Correlation and Regression Flashcards
(9 cards)
In regression, what are predictors?
Variables we believe to be the cause. There can be both primary, and secondary predictors.
In regression, what is criterion?
Variables we believe to be the outcome - Y
What is bivariate regression?
Bivariate regression is the prediction of scores on a single criterion variable (Y) based on scores on a single predictor variable (X).
What is multiple regression?
Multiple regression is the prediction of scores on a single criterion variable (Y) based on scores on mulitple predictor variables (X1, X2…) simultaneously.
What is zero-order correlation (r )?
The unadjusted correlation coefficient between any given predictor and Y, ignoring other predictors in the model. If we were to run a bivariate regression it would be beta. This is squared to get variance in Y that can be explained by the given predictor.
What is the limitation of zero-order correlation?
It does not account for the intercorrelations between all of the predictors in the model and their shared contributions in predicting Y.
What is partial correlation (pr)?
The correlation between any given predictor and the criterion after the variance accounted for by the other predictor/s in the model has been partialled out of both X and Y. By squaring it, you get the proportion of residual variance in Y that can be explained by the predictor.
What is residual variance?
The left over variance in Y that cannot be explained by other predictors.
What is semi-partial correlation (sr)?
The correlation between any given predictor and Y after the variance accounted for by the other predictor/s in the model has been partialled out of the predictor. By squaring it, you get the proportion of total variance in Y that can be uniquely explained by the criterion.
