Knowledge of Mathematics is absolutely essential for the statistician. Access to a computer is compulsory from the NQF Level: 6 onwards as CDs form part of the study material in certain modules. Credit for a BSc degree is granted for: (i) either STS111 and STS112 or STA121, 122, 123 and 124 or STA1501 and STA1502 (ii) either STS1113 or (STA121 and 122) or STA1501 and STA1502, (iii) either STS1124 or STA1501, (iv) either STA106 or STA124, (v) either STA1510 or STA1610, (vi) either STA1503 or STA2610, (vii) either STA121 and STA123 or STA1501, (viii) either STA121 and STA124 or STA1501 (ix). Credits for other previously passed Statistics courses is at the discretion of the Department.
NOTE: The modules STA1510 and STA1610 are both service modules and do not meet the requirements for admission to any second or third level modules. The same syllabus
is covered in both these service modules, but assessment at the two NQF levels differs. STA1510 and STA1610 may not both be included in one degree composition. The module
STA2610 is offered for BCom students only. STA1503 and STA2610 may not both be included in one degree.
Statistics for the generic Bachelor of Science degree
Major combinations:
NQF Level: 5: STA1501 STA1502 STA1503 plus MAT1512 MAT1503
NQF Level: 6: STA2601 STA2602 STA2603 and STA2604 plus MAT1613 DSC1630 MAT2615 MAT2611
NQF Level: 7: STA3701 STA3702 STA3703 STA3704 and STA3705 or STA3710
Statistics for the generic Bachelor of Commerce degree
Major combinations:
NQF Level: 5: STA1501, STA1502
NQF Level: 6: STA2601, STA2602, STA2603, STA2604, STA2610
NQF Level: 7: STA3701, STA3702, STA3703, STA3710, (STA3704 or STA3705)
Access to computer is compulsory to STA1504, STA1506 and STA1507.
| Descriptive Statistics and Probability - STA1501 |
| Under Graduate Degree |
Year module |
NQF level: 5 |
Credits: 12 |
| Module presented in English |
|
| Purpose: The purpose of this module is to give learners an entry level understanding and appreciation of the basic concepts and use of statistics. Qualifying students will have a critical understanding of the purpose and limitations of statistical methods. They will be able to interpret and present statistical information by using appropriate tables or charts, and by means of numerical measures relating to central location, relative standing and variability as well as basic concepts of probability. They will understand the basic concepts of association and trend analysis |
| Statistical Inference I - STA1502 |
| Under Graduate Degree |
Year module |
NQF level: 5 |
Credits: 12 |
| Module presented in English |
|
|
Co-requisite: STA1501 |
| Purpose: To have a basic perspective of the role of the sampling distribution of the mean, a proportion and the difference between two means in statistical inference, interval estimation and hypothesis testing. Students will be able To estimate single and combinations of population parameters; understand one-way analysis of variance; apply parametric and nonparametric tests such as two Chi-squared tests and the Wilcoxon signed rank sum test. They will also be familiar with simple linear regression and correlation, as well as with the basics of time series analysis and forecasting. The contents of this module are relevant in a wide variety of applications in business and economics and represent a significant contribution to the development of the student as a statistics practitioner. |
| Distribution Theory I - STA1503 |
| Under Graduate Degree |
Year module |
NQF level: 5 |
Credits: 12 |
| Module presented in English |
|
| Pre-requisite: STA1501 |
Co-requisite: STA1501 and (MAT1512 or DSC1520 or DSC1620) |
| Purpose: Qualifying Students will have a solid fundamental introductory knowledge of and skills in statistical theory and have a clear understanding of the nature of mathematical statistics in terms of its objective, namely statistical inference. These competencies include knowledge of different theoretical distributions for populations, using probability theory, to progress to statistical inference in an accurate mathematical manner. In this process, distribution theory models will be applied in specific discrete and continuous random variables. This module will support further studies and applications in the sector of statistical theory in the field Statistics, as part of the Bachelor of Science and Bachelor of Commerce qualifications. This module will be an illustration of Mathematical Statistics as a theory of information to contribute to the development of communities and of research in Southern Africa, Africa or globally, utilizing mathematics extensively, but only as a tool. |
| Basic Statistics For Experimental Sciences - STA1504 |
| Higher Certificate |
Year module |
NQF level: 5 |
Credits: 12 |
| Module presented in English |
Module presented online |
| Purpose: The purpose of this module is to give learners the basic statistical skills needed in experimental work in particular in the Physical sciences. Qualifying students will be able to utilize their knowledge and their analytical, practical and interpretative skills in using statistics and statistical software in collecting and capturing data from an experiment, analysing the data and reporting on it. They will be able to apply their skills to assist in decision making. |
| Statistics for Beginners - STA1505 |
| Higher Certificate |
Year module |
NQF level: 5 |
Credits: 12 |
| Module presented in English |
|
| Purpose: The purpose of this module is to give learners an entry level understanding and appreciation of the basic concepts and use of statistics. Qualifying students will have a critical understanding of the purpose and limitations of statistical methods. They will be able to interpret and present statistical information by using appropriate tables or charts, and by means of numerical measures relating to central location, relative standing and variability as well as basic concepts of probability. They will understand the basic concepts of association and trend analysis.
