Linear models are ubiquitous in statistics and can be found all across the language sciences. This chapter introduces the reader to simple linear regression (ordinary least squares regression) using a psycholinguistic dataset as an example (word frequency effects). Basic linear regression concepts are discussed, such as the distinction between intercepts and slopes, the distinction between fitted values and residuals, and the concept of error minimization (“best fitting line”). The chapter also discusses R 2 as a measure of how much variance is described by a linear model.