If a group of independent variables are not

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18. If a group of independent variables are not significant individually, but are significant as a group at a specified level of significance, this is most likely due to a) autocorrelation. b) the presence of dummy variables. c) the absence of dummy variables. d) collinearity. ANSWER: d TYPE: MC DIFFICULTY: Easy KEYWORDS: collinearity, assumption, properties
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Multiple Regression Model Building 205 19. As a project for his business statistics class, a student examined the factors that determined parking meter rates throughout the campus area. Data were collected for the price per hour of parking, blocks to the quadrangle, and one of the three jurisdictions: on campus, in downtown and off campus, or outside of downtown and off campus. The population regression model hypothesized is 0 1 1 2 2 3 3 i i i i i Y x x x β β β β ε = + + + + where Y is the meter price x 1 is the number of blocks to the quad x 2 is a dummy variable that takes the value 1 if the meter is located in downtown and off campus and the value 0 otherwise x 3 is a dummy variable that takes the value 1 if the meter is located outside of downtown and off campus, and the value 0 otherwise Suppose that whether the meter is located on campus is an important explanatory factor. Why should the variable that depicts this attribute not be included in the model? 20. True or False: The Variance Inflationary Factor (VIF) measures the correlation of the X variables with the Y variable. ANSWER: False TYPE: TF DIFFICULTY: Moderate KEYWORDS: variance inflationary factor, collinearity 21. True or False: Collinearity is present when there is a high degree of correlation between independent variables. ANSWER: True TYPE: TF DIFFICULTY: Easy KEYWORDS: collinearity 22. True or False: Collinearity is present when there is a high degree of correlation between the dependent variable and any of the independent variables. ANSWER: False TYPE: TF DIFFICULTY: Moderate KEYWORDS: collinearity, properties
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206 Multiple Regression Model Building 23. True or False: A high value of R 2 significantly above 0 in multiple regression, accompanied by insignificant t -values on all parameter estimates, very often indicates a high correlation between independent variables in the model. ANSWER: True TYPE: TF DIFFICULTY: Difficult KEYWORDS: collinearity, properties 24. True or False: One of the consequences of collinearity in multiple regression is inflated standard errors in some or all of the estimated slope coefficients. ANSWER: True TYPE: TF DIFFICULTY: Easy KEYWORDS: collinearity, properties 25. True or False: One of the consequences of collinearity in multiple regression is biased estimates on the slope coefficients.
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Christopher Reinemann
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