Linear Models
Linear models are a foundational class of statistical models used to describe the relationship between a dependent (or response) variable and one or more independent (or explanatory) variables. The core assumption is that this relationship can be approximated by a straight line, meaning the dependent variable is represented as a linear combination of the predictor variables plus an error term. By fitting a model to observed data, statisticians can estimate the magnitude and direction of each predictor's effect, test hypotheses about these relationships, and make predictions for new outcomes, making it a cornerstone of both inferential and predictive statistics.
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2. Simple Linear Regression