The University of Birmingham - School of Computer Science
Evaluation Methods and Statistics
Autumn Semester 2016
Mondays, 10:00-11:50am, G26, Mechanical Engineering
Exercises and practicals
Tuesdays, 11:00-11:50am, Small Lecture Theatre S06, Poynting
- Elementary Statistics: Looking at the Big Picture by Nancy Pfenning. Cengage Learning, 2011
An excellent introduction to the subject by a very experienced lecturer. Many examples from a wide range of subjects.
- Statistical Methods for Psychology by David C. Howell. Cengage Learning, 2013
A very good systematic introduction to the field, covering the everything of our module except programming in R.
If you want to buy a copy for yourself then you will notice that it is quite expensive. However, it is not necessary to get the latest edition and older editions are widely available on the web.
- Probability and Statistics with R by Maria Dolores Ugarte, Ana F. Militino, and Alan T. Arnholt. CRC Press, 2015
Focuses on the mathematical side of the subject and has a detailed coverage of general probability.
- Discovering Statistics Using R by Andy Field, Jeremy Miles and Zoe Field. Sage, 2012
- Discovering Statistics using SPSS by Andy Field. Wiley , 2009
Similar in scope to the one by Howell but the author has a different way of explaining things and this can be helpful to you. Whether you look at the R or the SPSS version doesn't matter; I recommend it because of its expanations and examples.
- The Visual Display of Quantitative Information by Edward Tufte. Graphics Press USA, 2nd edition, 2001. Homepage
A classic. I recommend it for additional reading as it covers data presentation in much much greater detail than we can in the course.
- R for Data Science by Garrett Grolemund and Hadley Wickham. Published online 2016. Print version available from O'Reilly.
The focus is on data exploration and visualisation with a very strong link to R which the authors know inside out.
Week 1: Basic concepts of statistics: populations, samples, variables, scales
Week 2: Summarising and visualising data
Week 3: The normal distribution; the central limit theorem
Week 4: Confidence intervals
Week 5: Class Test 1
Week 6: Sampling
Week 7: Comparing two means: The t-test.
Week 8: ANOVA
Week 9: Class Test 2
Week 10: Correlation and regression
Week 11: Report writing
- 20% continuous through class tests
We are planning to have two class tests during the term. The exact date will depend on the progress we are making with the material but we are currently envisaging these to take place in weeks 5 and 10.
- 80% 1.5-hour examination in May
There is one resit opportunity for this course in August next year.
The continuous assessment component will not be carried forward.
Previous test and exam papers