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BIOLOGY 383 Statistical Analysis of Biological Data As David Salsburg wrote in "The Lady Tasting Tea: how statistics revolutionized science in the twentieth century", biology has changed into a quantitative science that relies on statistical methods for setting confidence levels on the conclusions we make and for assessing how well our data fit our conceptual models. In this course, we will focus on statistical literacy -- not mathematics, and by doing so, we will discuss and explore how biologists "do" science: how we ask questions, design experiments, analyze data, and draw conclusions. We will use a modern statistical program that is free and widely used world-wide, rather than some unique, expensive program that you may never have access to again. Because our goal is to take a tour of statistics and start practicing statistical thinking, this course is surprisingly fun. |
Dr. Evan Weiher
353 Phillips
weiher@uwec.edu
office hours: tuesday 3-4 p.m., wednesday 2-3 p.m., thursday 12-1 p.m.
Lecture/Recitation: 11-noon, Tuesday & Thursday, 107 SSS
Textbooks:
Crawley, M. 2005. Statistics: an Introduction Using R. John Wiley and Sons. isbn 0-470-02297-3. This is an introductory text focusing on the use of R -- a free, open-source statistical program.
Quinn, G.P. and M.J. Keough. 2002. Experimental Design and Data Analysis for Biologists. Cambridge University Press. isbn: 0-521-00976-6. This is a comprehensive text and reference book focusing on biological examples.
Note: The following schedule is tentative.
Lab A01: 4-6, Tuesday, 219 Phillips
Lab A02: 1-3, Tuesday, 219 Phillips
Crawley's web page with useful links (exercises, examples, code).
Quizes and exams will focus on choosing statistical methods, justifying statistical choices, critical thinking and interpretation of output.
All of these are comprehensive / cumulative
Homework assignments will focus on gaining experience in running statistical tests and in interpreting and comparing results.
week 1: overview, what are statistics anyway?; estimation, means, variances, hypothesis testing, t-tests
week 2: introduction to anova, post-hoc tests
week 3: anova interactions, experimental design
week 4: anova nested, random, repeated
week 5: avova practice; quiz 1 (thursday)
week 6: covariance, correlation, linear regression
week 7: transformations, polynomial regression
week 8: ancova, general linear modeling
spring break
week 9: glm practice; quiz 2 (thursday)
week 10: multiple regression, model selection
week 11: introduction to multivariate statistics, FA, PCA
week 12: combining FA & regression
week 13: partial correlation, introduction to SEM
week 14: goodness of fit
week 15: review; exam 3 (in lab)
final exam - Thursday May 14, 10 a.m.