Quantitative Methods What lies beyond?.

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Presentation transcript:

Quantitative Methods What lies beyond?

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

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

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

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’

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

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

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

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

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

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

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)

9 (assumptions and model criticism)

9 (assumptions and model criticism)

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

4 (statistical elimination, legs example)

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