The Honours Degree in Mathematical Statistics
Students are required to select five papers or modules for the Honours degree (see the list below). Assessment of these modules is based on class work and an examination. Examinations for some of the modules are written off in June and others in November. There is a subminimum of 40% for each module. These modules count 90% of the final honours mark. Students are expected to present a research project which contributes 10% towards the final honours mark. A research seminar session is held weekly throughout the year.
Joint Honours Degrees
At the discretion of the Head of Department, a student may include modules from Pure Mathematics, Applied Mathematics, Computer Science, Economics or any other approved subject. In order for a student to register for a joint honours degree, at least 40% of the final mark must be obtained from one or other of the departments in which the modules are taken.
The Department of Statistics offers the following postgraduate papers or modules (not all each year):
- Bayesian Statistics: A study of the methods of decision making where prior beliefs and relevant data enable one to arrive at posterior beliefs.
- Econometrics: A study of statistical methods of obtaining estimates of parameters of economic theory. Econometric methods take into account random-like disturbances which create deviations from the exact behavioural patterns suggested by economic theory and mathematical economics.
- Linear Models: A study of the general procedures of estimation and hypothesis testing for linear statistical models with specific applications for unbalanced data that often arise in research and survey work.
- Multivariate Analysis: A study of statistical models suitable for describing and analyzing multivariable data.
- Neural Networks: A study of information processing methods that can be used to extract patterns and detect trends that are too complex to be noticed by either humans, or other computer techniques.
- Probability Theory: A study of the mathematical entities which are used in constructing statistical theories.
- Queueing Theory and Simulation: A study of models in which customers arrive in some random manner at a service facility.
- Pattern Recognition: A study of automatic classification techniques based on Bayesian decision theory. Applications comprise e.g. analysis of radar images, character recognition or protein structure prediction.
- Stochastic Calculus in Finance: A study of the mathematics of financial derivatives.
- Stochastic Processes:A study of mathematical models of systems that change in ‘time’ according to probabilistic laws. Examples of such systems are birth and death processes, storage and inventory systems, renewal and point processes.
- Survey Methods and Sampling Techniques: The course is a study of the theory of sample survey procedures and their application in practice.
- Time Series Analysis: A study of statistical methods of describing and analyzing the variation in quantities that change with time. Examples for analysis by these methods are stock market prices, economic indices, output or yield in chemical, industrial and manufacturing processes, demands for goods and services, etc.
Information with regards to student funding and financial aid can be found here. Prospective postgraduate students will find further information, application forms and the contact details of the Rhodes Univeristy Postgraduate Financial Aid Administrator, Mr John Gillam, on the postgradiate funding opportunities web page.
It is hoped that all postgraduate students will be appointed as tutors in the Department of Statistics. These students are expected to tutor undergraduate students in the Department for a certain number of hours per week.
It is hoped that all masters students will be awarded postgraduate bursaries. These students are expected to tutor and provide academic support and development to undergraduate students in the Department of Statistics for a certain number of hours per week.
Please contact the Department of Statistics for further information (E-mail: email@example.com)
Last Modified: Sun, 13 Jan 2013 08:49:56 SAST