This page is under continuous revision, with new information added as the term proceeds.

Lectures (Tuesdays 1-3 pm) and workshops (Thursdays 1-3 pm) are held in Beaty Biodiversity 224.

Below is an approximate list of lecture topics and contents. Links to the lecture slides will be added before each lecture. Click the refresh button on your browser to make sure you are seeing the latest version of this page.


Lecture slides
About the course
Course objectives
About the instructor
Why we use R
Organizing data for use in R


Lecture slides
The purpose of graphs
Principles of effective display
Types of graphs to achieve these principles
How some graphs fail, and what can be done
What about tables?

Design of experiments

Lecture slides
Plan your sample size
Experiments vs observational studies
Why do experiments
Clinical trials: experiments on people
Design experiments to minimize bias and effects of sampling error
Analysis follows design
What if you can’t do experiments

Linear models

Lecture slides
What is a linear model
Several examples
Estimating parameters vs testing hypotheses
Model comparison: “full” vs “reduced” models
Sequential vs marginal testing of terms
The lure of model simplification
Perils of correcting for covariates
Assumptions of linear models
Related methods in R

Mixed effects models

Lecture slides
Random vs fixed effects
Two-factor ANOVA example
Why the calculations are different with random effects
Unbalanced designs with random effects
Examples of experiments with random effects
Linear mixed-effects models
Example: Estimating repeatability of a measurement
Assumptions of linear mixed-effects models
An example violating the assumptions, with solutions


Lecture slides
Probability and likelihood
Maximum likelihood estimation
Example: estimate a proportion
Likelihood works backward from probability
Likelihood-based confidence intervals
Example: estimate speciation and extinction rates
Log-likelihood ratio test
Example: test a proportion

Generalized linear models

Lecture slides
What is a generalized linear model
Linear predictors and link functions
Example: estimate a proportion
Analysis of deviance table
Example: fit dose-response data using logistic regression
Example: fit count data using a log-linear model
Advantages and assumptions of glm
Quasi-likelihood modeling when there is excessive variance

Model selection

Lecture slides
Example: polynomial regression
The problem of model selection
Choose among models using an explicit criterion
Goals of model selection
Criteria: Mallow’s Cp and AIC
Search strategies: All subsets; stepAIC
Example: predicting ant species richness
Several models may fit about equally well
The science part: formulate a set of candidate models
Example: adaptive evolution in the fossil record

Bayesian data analysis

Lecture slides
What is probability
Another definition of probability
Bayes Theorem
Prior probability and posterior probability
How Bayesian inference is different from what we usually do
Example: one species or two
Example: estimate a proportion
Credible intervals
Bayes factor
Bayesian model selection

Bootstrap and resampling

Lecture slides
Estimation and hypothesis testing
Permutation test
The sampling distribution
The bootstrap standard error
The bootstrap confidence interval
Comparing two groups


Lecture slides
Meta-analysis compared with traditional review article
Quantitative summaries compared with vote-counting
How to carry out a meta-analysis
Effect size
Fixed and random effects
Publication bias
Make your results accessible to meta-analysis
Consider a mata-analysis for your first thesis chapter
Best practices

Multivariate statistics

Lecture slides
Why do a multivariate analysis
Ordination, classification, model fitting
Principal component analysis
Discriminant analysis, quickly
Species presence/absence data
Distance data

Species as data points

Lecture slides
Example: the problem with species data
Phylogenetic signal in ecological traits
Why phylogeny matters in comparative study
Phylogenetically independent contrasts
A linear model (general least squares) approach
A method for discrete data (and issues)
Use R!

Videos of 2017 lectures

Lecture video: Introduction
Graphics: Lecture video
Design of experiments: Lecture video
Linear Models: Lecture video (only the first half got recorded)
Mixed effects models: Lecture video
Likelihood: Lecture video
Generalized linear models: Lecture video
Model selection: Lecture video
Bayesian data analysis: Lecture video
Bootstrap and resampling: Lecture video
Meta-analysis: Lecture video
Multivariate statistics: Lecture video
Species as data points: Lecture video