Question #1
Multicollinearity can best be described as the condition in which the:
independent
variables in a regression have a high degree of correlation with one another
Multicollinearity is likely to be causing problems with your regression results if:
Hint: there are 2 correct answers
.
•
you have a high R?but the Overall Model Ftest indicates that the model is
not valid
•
the independent variables have one or more correlation coefficients above .
50
•
the independent variables have one or more correlation coefficients near 0
•
you have a very low R?but the Overall Model Ftest indicates a valid model
•
the Overall Model Ftest indicates a valid model but the individual t
tests indicate none of the independent variables are linearly related to
the dependent variable
•
the independent variables have one or more correlation coefficients
above .80
•
a majority of independent variables appear not to be linearly related to the
dependent variable
Multicollinearity can result in:
Hint: there are 4 correct answers
.
•
an inability to interpret R?
•
an increased value for SST
•
a decrease in the absolute value of the Overall Model Ftest test statistic
•
a decrease in the absolute value of the individual ttest test statistics
•
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 Spring '09
 PETRY
 Normal Distribution, Regression Analysis, multicollinearity, independent variables, overall model Ftest

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