We review random coefficient regression (RCR) models and methods for fitting these models from an applications perspective. Methods for data with exponential family distributions are presented with the Gaussian distribution as a special case. Attention is given to interpretation of fixed effects and the correlation structures implied by RCR models. Estimation methods are presented wtih computational approaches. Problems associated with testing fixed effects include accurate variance estimation and robustness to misspecification of the covariance structure. Methods for model selection and assessment are presented. An example is used to demonstrate recommended approaches.