The contents of this module are at the entry level for the basic knowledge of the future professional statistician in the academic, government or industry sectors.
|
| Basic Statistical Computing - STA1506 |
| Higher Certificate |
Year module |
NQF level: 5 |
Credits: 12 |
| Module presented in English |
Module presented online |
| Purpose: The purpose of this module is give learners entry level practical and professional skills at using a statistical computer program to do data management and data exploration. The qualifying student is familiar with data cleaning and coding, data entry and capturing, as well as exploring data and producing reports summarising the key points of the data.
The contents of this module are at the entry level of data management and data exploration, for the future professional statistician in the academic, government or industry sectors.
|
| Introduction to Research Skills - STA1507 |
| Higher Certificate |
Year module |
NQF level: 5 |
Credits: 24 |
| Module presented in English |
|
|
Co-requisite: STA1505, STA1506 |
| Purpose: The purpose of this module is give learners basic skills in planning and conducting a statistical investigation. Qualifying students will be able to utilize their knowledge and their analytical, practical and interpretative skills in statistics and statistical software by undertaking a basic statistical research project in real life and reporting on it. They will be able to apply their skills to assist in decision making. |
| Basic Statistics - STA1510 |
| Under Graduate Degree |
Year module |
NQF level: 5 |
Credits: 12 |
| Module presented in English |
|
| Purpose: To ensure that students are introduced to the most important basic statistical concepts. After completion students should have an informed understanding of different visual descriptions of data, including graphical and tabular techniques; measures of central location, dispersion and association. They should be able to use probability as a tool to create discrete and continuous probability distribution, used extensively in statistical inference; determine confidence intervals and perform hypothesis testing involving a sample mean and proportion; apply different forms of Chi-square testing; understand simple linear regression and correlation. |
| Introduction to Statistics - STA1610 |
| Under Graduate Degree |
Year module |
NQF level: 6 |
Credits: 12 |
| Module presented in English |
|
| Purpose: To ensure that students are introduced to the most important basic statistical concepts. After completion students should have an informed understanding of different visual descriptions of data, including graphical and tabular techniques; measures of central location, dispersion and association. They should be able to use probability as a tool to create discrete and continuous probability distributions, used extensively in statistical inference; determine confidence intervals and perform hypothesis testing involving sample means and proportions; apply different forms of Chi-square testing; understand simple linear regression and correlation. |
| Applied Statistics II - STA2601 |
| Under Graduate Degree |
Year module |
NQF level: 6 |
Credits: 12 |
| Module presented in English |
|
| Pre-requisite: STA1501 & STA1502 |
|
| Purpose: To enable students to identify the correct technique, manage the statistical software JMP to do the computations and interpret the results for decisions regarding tests for normality, independence and hypotheses concerning means, variances and regression. Access to a computer is compulsory. |
| Statistical Inference II - STA2602 |
| Under Graduate Degree |
Year module |
NQF level: 6 |
Credits: 12 |
| Module presented in English |
|
| Pre-requisite: STA1501 & STA1502 |
Co-requisite: STA1503 or STA2610 |
| Purpose: To enable students to gain insight in statistical inference using different properties of estimation and methods of estimation. Included are linear models and estimation by least squares as well as designing experiments and analysis of variance procedures. |
| Distribution Theory II - STA2603 |
| Under Graduate Degree |
Year module |
NQF level: 6 |
Credits: 12 |
| Module presented in English |
Module presented online |
| Pre-requisite: (STA1503 & MAT1512) or DSC1520 or DSC1620 |
Co-requisite: STA2610 |
| Purpose: To gain insight into the role that formal theory plays in data analytic methods, discussing a wide variety of discrete and continuous distributions simultaneously. After completion students should understand the joint probability structure of two random variables (discrete and continuous case); be able to calculate expectation, variance, covariance, conditional expectation and moment-generating functions; have insight into distributions of functions of independent random variables; prove the law of large numbers and the central limit theorem under fairly strong assumptions; comprehend how the Chi-square, t, and F distributions are derived from the normal distribution. |
