1.An analyst further studies the independent variables of a study she recently completed. The correlation matrix shown below is the result. Which statement best reflects possible problems with a multivariate regression?
| Age | Education | Experience | Income | Age | 1.00 |
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| Education | 0.50 | 1.00 |
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| Experience | 0.95 | 0.55 | 1.00 |
| Income | 0.60 | 0.65 | 0.89 | 1.00 |
A) Experience may be a redundant variable. B) Age should be excluded from the regression. C) Education may be unnecessary. D) Income is not needed.
2.An analyst runs a regression of portfolio returns on three independent variables. These independent variables are Price to Sales, Price to Cash Flow, and Price to Book. The analyst discovers that the p-values for each independent variable are relatively high. However, the F-test has a very small p-value. The analyst is puzzled and tries to figure out how the F-test can be statistically significant when the individual independent variables are not significant. What violation of regression analysis has occurred? A) serial correlation. B) conditional heteroskedasticity. C) unconditional heteroskedasticity. D) multicollinearity.
3.Which of the following statements regarding multicollinearity is FALSE? A) Multicollinearity may be a problem even if the Multicollinearity is not perfect. B) Multicollinearity makes it difficult to determine the contribution to explanation of the dependent variable of an individual explanatory variable. C) Multicollinearity may be present in any regression model. D) If the t-statistics for the individual independent variables are insignificant, yet he F-statistic is significant, this indicates the presence of Multicollinearity.
4.Which of the following is a potential remedy for multicollinearity? A) Take first differences of the dependent variable. B) Omit one or more of the collinear variables. C) Increase the sample size. D) Add dummy variables to the regression.
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