community assembly experiment 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
Lab A01: 4-6, Tuesday, 219 Phillips
Lab A02: 1-3, Tuesday, 219 Phillips

Requirements

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.
Crawley's web page with useful links (exercises, examples, code).

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.
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.