1. Quantitative Methods
What lies beyond?

2. General Linear Model What lies beyond? What does GLM do for us?
partitioning of variance and DF
tests for whether x-variables matter
statistical elimination
best-fit equation showing how x-variables matter
What is general about GLM?
categorical or continuous x-variables
main effects and interactions
any number of x-variables and interactions

3. General Linear Model What lies beyond? How is GLM not general?
linearity/additivity
Normality
homogeneity of variance
independence
a single y-variable

4. Generalised Linear Model
What lies beyond?
Generalised Linear Model
The Generalised Linear Model relaxes
linearity/additivity
Normality
homogeneity of variance
independence
a single y-variable

5. Generalised Linear Model
What lies beyond?
Generalised Linear Model
The Generalised Linear Model adds
link function
variance function
choice for estimating or setting the ‘scale factor’

6. Generalised Linear Model
What lies beyond?
Generalised Linear Model
The Generalised Linear Model includes:
Link function Variance Function Name of model
Identity Normal GLM
Logit Binomial Logistic Regression
Log Poisson Log-linear models
Inverse Exponential Survival analyses

7. General Linear Model What lies beyond? How is GLM not general?
linearity/additivity
Normality
homogeneity of variance
independence
a single y-variable

8. Generalised Linear Model
What lies beyond?
Generalised Linear Model
What does Generalised Linear Model do for us?
partitioning of deviance and DF
tests for whether x-variables matter
statistical elimination
best-fit equation showing how x-variables matter
What is general about Generalised Linear Model?
categorical or continuous x-variables
main effects and interactions
any number of x-variables and interactions

9. General Linear Model What lies beyond? How is GLM not general?
linearity/additivity
Normality
homogeneity of variance
independence
a single y-variable

10. General Linear Model What lies beyond? How is GLM not general?
linearity/additivity
Normality
homogeneity of variance
independence
a single y-variable

11. Multivariate methods What lies beyond? Principle components analysis
Factor analysis
Discriminant analysis
MANOVA
Cluster analysis / Numerical taxonomy

15. 10. 2 (principles of marginality), 10
10.2 (principles of marginality), 10.4 (applications of marginality), 11.1 (calculate R2 or R2adj), 5.3 (orthogonality)

17. 9 (assumptions and model criticism)

18. 9 (assumptions and model criticism)

20. 4 (statistical elimination)
10.2 (marginality and types of SS) and 10.4 (examples)

22. 4 (statistical elimination, legs example)

24. Last last words… What lies beyond?
Learn GLMs for the Biology course and finals
Be prepared to learn Generalised Linear Models for more advanced problems
A chance to do an exam question in the practicals