In this chapter, the authors’ consider models with a single predictor, measured more or less continuously. They begin by considering the definitions of both the intercept and slope in such models, with the former being a particular predicted value and the latter being the unit difference between predicted values. The authors’ provide formulas for estimating these parameters and used these to illustrate alternative ways of thinking about what a slope estimate means. They also consider models in which the predictor is centered or put into mean-deviation form. The authors’ devote to model comparisons, treating the two-parameter, single-predictor model as Model A and comparing it to an alternative Model C, testing inferences about the slope and the intercept. They consider models that contain only predictors presumed to be continuously measured. The confidence interval for the intercept represents the range of values for the intercept that would not be rejected by an inferential statistical test.