| Forecasting II - STA2604 |
| Under Graduate Degree |
Year module |
NQF level: 6 |
Credits: 12 |
| Module presented in English |
|
| Pre-requisite: STA1501& STA1502 |
|
| Purpose: To see forecasting as a structured process of classified techniques. After completion students can explore time series data, looking at seasonality, stationarity and trend; classify techniques for forecasting and asses accuracy of forecasts; deal with different characteristics of time series, such as smoothing methods and seasonal models; establish credibility in forecasting and implement the forecasting process. |
| Statistical Distributions - STA2610 |
| Under Graduate Degree |
Year module |
NQF level: 6 |
Credits: 12 |
| Module presented in English |
|
| Pre-requisite: STA1501 |
Co-requisite: STA1501 and STA1502 and (DSC1520 or DSC1620) |
| Purpose: To have a solid fundamental introductory knowledge of and skills in statistical theory and have a clear understanding of the nature of mathematical statistics in terms of its objective, namely statistical inference. These competencies include knowledge of different theoretical distributions for populations, using probability theory, to progress to statistical inference in an accurate mathematical manner. In this process, distribution theory models will be applied in specific discrete and continuous random variables. This module will support further studies and applications in the sector of statistical theory in the field Statistics, as part of the Bachelor of Science and Bachelor of Commerce qualifications. This module will be an illustration of Mathematical Statistics as a theory of information to contribute to the development of communities and of research in Southern Africa, Africa or globally, utilizing mathematics extensively, but only as a tool. |
| Applied Statistics III - STA3701 |
|
Year module |
NQF level: 7 |
Credits: 12 |
| Module presented in |
Module presented online |
| Pre-requisite: STA2601 |
Co-requisite: STA3703 and (MAT2611 or STA3710) |
| Purpose: To enable students to demonstrate an understanding of one- and two-way analysis of variance, fixed effects and mixed models, and simple and multiple linear regression. Access to computer is compulsory. |
| Statistical Inference III - STA3702 |
| Under Graduate Degree |
Year module |
NQF level: 7 |
Credits: 12 |
| Module presented in English |
|
| Pre-requisite: STA2603 or STA2610 |
Co-requisite: STA2602 |
| Purpose: To gain theoretical insight into likelihood, data reduction, point estimation and interval estimation. |
| Distribution Theory III - STA3703 |
| Under Graduate Degree |
Year module |
NQF level: 7 |
Credits: 12 |
| Module presented in English |
|
| Pre-requisite: STA2603 & MAT2615) or (STA2603 & STA2610) |
Co-requisite: STA3710 ( only for BCom students) |
| Purpose: To gain insight into distributions and their relationships. After completion students should comprehend non-centrality; understand compounding and generalization as methods for finding parameter-rich distributions; use bivariate and multivariate distributions to describe normal and non-normal variables. |
| Time Series III - STA3704 |
| Under Graduate Degree |
Year module |
NQF level: 7 |
Credits: 12 |
| Module presented in English |
|
| Pre-requisite: STA1501 & STA1502 |
Co-requisite: STA2604 |
| Purpose: To gain insight into Box-Jenkins methodology, AR, MA and ARIMA models; also to use statistical software for practical modelling of time series. |
| Sampling Techniques - STA3705 |
| Under Graduate Degree |
Year module |
NQF level: 7 |
Credits: 12 |
| Module presented in English |
|
| Pre-requisite: STA1501 & STA1502 |
Co-requisite: STA2601 |
| Purpose: To gain more advanced insight into stratified random sampling; systematic and cluster sampling; estimation of the sample size; ratio and regression estimation; sampling with unequal probabilities; complex surveys; non-response. |
| Mathematical Techniques in Statistics - STA3710 |
| Under Graduate Degree |
Year module |
NQF level: 7 |
Credits: 12 |
| Module presented in English |
|
| Pre-requisite: MAT1512 or DSC1520 or DSC1620 |
|
| Purpose: To gain a basic understanding of matrix presentations and be able to apply calculus in statistical calculations. After completion of this module students should have mastered the basics of matrix calculations; know about linear dependence and independence; determine the three matrix reductions; invert a matrix; find eigen values; apply all these techniques in statistics. Students should be able to solve problems where differentiation and integration techniques have to be applied. Other topics include generalized inverses, Kronecker products and matrix differentiation. |
