1. Data Handling & Analysis BD7054 2012-2013 Andrew Jackson Zoology, School of Natural Sciences a.jackson@tcd.ie

2. Statistics in science Analysis of data is central to science – Metaphorically – Literally (Introduction -> methods -> results -> discussion) Underpins one’s own research – Your own research project Essential in understanding others’ research – To question what they did – To incorporate their ideas in your own research

3. The scientific method Ask a question about the world around you – Why are vultures the only obligate scavengers among the extant terrestrial vertebrates?

4. The scientific method Decide what measurable outcome you will use to test a specific hypothesis – Physiology of vultures favours this mode of life – Compare metabolic costs of flight across vertebrate taxa Design an experiment or field study to test this idea Use statistics to determine whether your predictions hold Frame your findings within the broader background of the precedent science –introduction and discussion

5. Course Outline 8 th Oct – 12 th Oct – Introduction to R and statistics 21 st Jan – 25 th Jan – General Linear Models 29 th April – 3 rd May – Generalised Linear Models – Multivariate methods

6. Assessment On the Friday ending each week, you will be asked to submit either – an assessment, or – complete an online exam assessing your proficiency in data analysis using R

7. Learning outcomes NB slightly different from course handbooks summarise and communicate quantitative results graphically and textually to scientific standards. apply appropriate statistical analyses of commonly encountered data types. discuss the context of the analyses within a hypothesis driven framework of scientific logic. use the R statistical computing language for data analysis.

8. Course structure Series of Lecture / tutorials and computer practicals Lectures will be as interactive as possible Computer practicals – Use R to analyse data – Follow video podcasts for instruction – Demonstrators present to help

9. Week 1 Lectures / Tutorials – Monday to Thursday 10-12 – GGSR-A Computer sessions – Monday to Thursday 14-16 – Botany Hut computer rooms

10. Summary of statistics covered Linear regression General linear models – As a way to ask increasingly complex questions of our data using a common framework (ANCOVA / multiple regression) Generalised linear models – Extending these concepts to deal with non-normal data types (binary / surivival / count data)

11. Statistical software - R R is a command line interfaced software – Scary the first few times – Incredibly powerful and adaptable – Free – Open development Time-tabled computer sessions – Complete video-podcast and examples in your own time When Googling for R related topics add “cran” to your search terms

12. Delivery of course content Attendance at lectures/tutorials is mandatory Moodle website associated with course – Lectures will be posted – Web-based discussions – Links to video-podcasts Statistics, An introduction using R. Michael J Crawley. Wiley. ISBN 0-470-02298-1

13. Basic Experimental Design For more details see Experimental Design for the Life Sciences by Ruxton and Colegrave

14. Relationship between hormone levels in male chimpanzees and #females Measure hormone levels of male chimps and then count how many females are they foraging with. Higher hormone levels are expected when there are more females to mate with. However, hormone levels are influenced by age, diet, time of day etc.

15. Male hormones and #females Hormone level difference could be due to age, diet, time of day OR #females

16. Relationship between hormone levels in male chimpanzees and #females All chimps are the same age, diet, and time of day so hormone level difference ~ #females

17. Class Exercise Come up with a scientific question and plot your predictions

18. Computer Session 8 th October Work through 3 podcasts on my website – http://www.tcd.ie/Zoology/research/research/the oretical/Rpodcasts.php http://www.tcd.ie/Zoology/research/research/the oretical/Rpodcasts.php 1.Opening R for the first time 2.Working with script files 3.Importing data into R