This technical presentation was given by Elizabeth Claassen, JMP Statistical Developer and co-author of JMP for Mixed Models for JMP. Data from the book was used in the presentation and is available for you to download and use. The video is over 90 mins. long with questions interspersed throughout.
Mixed models are one of the most powerful ways to handle correlated observations in designed experiments. They often go by other names, including blocking models, variance component models, nested and split-plot designs, hierarchical linear models, multilevel models, empirical Bayes, repeated measures, covariance structure models and random coefficient models.
Reaching well beyond standard linear models, mixed models enable you to make accurate and precise inferences about your data and to gain deeper understanding of sources of signal and noise in the system under study. Well-formed fixed and random effects generalize well and help you make the best data-driven decisions.