| Research project in Statistics - HRSTA80 |
| Honours |
Year module |
NQF level: 8 |
Credits: 36 |
| Module presented in English |
Module presented online |
|
Co-requisite: STA4803, STA4804, STA4807, STA4812, STA4813 & STA4814 |
| Purpose: The purpose of this module is to prepare the student for research-based postgraduate study. Students completing this module successfully will be able to plan and conduct statistical research under supervision and can present the findings of the research in an appropriately structured written research report. They will be able to adopt a critical and ethical approach to conducting statistical analysis and research as well as reporting on it, both in their own work and in that of others. |
| Linear Models - STA4803 |
| Honours |
Year module |
NQF level: 8 |
Credits: 12 |
| Module presented in English |
Module presented online |
|
Co-requisite: STA4804 |
| Purpose: About the module: The module seeks to provide the student with skills in the theory of linear models and to enhance their ability to formulate, analyse and model research problems using matrix algebra. The module provides insights to the theoretical underpinnings of models. The successful student will be able to contribute to the development of statistical software and in the application of techniques to real-life data. Students will be able to define various components related to algebraic matrices; derive distributions related to a particular matrix and conduct hypothesis testing under various assumptions. |
| Regression - STA4804 |
| Honours |
Year module |
NQF level: 8 |
Credits: 12 |
| Module presented in English |
Module presented online |
|
Co-requisite: STA4803 |
| Purpose: About the module: The modules seeks to provide the student with knowledge and principles for application of statistical regression techniques and related procedures. The successful student will be able to analyse data using simple linear, multiple linear, polynomial regression and other robust regression models such as weighted least squares, median regression, etc. Also, the student will be able to use various regression diagnostics to detect data aberrations (in both the design space and response variable) and propose appropriate remedies. Further, in all these topics, identifying and solving problems in which responses show that responsible decisions using critical and creative thinking is paramount. |
| Time Series - STA4807 |
| Honours |
Year module |
NQF level: 8 |
Credits: 12 |
| Module presented in English |
Module presented online |
|
Co-requisite: STA4803, STA4804 |
| Purpose: About this module: The modules seeks to provide the student with knowledge and principles for application of time series analysis techniques. The successful student will be able to describe components of time series and analyse data using both deterministic and stochastic models (the Box-Jenkins ARIMA and seasonal ARIMA models). Also, students will be able to use spectral (frequency domain) techniques to search for periodicities (seasonalities) and well as modelling changing variance (heteroscedasticity) in a time series, i.e., ARCH and GARCH models. Further, in all these topics, students will be able to use statistical software, e.g. R, identify and solve problems in which responses show responsible decisions using
critical and creative thinking. |
| Survival Analysis - STA4808 |
| Honours |
Year module |
NQF level: 8 |
Credits: 12 |
| Module presented in English |
Module presented online |
|
Co-requisite: STA4803 |
| Purpose: About this module: The purpose of the module is to explore different aspects of survival analysis as data analysis methodology. Using statistical software, enable the student to explore and analyze a wide spectrum of problems on time to event data. More specifically, the successful student should be able to describe the distribution of failure times; apply and interpret nonparametric, semiparametric and parametric analysis of survival data, and lastly, build models and perform diagnostics. |
| Nonparametric Regression - STA4809 |
| Honours |
Year module |
NQF level: 8 |
Credits: 12 |
| Module presented in English |
Module presented online |
|
Co-requisite: STA4803, STA4804 |
| Purpose: About this module: The purpose of the module is to equip students with the knowledge and skills to apply the nonparametric regression methods such as kernel and spline smoothing with emphasis on nonparametric wavelet regression, additive model and non-parametric density estimation. Qualifying students will be able to apply these methods in finance, medical and environment sciences. Furthermore, the students will be able to use the statistical software such as R and SAS to analyze real data using nonparametric regression and other commonly used nonparametric methods.
