ABSTRACT
This chapter introduces the readers to the process of predicting values of a ‘criterion’ variable from values of a ‘predictor’ variable. Regression is a way of using correlation to predict values of one variable from another. In multiple regression, the authors can use regression theory to predict values of a ‘criterion’ variable (or target variable) using a set of variables (predictors) that correlate with it, thereby obtaining a better estimate of how the criterion varies than with just one correlating variable. At school you may have been encouraged to draw the line of best fit through the points on a scatterplot. Analysis in which the value of one ‘criterion’ variable is estimated using its known correlations with several other ‘predictor’ variables.
