| Description: |
The goals of this course are 2-fold. The first goal is to introduce students to commonly used statistical techniques including power analyses, t-tests, correlations, regression, anova, non-parametric tests, generalized linear models, principal components analysis, model selection, and experimental design/interpretation. At the end of the course, students should be able to use these techniques in their own research and should have a sufficient knowledge base that they can teach themselves related statistical techniques. The second goal is to get students comfortable programming in R. R has a ‘steep learning curve’. You cannot learn it in a single day. Mastering R requires repeated practice with problem sets. At the end of the course, students should be able to create publications quality graphs in R, analyze and simulate data, and trouble shoot their own code. Prerequisite: a general course in statistics or consent of instructor. |
