Question 1
Which of the following is not a possible indicator of multicollinearity?
◦ non-significant
t-tests for individual
β parameters when the
F-test for overall model adequacy is significant
◦ non-random patterns in the plot of the residuals versus the fitted values
◦ signs opposite from what is expected in the estimated β parameters
◦ significant correlations between pairs of independent variables
Question 2
The printout below shows part of the least squares regression analysis for the model
![](data:image/png;base64, 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)
fit to a set of data. The model attempts to predict a score on the final exam in a statistics course based on the scores on the first two tests in the class.
![](data:image/png;base64, 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)
Is there evidence of multicollinearity in the printout? Explain.