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Saint GBA334 week 3 quiz (in class)

 

Question 1. 1.The variable to be predicted is the dependent variable.

(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 is called

(Points : 4)

exponential smoothing.

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 is helpful when forecasting with qualitative data.

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

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

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.


nonlinearity.

 

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)





 

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)


 

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)





 

Question 13. 13.One purpose of regression is to predict the value of one variable based on the other variable.

(Points : 4)


 

Question 14. 14. A moving average forecasting method is a causal forecasting method. (Points : 4)


 

Question 15. 15. The most common quantitative causal model is regression analysis. (Points : 4)


 

Question 16. 16.The Delphi method solicits input from customers or potential customers regarding their future purchasing plans.

(Points : 4)


 

Question 17. 17. Which of the following methods tells whether the forecast tends to be too high or too low? (Points : 4)

MAD
MSE


 

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

 

Question 19. 19.In regression, an independent variable is sometimes called a response variable.

(Points : 4)


 

Question 20. 20.The correlation coefficient has values between ?1 and +1.

(Points : 4)


 

Question 21. 21.The coefficient of determination takes on values between -1 and + 1.

(Points : 4)


 

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.

168.3.
135.0.
127.7.

 

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)


13.5.


 

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)





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