Chapter 20 Regression and Multiple Regression Flashcards
b weight
The amount by which a criterion variable will increase for a one-unit increase in a predictor variable; a predictor’s coefficient in the multiple regression equation
Beta value
Standardised b weights (i.e., as expressed in standard deviations)
Collinearity
Extent of correlations between predictor variables
Criterion/target/dependent variable
Variable on which values are being predicted in regression
Heteroscedascity
Degree to which the variance of residuals is not similar across different values of predicted levels of the criterion
Linear regression
Procedure of predicting values on a criterion variable from a predictor or predictors using correlation
Multiple correlation coefficient
Value of the correlation between actual values of the criterion variable used in multiple regression and the predicted values
Multiple regression
Analysis in which the value of one ‘criterion’ variable is estimated using its known correlations with several other ‘predictor’ variables
Partial correlation
Method of finding the correlation of A with B after the common variance of a third correlated variable, C, has been removed (‘partialled out’)
Predictor
Variable used in combination with others to predict values of a criterion variable in multiple regression
Regression coefficient
Amount by which predictor variable values are multiplied in a regression equation in order to estimate criterion variable values
Regression line
Line of best fit on a scatterplot which minimises residuals
Residual
Difference between an actual score and what it would be as predicted by a predictor variable or by a set of predictor variables
Semi-partial correlation
Correlation between a criterion variable B with the residuals of A, after A has been regressed on C. Removes the common variance of A and C from the correlation of A with B.
Standardised regression coefficient
Full name for beta values in multiple regression
