Which of the following statements regarding multicollinearity is not true?
a. It exists in virtually all multiple regression models.
b. It is also called collinearity and intercorrelation.
c. It is a condition that exists when the independent variables are highly correlated with the dependent variable.
d. It does not affect the F-test of the analysis of variance.
e. It exists in virtually all multiple regression models and it is also called collinearity and intercorrelation.
Q. 2When the independent variables are correlated with one another in a multiple regression analysis, this condition is called:
a. heteroscedasticity
b. homoscedasticity
c. multicollinearity
d. causality
e. collinearity
Q. 3In regression analysis, multicollinearity refers to:
a. the response variables being highly correlated with one another
b. the predictor variables being highly correlated with one another
c. the response variable and the predictor variables are highly correlated with one another
d. the response variables are highly correlated over time
e. the predictor variables are highly correlated over time
Q. 4When multicollinearity is present, the estimated regression coefficients will have large standard error, causing imprecision in confidence and prediction intervals.
Indicate whether the statement is true or false
Q. 5When two or more of the predictor variables are highly correlated with one another, adding or deleting a predictor variable may cause significant changes in the values of the other regression coefficients.
Indicate whether the statement is true or false
Q. 6Multicollinearity will result in excessively low standard errors of the parameter estimates reported in the regression output.
Indicate whether the statement is true or false
Q. 7One of the consequences of multicollinearity in multiple regression is inflated standard errors in some or all of the estimated slope coefficients.
Indicate whether the statement is true or false
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