Linear Models

Catalog description

This course includes a matrix-based treatment of both general and generalized linear models. General linear models containing continuous and/or categorical variables are explored in terms of model fitting, assessment, selection, and inference. Remediation techniques such as transformations are also covered. The framework for generalized linear models is introduced in enough detail to support both non-normal distributions via link functions and random effects. A review, and augmentation, of key concepts from MA 305 /405 is included. Topics may include eigensystems, matrix decomposition, generalized inverses, and quadratic forms. Statistical software will be used extensively to carry out matrix operations, analyze data, and support the preparation of professional results.