Question

1. The reason that we obtain the best-fitting line as our regression equation is that we mathematically calculate the line with the smallest amount of total squared error.

T

F

2. Multiple regression is used to predict the value of a single DV from a weighted, linear combination of IVs.

T

F

3. The coefficient of determination in multiple regression is the proportion of DV variance that can be explained by at least one IV.

T

F

4. Multicollinearity is desirable in multiple regression.

T

F

5. Multicollinearity tends to increase the variances in regression coefficients, which ultimately results in a more stable prediction equation.

T

F

6. Tolerance is a measure of collinearity among IVs, where possible values range from 0–1. T/F

Answer 1

1) T (According to the basic theory of Multiple Regression)

2) F ( The IV 's may or may not be weighted)

3) T (According to the literature)

4) F (Multicollinearity is not desirable)

5) F (If the variances increase, then the prediction eqn gets unstable)

6) T (tolerance is equal to 1- coefficient of determination and hence lies between 0 and 1)

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