Answer to Question 1
D
Answer to Question 2
There are several clues to the presence of multicollinearity: (1) an independent variable known to be an important predictor ends up having a partial regression coefficient that is not significant; (2) a partial regression coefficient exhibits the wrong sign; and/or, (3) when an independent variable is added or deleted, the partial regression coefficients for the other variables change dramatically. A more practical way to identify multicollinearity is through the examination of a correlation matrix, which is a matrix that shows the correlation of each variable with each of the other variables. A high correlation between two independent variables is an indication of multicollinearity.
Went and Briggs demonstrated the relationship between light perception and auxin function.
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