## The University of Birmingham - School of Computer Science## Evaluation Methods and Statistics## Autumn Semester 2017## Staff
## LecturesMondays, 9:00-10:50am, LT 1, Sport and Exercise Sciences## Exercises and practicalsTuesdays, 11:00-12:50am, LT 1, Sport and Exercise Sciences## Recommended textbooks**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 explanations 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.*
## Course material## Week 1: Basic concepts of statistics: populations, samples, variables, scales- Handout 1 (boxes)
- R installation guide
- Practical 1 (model answers)
- Additional reading: The R manual: Chapters 1 and 2
- Data sources: Article published in the newspaper
*The Guardian*on 9 April 2017
## Week 2: Sampling, measuring, and recording results- Handout 2 (boxes)
- Additional reading: Chapters 2 and 3 of the book by Pfenning.
- Practical 2 (model answers)
- Additional reading: The R manual: Chapters 6.3 and 7.1
## Week 3: Summarising and visualising data- Handout 3 (boxes)
- Practical 3 (model answers)
- Read chapters 4 and 5 of the book by Pfenning, or chapters 1 and 2 of the book by Howell
- URLs mentioned in the handout:
- US mean household income
- US median household income
- UK Government spending
- Hans Rosling's TED talk on
*The best stats you've ever seen* - Our World in Data (Lot's of interesting data sets and visualisations)
- Article on exploratory data analysis in R on the very useful site R Bloggers
- Examples from 2014 exam results:
- Mathematical Techniques for Computer Science
table plot histogram scatterplot historical data as boxplots - HCI
table plot histogram scatterplot - First year Software Workshop
table plot histogram scatterplot
- Mathematical Techniques for Computer Science
## Week 4: Populations and random variables; the normal distribution## Week 5: Class Test 1## Week 6: Sample mean; Central Limit Theorem; confidence intervals- Handout 5 (boxes)
- Chapter 3 of Howell
- An illustration of the Central Limit Theorem
- Interactive illustration of normal distribution and a tabulation
- Implementation of a quincunx
## Still Week 6: Literate programming in R- Handout 6
- Practical 5 (model answers)
- Template for submitting solutions to the exercise sheet in .Rnw format (Ditto in .pdf)
- The LaTeX project.
- The LaTeX Wiki book.
- LaTeX tutorial pages at Clark University.
- The official knitr homepage
- A nice knitr tutorial
## Week 7: Comparing two means: The t-test.## Week 8: ANOVA- Handout 8 (boxes)
- Chapter 11 of Howell, in particular section 11.3
- Tutorial on ANOVA in R
- Practical 7 (model answers)
## Week 9: Class Test 2## Last year's course material (without linked files):## Week 10: Correlation and regression- Handout No.8
- For correlation and regression: Chapter 9 of Howell
- Practical 8
- twitter.csv
## Week 11: Report writing## Assessment**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 (23 October) and 9 (20 November).**80% 1.5-hour examination in May**
## Re-assessmentThere is one resit opportunity for this course in August next year. The continuous assessment component willnot be carried forward.
## Previous test and exam papers
## Other resources1. tryR - Interactive online tutorials to learn basics of R. 2. https://www.r-bloggers.com/ - R blog. 3. Cookbook for R - Worked examples and with the code explained. 4. Learning R - lot of articles on ggplot2 package for plotting. The R projectA local website created by a former colleague from the School of Physics providing support for using R A nice knitr tutorial The knitr manual Gnu Octave: A mathematical assistant similar to Matlab, but freely available (this is installed on the School's Linux machines) School-level module description |