Principles of statistical model building and analysis applied in linear and generalized linear models and illustrated through multivariate methods such as repeated measures, principal components, and supervised and unsupervised classification. Each student will give a written report and seminar presentation highlighting statistical methods used in a research project. Prerequisites: STAT 501, 502 or equivalent. Note: Cannot be used for credit towards a thesis-based graduate program in Statistics.
Principles of statistical model building and analysis applied in linear and generalized linear models and illustrated through multivariate methods such as repeated measures, principal components, and supervised and unsupervised classification. Each student will give a written report and seminar presentation highlighting statistical methods used in a research project. Prerequisites: STAT 501, 502 or equivalent. Note: Cannot be used for credit towards a thesis-based graduate program in Statistics.
