Basic Principles of Regression Analysis

The current chapter reviews the basic principles of linear regression models in a nonmathematical fashion. First, simple and multiple linear regressions are explained as methods for making predictions about outcome variables, otherwise called dependent variables, from exposure variables, otherwise called independent variables. Second, specific purposes of regression analyses are addressed: Particular attention has been given to common sense rationing and more intuitive explanations of the pretty complex statistical methodologies, rather than bloodless algebraic proofs of the methods.