Multiple regression represents an equation wherein a set of predictor variables is used to create a predicted value for a dependent variable. The mathematical elements, often described as ordinary least squares, are such that the goal of the equation is the generation of a model where the sum of the squared deviations are minimized between the observed and predicted values (the sum of the actual deviations should be zero). The process of creating a value that minimizes the sum of the deviations represents the assumptions of the normal curve for any process that involves estimation of a mean or a correlation. This process simply takes the same set of expectations and for a standardized equation operates using the following equation:

$$\begin{array}{l}\mathrm{Predicted}\mathrm{value}\mathrm{of}\mathrm{standardized}Y=\\ {\mathrm{\beta }}_1{X}_1+{\mathrm{\beta }}_2{X}_2+{\mathrm{\beta }}_3{X}_3+\cdots +{\mathrm{\beta }}_i{X}_i.\end{array}$$

This equation indicates that the predicted ...