Exam 18: Regression Analysis

Suppose we have a large number of possible independent variables that we want to explore, and we want to find a smaller set of these variables to use in predicting the dependent variable, eliminating other variables that add only an insignificant or trivial amount of explained variation in the dependent variable beyond the smaller set.Which method should we use regarding the order in which predictor variables are entered into our multiple regression analysis?

We can find the multiple correlation between a set of independent variables and a dependent variable by adding up the bivariate correlations that each has with the dependent variable.

Which of the following statements is most correct about the multiple regression equation?

The standardized betas in a multiple regression equation:

The regression line is drawn where the sum of squared distances (or deviations) of the actual data points from the line will be minimized.

If three interrelated independent variables each has a .20 bivariate correlation with a dependent variable, then the multiple correlation is:

The regression equation:

Suppose that independent variable A explains 24 percent of the variation in a dependent variable.Suppose that independent variable B has a bivariate correlation of .40 with that same dependent variable, but that after controlling for variable A, it explains only 1 percent of the variation in the dependent variable.Then:

Suppose we have hypothesized that even after controlling for variables A and B, variable C will have a significant and strong relationship with a dependent variable.Which method should we use regarding the order in which predictor variables are entered into our multiple regression analysis?

The partial correlation coefficient shows the portion of correlation between two variables that is not shared with other variables.

The multiple regression equation extends the bivariate regression formula by adding more independent variables after bX, and each additional variable gets multiplied by its own slope.

The regression equation predicts a variable Y value in light of: a known value on variable X, the point where the regression line intersects the y-axis, and the slope of the regression line.

Multiple correlation refers to the degree of correlation between a group of independent variables and a dependent variable.

The greater the multicollinearity among the independent variables, the less distortion in the multiple regression findings.

Multivariate analysis looks simultaneously at the interrelationships of three or more variables.

We can square the partial correlation coefficient to find the proportion of dependent variable variation uniquely attributable to the independent variable, when the effects of other variables are partialed out.

When we have a large number of possible independent variables that we want to explore, and we want to find a smaller set of these variables to use in predicting the dependent variable, eliminating other variables that add only an insignificant or trivial amount of explained variation in the dependent variable beyond the smaller set, we should use the hierarchical multiple regression method.

Regression analysis is most useful when the correlation between X and Y is very weak.

Which of the following statements is most correct about the multiple regression equation?

The greater the standardized beta weight, the greater influence a variable has in explaining the variation in the dependent variable when other variables are controlled.