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Associate Professor & Head of Department
G Jäger, PhD(Karlsruhe)
Professor
SE Radloff, PhD(Rhodes)
Associate Professor
I Szyszkowski, PhD(Maria Curie-Sklodowska)
Senior Lecturers
LJ Bangay, MSc, HDE(Rhodes)
JS Baxter, MSc, PGDHE(Rhodes)
Lecturers
LC Njovane, MSc(Rhodes)
L Raubenheimer, MCom(UFS)
R Sypkens, MSc(UFS)
Mathematical Statistics (MST) and Applied Statistics (AST) are four-semester subjects which may be taken as major subjects for the degrees of BSc, BSc(InfSys), BA, BSocSc, BCom, BBusSc and BEcon.
To major in Mathematical Statistics a candidate is required to obtain credit in the following courses: MAT 1 or MAT 101 + MAT 102; MST 2; MST 3. See Rule S.23.
To major in Applied Statistics a candidate is required to obtain credit in the following courses: MAT 1 or MAT 101 + MAT 102; MST 2; AST 3.
The availability of both MST 3 and AST 3 in any year is subject to adequate staffing.
A matriculation pass in mathematics is a prerequisite for admission to all first-year courses in the Department.
If a candidate obtains a pass in a semester-course offered by the Department, but fails to gain an aggregate pass for the full course in the following ordinary or supplementary examination, then that candidate is required to pass the semester-course failed in order to gain the full-credit.
Besides the major courses, the department offers various other courses in Statistics.
Statistics (STA 1) is a two-semester first-year course which may be taken for degree/diploma curricula in the Faculties of Humanities, Commerce and Science.
Theory of Finance and Statistics is a two-semester course comprising a one-semester course: Theory of Finance (STA 140/TOF 1), and a one-semester course: Statistics 1D (STA 130/1D). This course is taken for degree curricula in the Faculty of Commerce.
Summer School
The Department normally offers Summer School programmes in Theory of Finance, Statistics 1D and Statistics 101, but reserves the right not to offer a course in any year should it so decide. Summer Schools are held in January each year. Each school lasts for two weeks. Summer School is intended for preparation for supplementary examinations in courses failed in the previous year.
The attention of students who hope to pursue careers in the field of Bioinformatics is drawn to the recommended curriculum that leads to postgraduate study in this area, in which Mathematical Statistics is a recommended co-major with Biochemistry, and for which two years of Computer Science and either Mathematics or Mathematical Statistics are prerequisites. Details of this curriculum can be found in the entry for the Department of Biochemistry, Microbiology and Biotechnology.
See the Departmental Web Page http://www.ru.ac.za/academic/departments/statistics/ for further details, particularly on the content of courses.
STA 101
(One paper of 3 hours)
Graphical representations of data; measures of location and dispersion; simple classical probability theory; basic discrete and continuous distributions; expected values and moments; correlation and simple linear regression; point and interval estimation; modern univariate statistical inference; one-way ANOVA.
STA 102 (One paper of 3 hours and one practical of 3 hours)
Non-parametric procedures; design and analysis of questionnaires; contingency tables; factorial analysis of variance designs; computer based analysis.
Other first-year courses offered in the Department are as follows:
STA 130/STA 1D - Statistics 1D (One paper of 3 hours)
Collection and tabulation of statistical data; graphs and diagrams; frequency distributions; measures of central tendency and dispersion; shapes and parameters of classical distributions (normal, binomial, Poisson); simple classical probability theory; conditional probability; analysis of time series; index numbers; correlation and simple linear regression; sampling distributions; point and interval estimation; hypothesis testing.
STA 140/TOF 1 - Theory of Finance (One paper of 3 hours)
Simple interest and discount, compound interest and discounting, simple and complex annuities, loans, depreciation, securities, linear programming, elementary differentiation and integration.
Credit in Mathematics and/or Statistics (MAT 1 and/or at least two semester credits of MAT 101, MAT 102, STA 1D or STA 101) is required before a student may register for MST 201 or MST 202. Adequate performance in MST 201 is required before a student may register for MST 202.
MST 201 (One paper of 3 hours)
Axiomatic probability theory; conditional probabilities; random variables and standard univariate distributions; expected values and moments; moment generating functions. A selection of topics from: decision theory; risk theory and simulation.
MST 202 (One paper of 3 hours)
A selection of topics from: jointly distributed variates and distributions of functions of random variables; sampling distributions point and interval estimation; tests of hypotheses; design and analysis of questionnaires; contingency tables; correlation and linear regression; time series analysis; econometrics.
Credit in Mathematical Statistics (MST 2) and in Mathematics (MAT 1 or MAT 101 + MAT 102) is required before a student may register for MST 301 or MST 302. Note that full credit in Mathematics 1 is not required for entry into MST 201 or MST 202, but is required for entry into MST 301 and MST 302. Adequate performance in MST 301 is required before a student may register for MST 302.
MST 301 / AST 301 (Two papers of 3 hours each)
Distribution theory; normal sampling theory, multivariate normal distribution; the general linear model, analysis of variance; non-linear regression.
MST 302 (Two papers of 3 hours each)
A selection of topics from: limit theorems; applied stochastic processes; multivariate statistical procedures; non-parametric procedures; sampling techniques; quality control; Bayesian inference; financial statistics.
MST 301 is held in the first semester and AST 302 in the second semester. Credit may be obtained in each course separately and, in addition, an aggregate mark of at least 50% will be deemed to be equivalent to a two-credit course AST 3, provided that a candidate obtains the required sub-minimum in each component. No supplementary examinations will be offered for either course.
Credit in Mathematical Statistics (MST 2) and in Mathematics (MAT 1 or MAT 101 + MAT 102) is required before a student may register for MST 301 or AST 302. Note that full credit in Mathematics 1 is not required for entry into MST 201 or MST 202, but is required for entry into MST 301 and AST 302. Adequate performance in MST 301 is required before a student may register for AST 302.
AST 302 (Two papers of 3 hours each)
A selection of topics from statistical quality and process control; elements of econometrics and time series analysis; sample survey theory and techniques.
The course consists of five modules and a research project. The modules may be selected from the following topics: Bayesian statistics; econometrics; linear models; multivariate analysis; probability theory; stochastic processes; time series analysis; survey methods and sampling techniques; stochastic calculus in finance; queueing theory and simulation; neural networks; neuro-fuzzy systems; applied data analysis; pattern recognition.
Suitably qualified students are encouraged to proceed to research degrees under the direction of the staff of the Department. Requirements for the MSc and PhD degrees are given in the General Rules. The Master's degree may be taken either by examinations and an extended essay, or by thesis, or by a combination of examinations and a thesis, as directed by the Head of the Department. A candidate may also be required to take an oral examination.
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