This page is under continuous revision, with new information added as the term proceeds. Click the refresh button on your browser to make sure you are seeing the latest version of this page.
Below is an approximate list of lecture topics and contents. Links to the lecture slides will be added before each lecture.
Lecture overheads and videos from previous years are below.
About the course
Course objectives
About the instructor
Why we use R
Organizing data for use in R
Review of basic
concepts in statistics
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?
Interactive and moving
graphs
Plan your sample size
Experiments vs observational studies
Why do experiments
Clinical trials: experiments on people
Design
to minimize bias and effects of sampling error
Analysis Follows
Design
What if you can’t do experiments
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
Other related methods in R
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
Other designs with random effects, briefly
Assumptions of linear mixed-effects models
A solution when Time is a
factor
Probability and likelihood
Maximum likelihood estimation
Example: estimate a proportion
Likelihood works backward from
probability
Likelihood-based confidence intervals
Example:
estimate survival rates in Game of Thrones
Log-likelihood ratio
test
Example: test a proportion
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
Example: model contingency tables
Example: polynomial regression
The problem of model selection
Choose among models using an explicit criterion
Goals of model
selection
Search strategies: dredge(), stepAIC()
Criterion:
AIC
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
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
Estimation and hypothesis testing
Permutation test
Estimation
The sampling distribution
The bootstrap standard
error
The bootstrap confidence interval
Comparing two groups
Meta-analysis as part of a systematic review
Meta-analysis
compared with traditional review article
How to carry out a
meta-analysis
Effect size
Fixed and mixed effects
meta-analysis
Associating effect sizes with relevant variables
Publication bias
Make your own results accessible to
meta-analysis
Consider a meta-analysis for your first thesis
chapter
Current best practices
Why do a multivariate analysis
Ordination, classification, model
fitting
Principal component analysis
Species presence/absence
data
Discriminant analysis
Machine learning, quickly
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
Discrete data Phylogenetic methods have many
applications R: An embarrassment of riches
Use R!
© 2009-2026 Dolph Schluter