : 3) Sales and Income do not have a significant relationship at the 95% confidence level. Price and Advertising may be collinear (Multicollnearity) Price and Sales do not have a significant linear relationship at the 95% confidence level. Both 1 and 2 above. There are not problems indicated with the correlations.
Question 4.4. What statistic is designed to detect multicollinearity in a regression model? (Points : 3) Durbin-Watson Coefficient t-value Standard error Variance Inflation Factor
Question 5.5. What is meant when we refer to multiple regression coefficients as partial or net regression coefficients? (Points : 3) Any independent variable only explains a portion of the variation in the dependent variable. The coefficients have significance levels that achieve the confidence level desired. The coefficients represent the average change in the dependent variable per unit change in the independent variable holding all other things constant. Both a and b above. None of the above.
Question 6.6. What statistic is used to determine if residuals are heteroscedastic? (Points : 3) Durbin Watson
R-square BK squared fit coefficient t-value Variance Inflation Factors 4th Lag LBQ
Question 7.7. The following study estimated demand equation for illicit drugs using a sample size of 4000. The significance level desired for the regression is 5%. The regression output is (Y= Number of days illicit drugs are used):Variable Coefficient Estimate t-ValueIntercept 3.089 17.98Alcohol Prices per liter 0.045 5.93Income ($1000) 0.000057 17.45Male (1 if male) 1.637 29.23Female (1 if female) 1.282 6.35Marital Status (1 if married) -0.807 1.13Age 12 to 20 (1 if between 12 & 20) -1.531 17.97Age 21 to 30 (1 if between 21 & 30) .035 0.51Black (1 if Black) .580 1.84Hispanic (1 if Hispanic) -0.564 -1.03Years in prison for felony drug possession -0.532 -4.72Which of the above variables would you not keep in the regression? (Points : 3) Discard only variables with negative t-values (Hispanic and Years in prison). Delete Marital Status, Age21-30, Black, and Hispanic. Discard Female. 2 and 3 above. Keep all of the variables.
Question 8.8. With time series data when error terms are significantly correlated (or autoregressive) the problem is termed (Points : 3) heteroscedascity. positive serial correlation. multicollinearity. negative serial correlation homoscedasticity
Question 9.9. GE earnings in billions of dollars are estimated using U.S GNP (Gross National Product) in billions of dollars. The regression equation is Y = .065 + .02X. Interpreting the equation (Points : 3) Y =.065, if the value when X is equal to zero. when X increases by one billion dollars the average change in Y is .02 billion dollars. when X increases by one billion dollars the average change in Y is .02 billion dollars plus .065 billion dollars. both 1 a
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