19658 – 778 (120) BCommHon in Statistics
See the Faculty Calendar
here
for a detail description of this program. Modules presented in this program:
NA  This module is not presented in 2023.
* The MMS A module is a prerequisite for this MMS B module.
2*  Stochastic modelling  presented under Capita Selecta (Statistical modelling and Inference)
Module Content  Honours
Introduction to R (13074723)
Objectives and content:
This module is an introduction to programming and data analysis within the R open source environment. It is presented as a block course in the first two weeks of the first semester and commences the week preceding general commencement of classes. The viewpoint of this module as well as of all modules where R plays a role is in agreement with the aim of the R computer language: "R has a simple goal:
To turn ideas into software, quickly and faithfully".
Biostatistics (10408712)
Objectives and content:
Biostatistics may be regarded as the study of the application of statistics to medicine. It covers medical terminology, the design of clinical trials, the collection and numerical analysis of data, the interpretation of the analyses and the drawing of conclusions. Particular emphasis is given to skills relevant to medical literature (the writing, as well as the understanding of writing by others) and statistical techniques and software that are widely used when doing medical research. It is not a mathematically strenuous course. It deals primarily with the philosophy and terminology of medical research, as well as the statistical techniques problems encountered in the medical field in particular. Topics that will be covered are: SAS,
Clinical trials, Power and sample size analysis, Longitudinal data analysis, Handling missing data and Statistical genetics.
Multivariate Methods in Statistics A & B (10600721 & 10601751)
Objectives and content:
The objective of the course is to teach students the practical application of multivariate analysis. Various multivariate methods are dealt with. Students learn when and where to apply these techniques. The consequences of the assumptions made on some of these techniques are also studied. The following topics are studied: Matrix algebra, Characterising and displaying multivariate data, The multivariate normal distribution, Inferences on one or two mean vectors, Multivariate analysis of variance, Inferences on the covariance matrix, Discriminant analysis, Classification analysis, Multivariate regression, Canonical correlation, Principal component analysis, Factor analysis and Cluster analysis. The R and SAS software are used in all the applications to datasets. The MMS A module is a prerequisite for the MMS B module.
Experimental Design (10440713)
Objectives and content:
This module does not require advanced mathematics and is an option for both statistics and mathematical statistics students. Focus is mainly on the practical implementation of techniques together with computer packages from consultancy perspective. Attention is given to modeling, design matrices, least squares and diagnostics.
Data mining (58777741)
Objectives and content: Statistical learning is a relatively new area in statistics. It is concerned with modeling and understanding patterns in complex datasets. With to the explosion of "Big Data", there is currently a high demand for individuals with expertise in statistical learning. The methods studied in this module include regularised regression by means of ridge regression and the lasso; classification using linear discriminant analysis, logistic regression, quadratic discriminant analysis and knearest neighbors; resampling methods such as kfold crossvalidation, leaveoneout crossvalidation and the bootstrap; linear model selection and dimension reduction methods; handling nonlinearity via regression splines, smoothing splines, local regression, generalised additive models, bagging, random forests and boosting; and nonlinear classification and regression by means of support vector machines. The objectives of the module are to equip students with the following knowledge and skills:

the theory underlying the above statistical learning techniques;

application of statistical learning methods in a programming environment;

assessment and comparison of various models;

interpretation and effective (written and verbal) communication of results.
We extensively make use of the R programming language, therefore note that the R course is a prerequisite.
Sampling Techniques (10705742)
Objectives and content:
The design of a sample is one of the most important aspects of any survey: no amount of statistical analysis can compensate for a badlydesigned sample. Therefore, the emphasis of this course is the scientific design of samples, determination of sample sizes and is related to methods for analysing the data from a survey. Contents include: Questionnaire design, sampling techniques (simple random, stratified, systematic, cluster, complex), proportional vs disproportional allocation for stratified sampling, ratio and regression estimation, estimation of means, totals proportions and their variances, weighting of survey data, dealing with nonresponse.
Financial Mathematical Statistics (11164732)
Objectives and Content: In this module an introduction is given to Extreme Value Theory (EVT) and its role in Financial Risk Management. EVT entails the study of extreme events and for this theory has been developed to describe the behaviour in the tails of distributions. The module will disduss the theory in a conceptual fashion without proving the results. It will be shown how this theory can be used to carry out inferences on the relevant parameters of the underlying distribution. Both the classical approach of block maxima based on the FisherTippett Theorem and the more modern threshold approach based on the PickandsBalkemade Haan Theorem will be discussed and applied. Results for both independent and dependent data will be covered.
Applied Time series Analysis (10748722)
Objectives and content:
This module is a continuation of undergraduate time series analyses and concentrates on more advanced forecasting techniques. Topics that are covered include:

The Box & Jenkins methodology of tentative model identification, conditional and unconditional parameter estimation and diagnostic methods for checking the fit of the series.

ARIMA and Seasonal ARIMAprocesses.


Introduction to Fourier Analysis, spectrum of a periodic time series, estimation of the spectrum, periodogram analysis, smoothing of the spectrum.


Case studies using STATISTICA, R and SAS.

Forecasting with ARMA models and prediction intervals for forecasts.


Transfer function models and intervention analysis.


Multiple regression with ARMA errors, cointegration of nonstationary time series.

Conditional heteroscedastic time series models, ARCH and GARCH.
Applied Stochastic simulation (65269746)
Objectives and content:
In this module the student learns to understand and apply the underlying mathematical principles behind stochastic simulation. Statistical theory is applied to practical problems which are modelled on computer. To achieve this, the student revises probability theory, including conditional probability and independence, discrete and continuous distributions, probability integral transformation, and Bayes' theorem. Pseudorandom number generation is studied to simulate stochastic problems. These pseudorandom numbers are used to evaluate integrals, and to generate discrete and continuous random variables. It is also important to do a statistical analysis of simulated data using point and interval estimates of the mean and variance, as well as bootstrap techniques. Input data for simulation models are analysed with the chisquared and KolmogorovSmirnoff goodnessoffit tests. The student finally develops practical simulation models of discreteevent, dynamic stochastic processes using a dedicated simulation software package.
Capita Selecta in Statistics A & B (11920725 & 11921755)
Objectives and content:
Selected and specialised topics to be followed in Applied Statistics. Content varies from year to year when offered.