BEDA weekly schedule
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| Week | Lectures | Practical | Notes |
|---|---|---|---|
| 1 | Introduction and fundamentals Welcome to BEDA! | Open |
|
| 2 | Sampling and the linear model Randomisation and why we teach the linear model first | Open |
|
| 3 | Choosing the right model(s) The easy way to validate model assumptions. | Practical session |
|
| 4 | Multiple predictors and transformations Expanding models and transformations to increase model fit. | Practical session |
|
| 5 | Controls and revision How to control your experiments with data that you collect. | Practical session |
|
| 6 | Refresher and categorical variables A refresher on designing experiments with categorical variables. | No practical | — |
| 7 | Relationships and binary responses How to design experiments with continuous or binary responses. | Practical session | — |
| 8 | Model selection and revision Which model should we use? Some ideas. | Practical session |
|
| Break | Mid-semester break — 28 September–2 October 2026 | ||
| 9 | Multivariate analysis and PCA Introducing multivariate analysis, principal component analysis and factor analysis. | Practical session |
|
| 10 | Clustering and multidimensional scaling Using clustering and nMDS to identify and test multivariate patterns. | Practical session |
|
| 11 | MANOVA and multivariate revision The MANOVA way of designing and interpreting experiments. | Practical session |
|
| 12 | Rarefaction and Bayesian analysis The lectures cover rarefaction for diversity comparisons and Bayesian statistical inference. | Practical session | — |
| 13 | Exam revision You will review the course's experimental-design and modelling concepts for the final exam. | Practical session |
|
Weekly schedule
Lectures Welcome to BEDA!
Practical Open
- Resource: Am I ready for BEDA?
- Practice quiz (0%): Quiz 1
- Notice: Snail-collecting competition: prizes to be won
Lectures Randomisation and why we teach the linear model first
Practical Open
- Resource: Common statistical tests are linear models
- Practice quiz (0%): Quiz 2
Choosing the right model(s)
Lectures The easy way to validate model assumptions.
Practical
- Practice quiz (0%): Quiz 3
- Notice: Early Feedback Task opens Friday 21 August at 10:00
Multiple predictors and transformations
Lectures Expanding models and transformations to increase model fit.
Practical
- Assessment (5%): Early Feedback Task due Friday 28 August at 23:59
Controls and revision
Lectures How to control your experiments with data that you collect.
Practical
- Assessment (10%): Evaluation Quiz due Friday 4 September at 23:59
- Notice: Begin working on Report 1
Refresher and categorical variables
Lectures A refresher on designing experiments with categorical variables.
Practical No practical
Notes —
Relationships and binary responses
Lectures How to design experiments with continuous or binary responses.
Practical
Notes —
Model selection and revision
Lectures Which model should we use? Some ideas.
Practical
- Assessment (25%): Report 1 due Friday 25 September at 23:59
Mid-semester break — 28 September–2 October 2026
Multivariate analysis and PCA
Lectures Introducing multivariate analysis, principal component analysis and factor analysis.
Practical
- Notice: Labour Day — Monday 5 October
Clustering and multidimensional scaling
Lectures Using clustering and nMDS to identify and test multivariate patterns.
Practical
- Notice: Present your experimental design for feedback
- Notice: Prepare for Report 2
MANOVA and multivariate revision
Lectures The MANOVA way of designing and interpreting experiments.
Practical
- Assessment (5%): Report 2 group dataset due at 10:00 on your practical day
Rarefaction and Bayesian analysis
Lectures The lectures cover rarefaction for diversity comparisons and Bayesian statistical inference.
Practical
Notes —
Exam revision
Lectures You will review the course’s experimental-design and modelling concepts for the final exam.
Practical
- Assessment (15%): Report 2 individual report due Friday 6 November at 23:59