Principles of Experimental Analysis (512)
The purpose of this course is to provide students with knowledge and experience in using regression and Analysis of Variance (ANOVA) techniques and other common statistical modeling methods for continuous outcome data. By the end of the course, students should feel comfortable understanding and applying commonly-used linear models to data arising from a wide variety of disciplines. This class is introductory in that all techniques are taught using scalars, instead of vectors and matrices and algebra, rather than calculus. The emphasis is more on the concepts and ideas involved in model building, data analysis, and diagnostic techniques, rather than the underlying statistical theory. Students will learn how to cohesively report results of statistical analyses, both orally and written. The class is taught using SAS. Students will learn how to program every method discussed using SAS and be able to interpret all output for SAS procedures for basic linear models.
Topics covered include straight-line regression and correlation; multiple regression models, estimation, and testing; dummy variables; analysis of covariance; regression diagnostics; model building and selection strategies; one-way ANOVA; multiple comparison techniques; two-way ANOVA; randomized blocks. We will learn the appropriate SAS computer syntax for all methods mentioned above.