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The University of Birmingham - School of Computer Science

Evaluation Methods and Statistics

Autumn Semester 2016

Staff Details

Professor Achim Jung
Room 213
Tel: (+44) 121 414 4776
Email
Office hours
Tutor
 
Aditya Acharya
Room 242
Email

Lectures

Mondays, 10:00-11:50am, G26, Mechanical Engineering

Exercises and practicals

Tuesdays, 11:00-11:50am, Small Lecture Theatre S06, Poynting

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 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.

Course material

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

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 and 10.
  • 80% 1.5-hour examination in May

Re-assessment

There is one resit opportunity for this course in August next year. The continuous assessment component will not be carried forward.