Question 1

Part of a regression output is provided below.Some of the information has been omitted.  The approximate value of F is:

Accepted Answer

A)   1605.7.
B)   0.9134.
C)   89.66.
D)   impossible to calculate with the given information.E) B) and C)
F) A) and C)

Question 2

Which statement about leverage is incorrect?

Accepted Answer

A)   Leverage refers to an observation's distance from the mean of X.
B)   If n = 40 and k = 4 predictors,a leverage statistic of .15 would indicate high leverage.
C)   If n = 180 and k = 3 predictors,a leverage statistic of .08 would indicate high leverage.D) None of the above
E) A) and B)

Question 3

If you rerun a regression,omitting a predictor X5,which would be unlikely?

Accepted Answer

A)   The new R2 will decline if X5 was a relevant predictor.
B)   The new standard error will increase if X5 was a relevant predictor.
C)   The remaining estimated β's will change if X5 was collinear with other predictors.
D)   The numerator degrees of freedom for the F test will increase.E) C) and D)
F) A) and B)

Question 4

Heteroscedasticity exists when all the errors (residuals)have the same variance.

Accepted Answer

A) True 
 B)False

Question 5

There is one residual for each predictor in the regression model.

Accepted Answer

A) True 
 B)False

Question 6

Analyze the regression results below (n = 33 cars in 1993)using the concepts you have learned about multiple regression.Circle things of interest and write comments in the margin.Make a prediction for CityMPG for a car with EngSize = 2.5,ManTran = 1,Length = 184,Wheelbase = 104,Weight = 3000,and Domestic = 0 (show your work).The variables are CityMPG = city MPG (miles per gallon by EPA rating);EngSize = engine size (liters);ManTran = 1 if manual transmission available,0 otherwise;Length = vehicle length (inches);Wheelbase = vehicle wheelbase (inches);Weight = vehicle weight (pounds);Domestic = 1 if U.S.manufacturer,0 otherwise.

Accepted Answer

The regression is significant overall (F...

Question 7

The effect of a binary predictor is to shift the regression intercept.

Accepted Answer

A) True 
 B)False

Question 8

Given that the fitted regression is Y = 76.40 -6.388X1 + 0.870X2,the standard error of b1 is 1.453,and n = 63.At α = .05,we can conclude that X1 is a significant predictor of Y.

Accepted Answer

A) True 
 B)False

Question 9

Which of the following would be most useful in checking the normality assumption of the errors in a regression model?

Accepted Answer

A)   The t-statistics for the coefficients
B)   The F-statistic from the ANOVA table
C)   The histogram of residuals
D)   The VIF statistics for the predictorsE) None of the above
F) B) and D)

Question 10

Heteroscedasticity of residuals in regression suggests that there is:

Accepted Answer

A)   nonconstant variation in the errors.
B)   multicollinearity among the predictors.
C)   nonnormality in the errors.
D)   lack of independence in successive errors.E) B) and C)
F) All of the above

Question 11

A binary (categorical)predictor should not be used along with nonbinary predictors.

Accepted Answer

A) True 
 B)False

Exam 13: Multiple Regression

Given that the fitted regression is Y = 76.40 -6.388X₁ + 0.870X₂,the standard error of b₁ is 1.453,and n = 63.At α = .05,we can conclude that X₁ is a significant predictor of Y.

Correct Answer
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Heteroscedasticity exists when all the errors (residuals)have the same variance.

Correct Answer
verified

The effect of a binary predictor is to shift the regression intercept.

Correct Answer
verified

Part of a regression output is provided below.Some of the information has been omitted. The approximate value of F is:

Correct Answer
verified

There is one residual for each predictor in the regression model.

Correct Answer
verified

Correct Answer
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The regression is significant overall (F...
View Answer

Heteroscedasticity of residuals in regression suggests that there is:

Correct Answer
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Which of the following would be most useful in checking the normality assumption of the errors in a regression model?

Correct Answer
verified

Which statement about leverage is incorrect?

Correct Answer
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A binary (categorical)predictor should not be used along with nonbinary predictors.

Correct Answer
verified

If you rerun a regression,omitting a predictor X₅,which would be unlikely?

Correct Answer
verified

Exam 13: Multiple Regression

Given that the fitted regression is Y = 76.40 -6.388X1 + 0.870X2,the standard error of b1 is 1.453,and n = 63.At α = .05,we can conclude that X1 is a significant predictor of Y.

Correct AnswerverifiedShow Answer

Heteroscedasticity exists when all the errors (residuals)have the same variance.

Correct AnswerverifiedShow Answer

The effect of a binary predictor is to shift the regression intercept.

Correct AnswerverifiedShow Answer

Part of a regression output is provided below.Some of the information has been omitted. The approximate value of F is:

Correct AnswerverifiedShow Answer

There is one residual for each predictor in the regression model.

Correct AnswerverifiedShow Answer

Correct AnswerverifiedThe regression is significant overall (F...View AnswerShow Answer

Heteroscedasticity of residuals in regression suggests that there is:

Correct AnswerverifiedShow Answer

Which of the following would be most useful in checking the normality assumption of the errors in a regression model?

Correct AnswerverifiedShow Answer

Which statement about leverage is incorrect?

Correct AnswerverifiedShow Answer

A binary (categorical)predictor should not be used along with nonbinary predictors.

Correct AnswerverifiedShow Answer

If you rerun a regression,omitting a predictor X5,which would be unlikely?

Correct AnswerverifiedShow Answer

Given that the fitted regression is Y = 76.40 -6.388X₁ + 0.870X₂,the standard error of b₁ is 1.453,and n = 63.At α = .05,we can conclude that X₁ is a significant predictor of Y.

Correct Answer
verified

Correct Answer
verified

Correct Answer
verified

Correct Answer
verified

Correct Answer
verified

Correct Answer
verified
The regression is significant overall (F...
View Answer

Correct Answer
verified

Correct Answer
verified

Correct Answer
verified

Correct Answer
verified

If you rerun a regression,omitting a predictor X₅,which would be unlikely?

Correct Answer
verified