A linear regression model with a linear X linear interaction can be written as: (1)Yi=B0+B1X1i+B2X2i+B3(X1i·X2i)+Ei where Yi is the score of person i (i = 1, … , N) on the dependent variable, B0 is the intercept, the Bj (j = 1, 2, 3) are regression weights for the three predictors, X1i and X2i are scores of person i on predictors X1 and X2, respectively, and, Ei is a stochastic error score. The third predictor in Equation 1 is the product of X1i and X2i and carries the interactive effects of X1i and X2i if the two lower-order effects (i. e., X1i and X2i) are included in the equation (cf.Cohen, 1978).1
