Describe the relationship between a dependent variable and a set of independent variables. Apply flexible design and contrast options to estimate means and variances and test and predict means. Mix and match categorical and continuous predictors to build models. Use linear mixed models for greater accuracy when predicting nonlinear outcomes. Formulate dozens of models, including split-plot design, multilevel models with fixed-effects covariance and randomized complete blocks design.

Provide a unifying framework that includes classical linear models with normally distributed dependent variables, logistic and probit models for binary data and loglinear models for count data, as well as various nonstandard regression-type models. Apply many general statistical models, including ordinal regression, Tweedie regression, Poisson regression, Gamma regression and negative binomial regression.

Model means, variances and covariances in data that display correlation and non-constant variability. Formulate dozens of models, including split-plot design, multilevel models with fixed-effects covariance and randomized complete blocks design. Select from 11 non-spatial covariance types. Improve accuracy with repeated measures data, including situations in which there are different numbers of repeated measurements, different intervals for different cases or both.