Using Software in Teaching Statistics

1. Using Software in Teaching Statistics Damon Berridge, Centre for Applied Statistics, Dept of Mathematics & Statistics d.berridge@lancaster.ac.uk ESRC NCRM Meeting: Training the Trainers, London, 4 June 2007

2. Acknowledgement • This presentation is based on a set of Powerpoint slides ‘Using Open Source Software to Teach Mathematical Statistics’ by Douglas M. Bates, University of Wisconsin-Madison http://www.stat.wisc.edu/~bates/JSM2001.pdf

3. Outline • Discussion of general issues • Introduction to R • Obtaining & installing R • Examples • Useful resources • Concluding remarks

4. Discussion of general issues • It is common to use some statistical software in teaching statistics to social scientists. • Software in statistics courses for social scientists must have a simple interface for it to be useful. • Is it important that use of computing system be integrated with lectures and text?

5. Linking the computing to the text • Two ways of achieving this: • Write a text that is tied to specific software and illustrates the use of that software, e.g. CAS course on GLMs. • Adopt a conventional text but provide examples from text in computing system, e.g. CAS course on Duration Analysis. • Alternatively: • Write self-standing material that is not tied to specific software, e.g. LEMMA.

6. Advantages of Open Source software • No licence fees or management of licences. • Software can be installed on all departmental PCs. • Staff and students can install software on their own laptops without charge (and without violating licences). • Open Source projects encourage contributions from users so extensions are easier.

7. Introduction to R • A number of CAS methodology courses use Open Source software such as R. • R is an Open Source project based largely on interactive programming language S. • Initially developed by Ihaka & Gentleman at University of Auckland in 1996.

8. Introduction to R – cont’d. • R is now developed and maintained by a widely-dispersed, international group of volunteers from academia and industry. • R operates through web sites, archives, e-mail lists, etc. (see Useful Resources).

9. What is R? • An Open Source implementation of programming language S. • A language and environment for data analysis and graphics. • A means of technology transfer through packages, e.g. SABRE in R.

10. What is R? – cont’d. • A flexible data exchange mechanism accessing: • text files and saved R workspaces • S-PLUS data objects, SAS XPORT datasets, SPSS saved datasets, Minitab worksheets, etc.

11. How do I get R? • Informational web site: http://www.r-project.org/ • CRAN – the Comprehensive R Archive Network • Primary CRAN site: http://cran.r-project.org/ • Mirror sites, e.g. http://cran.uk.r-project.org/ • New releases occur frequently – be prepared to re-install!

12. Installing R in Windows • Simple procedure: download and run installer R-2.5.0-win32.exe • CRAN sites are available for installing R on other platforms, e.g. Macintosh, Linux, Unix, etc.

13. Example 1: Web-based resources Registration required; example sessions available

14. Example 2: Support material • Course notes available for downloading from: http://www.cas.lancs.ac.uk/short_courses/coursematerials.html • Audio, slides and demos available at: http://www.cas.lancs.ac.uk/e-learning/index.php are based on the notes.

15. Useful resources • Web site http://www.r-project.org/ and CRAN. • FAQ list at http://cran.r-project.org/doc/FAQ/ is a good source of information. • Manuals in documentation directory http://cran.r-project.org/doc/manuals See especially R-intro.pdf and R-data.pdf. • Paper ‘Using the R statistical computing environment to teach social statistics courses’ by Fox & Andersen, Dept of Sociology, McMaster University http://socserv.mcmaster.ca/jfox/Teaching-with-R.pdf