Reference no: EM13920434

Question 1. 

The variable to be predicted is the dependent variable

True

False

Question 2. 

If computing a causal linear regression model of Y = a + bX and the resultant r2 is very near zero, then one would be able to conclude that

Y = a + bX is a good forecasting method.

Y = a + bX is not a good forecasting method.

a multiple linear regression model is a good forecasting method for the data.

a multiple linear regression model is not a good forecasting method for the data.

None of the above

Question 3. 

A judgmental forecasting technique that uses decision makers, staff personnel, and respondent to determine a forecast

exponential smoothing.

the Delphi method.

jury of executive opinion.

sales force composite.

consumer market survey.

Question 4.

Which of the following statements about scatter diagrams is true?

Time is always plotted on the y-axis.

It can depict the relationship among three variables simultaneously.

It is helpful when forecasting with qualitative data.

The variable to be forecasted is placed on the y-axis.

It is not a good tool for understanding time-series data.

Question 5.

Which of the following is not classified as a qualitative forecasting model?

exponential smoothing

Delphi method

jury of executive opinion

sales force composite

consumer market survey

Question 6. 

The correlation coefficient resulting from a particular regression analysis was 0.25. What was the coefficient of determination?

0.5

-0.5

0.0625

There is insufficient information to answer the question.

None of the above

Question 7. 

Which of the following is a technique used to determine forecasting accuracy?

exponential smoothing

moving average

regression

Delphi method

Mean absolute percent error

Question 8. 

The condition of an independent variable being correlated to one or more other independent variables is referred to as

multicollinearity.

statistical significance.

linearity.

nonlinearity.

The significance level for the F-test is not valid.

Question 9. 

A prediction equation for starting salaries (in $1,000s) and SAT scores was performed using simple linear regression. In the regression printout shown below, what does the coefficient of determination of 0.87425889 mean?

SUMMARY OUTPUT                   Regression Statistics                   Multiple R   0.935018125     R-Square   0.87425889     Adjusted R-Square   0.860287655     Standard Error   3.3072944     Observations   11               ANOVA   df F Significance F           Regression   1 62.57564 0.000024 Residual   9     Total   10                   Coefficients t-Statistics p-Value Intercept   -29.1406 -3.36493 0.008324 SAT   0.06544 7.910476 0.0000242

 

A coefficient of determination of 0.87425889 means that there is a strong correlation between starting salaries and SAT scores.

A coefficient of determination of 0.87425889 means that SAT is not a good predictor of starting salaries

A coefficient of determination of 0.87425889 means that 87.425889 percent changes in starting salaries have been accounted for by changes in SAT scores.

A coefficient of determination is not a good measure of the relationship between starting salaries and SAT scores.
None of the above

Question 10. 

The coefficient of determination resulting from a particular regression analysis was 0.85. What was the correlation coefficient, assuming a positive linear relationship?

0.5

-0.5

0.922

There is insufficient information to answer the question.

None of the above

Question 11.

Time-series models attempt to predict the future by using historical data. 

True

False

Question 12. 

A prediction equation for starting salaries (in $1,000s) and SAT scores was performed using simple linear regression. In the regression printout shown below, what does the significance F meanl?

SUMMARY OUTPUT                   Regression Statistics                   Multiple R   0.935018125     R-Square   0.87425889     Adjusted R-Square   0.860287655     Standard Error   3.3072944     Observations   11               ANOVA   df F Significance F           Regression   1 62.57564 0.000024 Residual   9     Total   10                   Coefficients t-Statistics p-Value Intercept   -29.1406 -3.36493 0.008324 SAT   0.06544 7.910476 0.0000242

The significance F means that starting salary is a good predictor of SAT scores.

The significance F means that SAT score is a good predictor of starting salary.

The significance F means that SAT score is not a good predictor of starting salary.
The significance F means that starting salary is not a good predictor of SAT score.

None of the above.

Question 13. 

One purpose of regression is to predict the value of one variable based on the other variable.

True 

False 

Question 14. 

A moving average forecasting method is a causal forecasting method.

 True 

False 

Question 15. 

The most common quantitative causal model is regression analysis.

True

False

Question 16. 

The Delphi method solicits input from customers or potential customers regarding their future purchasing plans.

 True 

False 

Question 17. 

Which of the following methods tells whether the forecast tends to be too high or too low?

MAD

MSE

MAPE

decomposition

bias

Question 18.

Daily demand for newspapers for the last 10 days has been as follows: 12, 13, 16, 15, 12, 18, 14, 12, 13, 15 (listed from oldest to most recent). Forecast sales for the next day using a two-day moving average.

14

13

15

28

12.5

Question 19. 

In regression, an independent variable is sometimes called a response variable.

True

False

Question 20. 

The correlation coefficient has values between ?1 and +1.

True

False

Question 21. 

The coefficient of determination takes on values between -1 and + 1.

True 

False 

Question 22.

Enrollment in a particular class for the last four semesters has been 122, 128, 100, and 155 (listed from oldest to most recent). The best forecast of enrollment next semester, based on a three-semester moving average, would be

116.7.

126.3.

168.3.

127.7.

135.0.

Question 23. 

Daily demand for newspapers for the last 10 days has been as follows: 12, 13, 16, 15, 12, 18, 14, 12, 13, 15 (listed from oldest to most recent). Forecast sales for the next day using a two-day weighted moving average where the weights are 3 and 1 are

14.5.

13.5.

14.

12.25.

12.75.

Question 24. 

A prediction equation for starting salaries (in $1,000s) and SAT scores was performed using simple linear regression. In the regression printout shown below, what can be said about the level of significance for the overall model?

SUMMARY OUTPUT               Regression Statistics               Multiple R 0.935018125     R-Square 0.87425889     Adjusted R-Square 0.860287655     Standard Error 3.3072944     Observations 11             ANOVA df F Significance F         Regression 1 62.57564 0.000024 Residual 9     Total 10               Coefficients t-Statistics p-Value Intercept -29.1406 -3.36493 0.008324 SAT 0.06544 7.910476 0.0000242

SAT is not a good predictor for starting salary.

The significance level for the intercept indicates the model is not valid.

The significance level for SAT indicates the slope is equal to zero.

The significance level for SAT indicates the slope is not equal to zero.

None of the above

Question 25.

A prediction equation for starting salaries (in $1,000s) and SAT scores was performed using simple linear regression. In the regression printout shown below, what is the regression equation?

SUMMARY OUTPUT                   Regression Statistics                   Multiple R   0.935018125     R-Square   0.87425889     Adjusted R-Square   0.860287655     Standard Error   3.3072944     Observations   11               ANOVA   df F Significance F           Regression   1 62.57564 0.000024 Residual   9     Total   10                   Coefficients t-Statistics p-Value Intercept   -29.1406 -3.36493 0.008324 SAT   0.06544 7.910476 0.0000242

Starting Salaries = 0.06544 - 29.1406SAT

Starting Salaries = -29.1406 + 0.06544SAT

Starting Salaries = 0.935018125 + 0.6544SAT

Starting Salaries = 0.87425889 + 0.06544SAT

None of the above