|
| Probability and Stochastic Processes - STA4811 |
| Honours |
Year module |
NQF level: 8 |
Credits: 12 |
| Module presented in English |
Module presented online |
|
Co-requisite: STA4801 |
| Purpose: About this module: Students who complete this module should be able to solve problems relating to aspects of probability theory, with the emphasis on conditional probabilities and conditional expectations; and to solve problems relating to stochastic processes especially Poisson processes, discrete and continuous-time Markov chains. |
| Inference - STA4812 |
| Honours |
Year module |
NQF level: 8 |
Credits: 12 |
| Module presented in English |
Module presented online |
|
Co-requisite: STA4801 |
| Purpose: About this module: This module discusses the theory of statistical inference with emphasis on the applications of the theoretical concepts to specific examples, not on memorization of theoretical results and proofs. In particular, theories of optimal methods of estimation and hypotheses testing are discussed. Qualifying students will have a comprehensive fundamental knowledge of the theories of applied statistical methods so as to critically appraise them before use rather than
unconsciously using the methods. |
| Generalized Linear Models - STA4813 |
| Honours |
Year module |
NQF level: 8 |
Credits: 12 |
| Module presented in English |
Module presented online |
|
Co-requisite: STA4803, STA4804 |
| Purpose: About this module: The purpose of the module is to equip students with the knowledge and skills to apply the generalized linear models (GLMs) such as regression models for binary data, models for count data (e.g., Poisson regression) and log-linear models for contingency tables, in a wide range of real-life situations in various fields, e.g. in financial areas, biological sciences and health sciences. The module will also cover extensions of GLM theory to models for over-dispersion and quasi-likelihood estimation. Students who complete this module can assess whether a GLM can be used in a given situation, manage the statistical software such as R, SAS and STATA, to carry out a statistical analysis, and interpret the results. |
| Multivariate Statistical Techniques - STA4814 |
| Honours |
Year module |
NQF level: 8 |
Credits: 12 |
| Module presented in English |
Module presented online |
|
Co-requisite: STA4803, STA4804 & STA4813 |
| Purpose: The purpose of this module is to enable students to apply and interpret multivariate statistical techniques. While theory is important in understanding how a certain technique works, it is of little use if it cannot be applied. The focus will be on applied techniques in multivariate analysis for interdependence and dependence techniques using statistical packages such as SASJMP, SPSS, R and SAS to analyse and interpret outputs. The student should be able to describe how the techniques are used in practice, by covering aspects such as deciding when a technique is appropriate, how the relevant data is fed into statistical packages and interpretation of the output of such packages. |
| Statistical Techniques for Science and Engineering - STA4820 |
| Honours |
Year module |
NQF level: 8 |
Credits: 12 |
| Module presented in English |
Module presented online |
| Purpose: The purpose of this module is to give learners statistical skills needed in natural, physical, social, financial and economical sciences. Qualifying students will be able to utilize their knowledge and their analytical, practical and interpretative skills in data management, data analysis using appropriate software, and writing statistical reports based on the analysis. |