Question

1. The variance inflation factor (VIF) for a given predictor “indicates whether there exists a strong linear association between it and all remaining predictors” (Stevens, 2001).T/F
2. In standard multiple regression, the IV that has the highest correlation with the DV is entered into the analysis first.T/F

3. Sequential multiple regression is also sometimes referred to as statistical multiple regression.T/F

4. Stepwise multiple regression is often used in studies that are explanatory in nature. T/F

5. Model validation, sometimes called model cross-validation, is an important issue in multiple regression. T/F

Answer 1

1) T(True)

Explanation:

Looking at correlations on among pairs of predictors. It is possible that the pairwise correlations are small, and yet a linear dependence exists among three or even more variables. That's why many regression analysts often rely on what are called variance inflation factors (VIF) to help detect multicollinearity.

A variance inflation factor (VIF) quantifies how much the variance is inflated.

2) F(False)

Explanation:

In Regression problems, When you are predicting then assume some assumption. No multicollinearity between predictor variables. Since VIF use for finding the multicollinearity between predictor variables . If one variable has Highest corelation then , it removes from predictors variables and not use for analysis.

3) F(False)

Explanation:

Many times researchers use sequential multiple regression (hierarchical or block-wise) entry methods that do not rely upon statistical results for selecting predictors. Sequential entry allows the researcher greater control of the regression process. Items are entered in a given order based on theory, logic or practicality, and are appropriate when the researcher has an idea as to which predictors may impact the dependent variable.

4)F (False)

Explanation:

stepwise multiple regression, is considered statistical regression methods .Stepwise selection involves analysis at each step to determine the contribution of the predictor variable entered previously in the equation.

5)T( True)

Explanation:

Model validation or Model cross validation use for the validate your accuracy of predicting model  on your training and test data set