Linear regression is a basic type of predictive analytics. The main purpose of regression is to examine: a set of variables does a good job of predicting an outcome variable, and picks particular variables that will be significant in the outcome variable, and is indicated by the magnitude and significance of the beta estimates, affect the outcome variable. These regression estimates are used to explain the relationship between a dependent variable and one or more independent variables. The simplest form of the regression equation with a dependent variable and an independent variable is defined by the formula y = c + b*x, where y = estimated dependent variable score, c = constant, b = regression coefficient and x = independent variable score.
