Official Description: PLS206 Applied Multivariate Modeling in Agricultural and Environmental Sciences (4) Lecture–3 hours; discussion–1 hour. Prerequisite: one of course 120, Statistics 106, 108, course 205 or equivalent. Multivariate linear and nonlinear models. Model selection and parameter estimation. Analysis of manipulative and observational agroecological experiments. Discriminant, principal component, and path analyses. Logistic and biased regression. Bootstrapping. Exercises based on actual research by UC Davis students. Not open for credit to students who have complete Agronomy 206. (Former course Agronomy 206.)
Course goals: To give graduate students in biology and related sciences an overview of regression analysis and multivariate statistical modeling. The emphasis is on covering a wide variety of techniques and preparing students for further individual study according to their specific needs. Linear models are presented in detail. Students will learn R to complete assignments. My philosophy is to promote discovery and resourcefulness. For example, instead of showing ten different ways to do separation of means, we will: (1) try to understand why separation of means is an issue, (2) learn one reasonable way to do it, (3) make a plan for learning other methods if necessary. Students are expected to independently find and use information beyond what is presented during meetings.
Instructors: Emilio A. Laca
Other info and comments: Syllabus_(2014)
Software Used: R