Statistical analysis with R

Purpose:  To develop competence and confidence in data analysis, encompassing the majority of statistical methods that most environmental scientists need to use in their day-to-day research, as well as a conceptual framework for learning more specialised methodologies for particular research fields.

Composition:  5-day course for a maximum of 25 people including the following taught sessions, interspersed with informal workshop sessions in which students can apply the methods to their own datasets and/or class example datasets.

Session topics:

 1. Using (and not abusing) Generalised Linear Models (GLM),

2. Analysing count data data with GLM,

3. Analysing binomial, proportional and ordinal data with GLM,

4. Generalised Additive Models (GAM),

5. Mixed models (GLMM) and alternatives to GLMM,

6. Multi-model inference and model averaging,

7. Bootstrapping,

8. Time-series analysis,

9. Survival analysis,

10. Spatial analysis,

11. Multivariate methods,

12. Where to go from here; onwards and upwards with R.

These sessions will include relevant material for researchers using both R and other analytical software such as Arc-GIS, Q-GIS and Matlab.