The article discusses using random effects to model heterogeneity in data, instead of fixed parameters. This allows for analysis of longitudinal and panel data using mixed linear models. These models are not as commonly used as regression but are important for analyzing data over time. The researchers introduce the error-components model and the linear mixed-effects model to estimate regression coefficients and variance components, as well as test hypotheses about regression coefficients.