We expect virtually all the data in a stable process to fall within 2 standard deviations of the mean.
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Q. 2A regression model that is deemed to have a regression slope coefficient that could be equal to zero should not be used for prediction since there is no established linear relationship between the x and y variable.
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Q. 3It is possible for the standard error of the estimate to actually increase if variables are added to the model that do not aid in explaining the variation in the dependent variable.
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Q. 4In a contingency analysis, the greater the difference between the actual and the expected frequencies, the more likely:
A) H0 should be rejected.
B) H0 should be accepted.
C) we cannot determine H0.
D) the smaller the test statistic will be.
Q. 5An advantage of exponential smoothing techniques over a regression-based trend model is that the exponential smoothing model allows us to weigh each observation equally, thereby giving a fairer method of developing a forecast.
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Q. 6A manufacturing company is interested in predicting the number of defects that will be produced each hour on the assembly line. The managers believe that there is a relationship between the defect rate and the production rate per hour.
The managers believe that they can use production rate to predict the number of defects. The following data were collected for 10 randomly selected hours.
Defects Production Rate Per Hour
20 400
30 450
10 350
20 375
30 400
25 400
30 450
20 300
10 300
40 300
Given these sample data, the simple linear regression model for predicting the number of defects is approximately = 5.67 + 0.048x.
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Q. 7Standard stepwise regression combines attributes of both forward selection and backward elimination.
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Q. 8The prediction interval developed from a simple linear regression model will be at its narrowest point when the value of x used to predict y is equal to the mean value of x.
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