Question

when the highest partial correlation coefficient is significant at any step in stepwise multiple regression, what does this tell us about R^2 change?

Answer 1

The process of multiple regression is used to predict independent variable on the basis of two or more predictor variables. In this stepwise regression is done where we check the partial correlation coefficient and decide whether the predictor variables enters into the next step regression model and we proceed in such way.

Passion correlation is the correlation between that variable and the residual of the previous model.Also we know the R^{​​​​​​2} change significance level decide whether a predictor variable enters the next step of a state wise regression. Now the higher the value of partial correlation coefficient the greater is the change in R^{​​​​​​2} ,so the predictor variable have larger chances to enter the next step regression which thereby implies that the considered dependent variable significantly decides it's effect on the independent variable to be predicted.