Regression coefficient interpretation with coded factors operates on the principle that scaling independent variables to a standardized range (typically -1 and +1) transforms regression slopes into direct measures of effect magnitude relative to the original factor units. This theoretical framework relies on formal definitions where coefficients represent partial derivatives, quantifying the change in the response variable per unit change in the predictor while holding other predictors constant within an orthogonal design space. It serves as a fundamental subfield of experimental design analysis that decouples main effects and interaction magnitudes from baseline scales to ensure comparability across different factor settings.
Prerequisite chain context: requires Interaction Effects in Statistical Models.