Saint GBA334 week 3 quiz (in class) - 90318

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Question 1. 1. The variable to be predicted is the dependent variable True False (Points : 4) Question 2. 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 (Points : 4) 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. 3. A judgmental forecasting technique that uses decision makers, staff personnel, and respondent to determine a forecast (Points : 4) exponential smoothing. the Delphi method. jury of executive opinion. sales force composite. consumer market survey. Question 4. 4. Which of the following statements about scatter diagrams is true? (Points : 4) 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. 5. Which of the following is not classified as a qualitative forecasting model? (Points : 4) exponential smoothing Delphi method jury of executive opinion sales force composite consumer market survey Question 6. 6. The correlation coefficient resulting from a particular regression analysis was 0.25. What was the coefficient of determination? (Points : 4) 0.5 -0.5 0.0625 There is insufficient information to answer the question. None of the above Question 7. 7. Which of the following is a technique used to determine forecasting accuracy? (Points : 4) exponential smoothing moving average regression Delphi method Mean absolute percent error Question 8. 8. The condition of an independent variable being correlated to one or more other independent variables is referred to as (Points : 4) multicollinearity. statistical significance. linearity. nonlinearity. The significance level for the F-test is not valid. Question 9. 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 (Points : 4) 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. 10. The coefficient of determination resulting from a particular regression analysis was 0.85. What was the correlation coefficient, assuming a positive linear relationship? (Points : 4) 0.5 -0.5 0.922 There is insufficient information to answer the question. None of the above Question 11. 11. Time-series models attempt to predict the future by using historical data. (Points : 4) True False Question 12. 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 (Points : 4) 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. 13. One purpose of regression is to predict the value of one variable based on the other variable. (Points : 4) True False Question 14. 14. A moving average forecasting method is a causal forecasting method. (Points : 4) True False Question 15. 15. The most common quantitative causal model is regression analysis. (Points : 4) True False Question 16. 16. The Delphi method solicits input from customers or potential customers regarding their future purchasing plans. (Points : 4) True False Question 17. 17. Which of the following methods tells whether the forecast tends to be too high or too low? (Points : 4) MAD MSE MAPE decomposition bias Question 18. 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. (Points : 4) 14 13 15 28 12.5 Question 19. 19. In regression, an independent variable is sometimes called a response variable. (Points : 4) True False Question 20. 20. The correlation coefficient has values between ?1 and +1. (Points : 4) True False Question 21. 21. The coefficient of determination takes on values between -1 and + 1. (Points : 4) True False Question 22. 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 (Points : 4) 116.7. 126.3. 168.3. 127.7. 135.0. Question 23. 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 (Points : 4) 14.5. 13.5. 14. 12.25. 12.75. Question 24. 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 (Points : 4) 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. 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 (Points : 4) 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
Solution Description


Question 1. 1.

The variable to be predicted is the dependent variable

True

False

(Points : 4)

Question 2. 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

(Points : 4)

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. 3.

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

(Points : 4)

exponential smoothing.

the Delphi method.

jury of executive opinion.

sales force composite.

consumer market survey.

Question 4. 4. Which of the following statements about scatter diagrams is true?

(Points : 4)

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. 5.

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

(Points : 4)

exponential smoothing

Delphi method

jury of executive opinion

sales force composite

consumer market survey

Question 6. 6.

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

(Points : 4)

0.5

-0.5

0.0625

There is insufficient information to answer the question.

None of the above

Question 7. 7.

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

(Points : 4)

exponential smoothing

moving average

regression

Delphi method

Mean absolute percent error

Question 8. 8.

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

(Points : 4)

multicollinearity.

statistical significance.

linearity.

nonlinearity.

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

Question 9. 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

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