ABSTRACT
The race is not always to the swift, nor the battle to the strong, but that’s the way to bet.
— Damon Runyon (Runyon on Broadway, 1950)
The attempt to predict the values on a criterion (dependent) variable by a function of predictor (independent) variables is typically approached by simple or multiple regression, for one or more than one predictor, respectively. The most common combination rule is a linear function of the independent variables obtained by least squares; that is, the linear combination that minimizes the sum of the squared residuals between the actual values on the dependent variable and those predicted from the linear combination. In the case of simple regression, scatterplots again play a major role in assessing linearity of the relation, the possible effects of outliers on the slope of the least-squares line, and the influence of individual observations in its calculation. Regression slopes, in contrast to the correlation, are neither scale invariant nor symmetric in the dependent and independent variables. One usually interprets the least-squares line as one of expecting, for each unit change in the independent variable, a regression slope change in the dependent variable.1
