ABSTRACT
Some of the advantages of using a model to understand the relationship between predictor variables (Xs) and an outcome variable (Y) are that a model allows us to gain insight about:
Function: The structure of the association between the variables (e.g., linear or some r other function). Parameters: How a change in a predictor variable, r X, is expected to affect an outcome variable, Y. Partial parameters: How a change in one of the predictor variables affects the out-r come variable while controlling for the effects of other predictor variables included in the model. Smooth prediction: What the expected (or predicted) value of the outcome variable r might be for any given values of the predictor variables.
