IB 517

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Credit: 4 hours.

Students will review and master commonly used statistical techniques including probability distributions, power analyses, t-tests, correlations, regression, ANOVA, generalized linear models, principal components analysis, model selection, and experimental design/interpretation. Weekly discussions focus on statistical issues such as data dredging, the difference between statistical and biological significance, and the difference between correlation and causation. The laboratory involves programming in R to create publication quality graphs, analyze/simulate/interpret data, and trouble-shooting code.

4 graduate hours. No professional credit. Prerequisite: General statistics course or consent of the instructor.

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