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.

Lecture overheads

About the course

Course objectives

About the instructor

Why
we use R

Organizing data for use in R

Lecture overheads

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?

Lecture overheads

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 canâ€™t do experiments

Lecture overheads

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

Lecture
overheads

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

An example violating an
assumption, with a solution

Lecture overheads

Probability and likelihood

Maximum likelihood estimation

Example: estimate a proportion

Likelihood works backward from
probability

Likelihood-based confidence intervals

Example:
estimate survival rates

Log-likelihood ratio test

Example: test
a proportion

Lecture overheads

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

Lecture overheads

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

Lecture overheads

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

Lecture overheads

Estimation and hypothesis testing

Permutation test

Estimation

The sampling distribution

The bootstrap standard
error

The bootstrap confidence interval

Comparing two groups

Lecture overheads

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
meta-analysis

Correcting for publication bias

Make your own
results accessible to meta-analysis

Consider a meta-analysis for
your first thesis chapter

Current best practices

Lecture overheads

Why do a multivariate analysis

Ordination, classification, model
fitting

Principal component analysis

Discriminant analysis,
quickly

Species presence/absence data

Distance data

Lecture overheads

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!

Introduction

Graphics

Design of experiments

Linear models

Mixed
effects models

Likelihood

Generalized linear models

Model selection

Bayesian data analysis

Bootstrap and resampling

Meta-analysis

Multivariate
statistics

Species
as data points

Introduction

Graphics

Design of experiments

Linear
models

Mixed
effects models (2024 lecture)

Likelihood

Generalized linear models

Model
selection

Bayesian
data analysis

Bootstrap and
resampling

Meta-analysis

Multivariate
statistics

Species as data
points

The course in 2023 was taught by Beth Volpov

Introduction

Graphics

Design of
experiments

Linear models

Mixed effects
models

Likelihood

Generalized linear models

Model selection

Bayesian data analysis

Bootstrap and
resampling

Meta-analysis

Multivariate
statistics

Studentsâ€™ choice

© 2009-2024 Dolph Schluter