Statisticians often use linear models for data analysis and for developing new statistical methods. Success in either endeavor requires a solid understanding of the underlying theory. Historically, univariate, multivariate, and mixed linear models have been discussed separately. In contrast, we give a unified treatment in order to make clear the distinctions among the three classes of models. No single model class proves uniformly best. Therefore choosing the best approach requires a detailed knowledge of the differences and similarities.