1. Experimental Design and Modelling Harry R. Erwin, PhD School of Computing and Technology University of Sunderland

2. Resources Rowntree, D. (1981) Statistics Without Tears. Harmondsworth: Penguin. Hinton, P.R. (1995) Statistics Explained. London: Routledge. Hatch, E.M. and Farhady, H. (1982) Research Design And Statistics For Applied Linguistics. Rowley Mass.: Newbury House. Crawley, MJ (2005) Statistics: An Introduction Using R. Wiley. Gonick, L., and Woollcott Smith (1993) A Cartoon Guide to Statistics. HarperResource (for fun).

3. Module Outline Introduction Using R Data analysis (the gathering, display, and summary of data) Probability (the laws of chance) Statistical inference (the drawing of conclusions from specific data knowing probability) Experimental design and modeling (putting it all together)

4. Lecture Outline Experimental Design –The process of defining how to collect data that will allow you to falsify a hypothesis. Modelling –The process of defining how the data you’ve collected might be best explained. Involves regression, ANOVA, and ANCOVA

5. Experimental Design

6. Modelling This is the process of defining a minimal model for the data.

7. Regression Analysis Handles continuous data.

8. ANOVA Handles categorical data Why does it involve variance?

9. ANCOVA A mixture of regression and ANOVA.

10. Summary There’s a lot more to statistics. Crawley is a good reference if you’re planning to do a statistical analysis. If your analysis has any complexity, consult a working statistician. I am an experimental scientist, not a working statistician, but I do run a weekly statistical surgery. This semester, it meets in DGIC 109 from 2-3 pm on Wednesdays. Good Luck!