FEEG6017 -- Statistics for Computational Modelling

This is the course website for Statistics for Computational Modelling 2014/2015. The site provides links to the lecture slides and to the problem sheets for the tutorial sessions and gives some additional information on assessment.

Most of the lecture slides and material has been developed by Jason Noble who taught statistics for the ICSS DTC in previous years. Links to his lecture material can be found on Jason's slides from previous years. The module is scheduled as follows:

  • A two hour lecture slot for Mondays 9-11, 177/2011 LR 1
  • A two hour tutorial slot for Thursdays 9-11, 175/1001

    Lecture Slides

    Links to lecture slides (some slides cover more than one lecture):

    Introduction lecture.
    Getting started with R.
    Describing distributions.
    Sampling and the central limit theorem.
    The normal distribution, estimation, confidence intervals.
    Hypothesis testing, t-tests, p-values, type I and type II errors.
    Relationships between two variables: Covariance, correlation coefficient, and r^2.
    ANOVA.
    Linear regression.
    Logistic regression.
    Chi squared tests.
    Interaction terms.
    AIC and model reduction.
    Multivariate analysis of variance.
    Bayes' theorem and Bayesian inference.
    Principal components analysis.
    Time series analysis, autocorrelation.

    Problem sheets for tutorials

    First tutorial session -- Feb 5.
    Second tutorial session -- Feb 12.
    Third tutorial session -- Feb 19.
    Fourth tutorial session -- Feb 26.
    Fifth tutorial session -- Mar 6. Datasets: filData.txt.
    Tutorial sessions from now on are used for coursework.

    Assessment

    Assessment on the module will be 30% an in-class test, to be done on Monday 27 April. Some practice questions are available here ; the real test will have 15 multiple choice questions and 5 short answer questions. It will be an open-book test and you're welcome to use computers, online references, etc.

    There is also a practical assignment, due on Monday May 11. The details are here; the assignment involves conducting a statistical analysis on a given data set and writing a short report that interprets that analysis.