Statistics IB

This is a home page for a course of 16 lectures to second year Cambridge mathematics students over 8 weeks.

Each lecture is as self-contained as possible and has course notes of four A4 pages.

I have also made additional notes about things I said in lectures and questions students have asked.

There is a single examples sheet and a file of exam questions. Students should receive three supervisions on the examples sheet. There are four recommended books. If you enjoy this course then you should consider related courses in Part II and other items of interest.

Course notes

The notes are formatted as two side-by-side pages per A4 sheet. So to view the notes properly you may need to go to the ghostview menus and set the display for A4 paper in landscape mode. Each lecture begins on a new page.

There are individual files for each lecture. This may be more convenient for you if you do not have the ability to run ghostscript from your computer, but are able to print a postscript file.

• statistical tables,
• toc.ps (contents), index.ps (keywords), notation.ps,
• S01.ps, S02.ps, S03.ps, S04.ps, S05.ps, S06.ps, S07.ps, S08.ps, S09.ps, S10.ps, S11.ps, S12.ps, S13.ps, S14.ps, S15.ps, S16.ps.

• notes.pdf For people who can spare the paper, this contains a full size (A4) version of the lecture notes (1414 K).

Corrections: Whenever I find an error in the notes the files above are corrected and a note is added to this list of corrections. These corrections should be applied to the notes handed out in lecture. They have been encorporated to the version of the notes above. Here is a postscript file also giving the corrections: corrections.ps

Where to collect photocopies of the notes: I always try to bring copies of the previous lecture's notes to each lecture. Otherwise, you can find copies of notes by using this map to locate the Statistical Laboratory pigeonholes.

Examples sheet

• sheet 1 (lectures 1-5), sheet 2 (lectures 6-10), sheet 3 (lectures 11-16).

• old example sheet 1, old example sheet 2, old example sheet 3.

Corrections: Whenever I find an error in the notes the files above are corrected and a note is added to this list of corrections.

Overhead Slides and Digressions

1. Anchoring and bias. (lecture 1)
2. A method of conducting a questionaire about a sensitive topic. (lecture 2)
3. How many words did Shakespeare know? (lecture 3)
4. A confidence interval for the remaining life of the human race. (lecture 4)
5. Utility and lotteries. (lecture 5)
6. The Alias paradox. (lecture 6)
7. The Ellsberg paradox. (lecture 7)
8. An estimation game. (lecture 8)
9. A statistical love story. (lecture 9)
10. Benford's distribution for the leading digit. (lecture 10)
11. An analysis of Jane Austin's style. (lecture 11)
12. Latin squares and experimental design. (lecture 12)
13. The Stein estimator. (lecture 13)
14. Factor analysis and the Myers-Briggs test. (Lecture 14)
15. Discriminant analysis, principal components, bootstrap. (Lecture 15)

• digress.ps

• oheads.ps

Additional material

Exam questions

This file has exam questions from 1988-1997 and 1999.

• stripos.pdf (143K)

1. M. H. De Groot, Probability and Statistics, 2nd edition, Addison-Wesley, 1986.
2. J. A. Rice, Mathematical Statistics and Data Analysis, 2nd edition, Duxbury Press, 1994.
3. G. Casella and J. O. Berger, Statistical Inference, Brooks Cole, 1990.
4. D. A. Berry and B. W. Lindgren, Statistics, Theory and Methods, Brooks Cole, 1990 (out of print).

In Part IIA there is Computational Statistics and Statistical Modelling.

In Part IIB there is Statistical Inference.

Other items of interest

• Chance News (a collection of probability and statistics examples taken from newspapers, magazines, tv, etc., with criticism and discussion).
• Rate Your Risk Quiz.

  E.g. What is the risk of your being wiped out (with nearly everybody else) next year by a catastrophic comet, meteor, or asteroid impact? One in 750,000? One in 20,000? One in 15,000,000?

• Pictures of famous statisticians
• Biographies of some famous statisticians: Bayes (Bayesian inference), Gosset (Student's t-distribution), Neyman (Neyman-Pearson test), Pearson (Neyman-Pearson test).

• A personality profiling test.

• Sex difference in use of computer-based teaching resources.
  Some people have suggested that male and female students may differ in their use of computer-based teaching material. A preliminary finding for pre-university students is that females actually read the information and use it, whereas males are often content merely to revel in the process of locating and downloading the data. This issue was explored by a questionnaire survey.ps of students during the sixth lecture of the Optimization IB course in 1995. The results.ps are quite interesting. You may like to read the article in the Times Education Supplement by Angela MacFarlane which discusses these issues.
• My current favorite music CD is still Reactivate 10.

Feedback

Click here to send me email with comments or corrections on my lectures, the notes or the examples sheet.

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