BACHELOR OF SCIENCE HONOURS APPLIED STATISTICS (HASTS)
| Module Code | Module Name | Description |
|---|---|---|
| HASTS101 | Probability Theory | Introduction to the theory of probability with emphasis on understanding of concepts. Probability, probability space, random variables, independence, conditional probability, conditional expectation, integral version of the law of total probability, Bayes' formula for densities, moment generating functions, joint MGF's, characteristic functions, multinomial and multivariate normal distribution. |
| HASTS102 | Applied Statistics | The aim of the course is to introduce students to the Scientific Approach to the analysis of data and focuses on Descriptive Statistics and an introduction to non-parametric statistics. |
| HASTS105 | Calculus | This course aims to gently introduce the rigour of mathematical analysis to first year undergraduate students with some background in A-Level calculus. The concentration is on motivating results and concepts geometrically rather than on providing rigorous proofs. Concepts are defined carefully and results stated precisely, but illustrated by way of vivid, concrete examples. The course covers topics such as: Vectors; Lines and planes. Vector-valued functions. Functions of several variables; Integration. Infinite series and Taylor series. |
| HASTS107 | Programming Fundamentals | This course introduces the fundamental concepts of programming and problem solving in a structured program logic environment. This course is aimed at students with little or no prior programming experience but a desire to understand computational approaches to problem solving. Topics include language syntax, data types, program organization, and problem solving methods, algorithm design, and logic control structures. Students will also learn to document and debug software and to utilize software libraries. Upon completion, students should be able to manage files with operating system commands, use top-down algorithm design, implement algorithmic solutions in a programming language and write small programs that allow them to accomplish useful goals. |
| HASTS114 | Linear Mathematics and Ordinary Differential Equations | This course is mostly about algebraic ideas and complements the material in Calculus. It introduces and develops concepts (including complex numbers and the algebra of polynomials) necessary for a first course in linear algebra. Along the way some of the mathematical areas where linear structure emerges as the common feature are included to give motivation and to illustrate the theory. Vector Spaces: Basic definitions and examples, subspaces, dimension and basis, the vector spaces and subspaces. Inner Product spaces: Basic definitions with many examples, the notions of norm (length) and distance (emphasis will be on the Euclidean product). Linear Transformations: Basic definitions and results, including images and kernels, with examples; matrix representations of linear transformations. Revision of basic techniques for the determination of solutions of first and second order ODE's. Modelling with first and second-order ODE's. Existence and uniqueness of solutions. Series solutions of linear ODE's, solutions near ordinary points, singular points, Euler equations. Laplace transform methods for solving initial value problem (IVP). Solution of IVP, Heaviside and Dirac functions. |
| HASTS112 | Regression Analysis and ANOVA | Review of Multivariate normal distribution, matrix and vector algebra linear model in matrix notation. Fitting a straight line by least squares; lack of fit; pure error; review of multiple linear regression. Estimation, testing hypotheses and confidence regions for parameters in full rank linear regression model; polynomial regression models. |
| HASTS113 | Official, Social and Economic Statistics |
The course introduces students to methodologies used in calculation of vital statistics in Zimbabwe. |
| HASTS111 | Statistical Inference | General overview of the ideas of statistical inference. Estimation: Methods of estimation, moments, maximum likelihood and least squares. Desirable properties of estimators such as sufficiency, unbiasedness, consistency and minimum variance. Sampling distributions of the estimators. Confidence intervals for normal random variables. Tests of Hypothesis: Review of Applied Statistics 1 non parametric methods. Introduction, intuitive choice of test statistics and construction of critical regions, types of errors, sampling distribution of test statistics, size and power of the test, tests on the mean, variance and proportion based on samples from normal populations, interpretation of Minitab outputs. |
| HASTS201 | Theory of Estimation and Testing of Hypotheses | The course is an extension of Statistical inference 1 with further consideration of theory of estimation and hypothesis testing. |
| HASTS206 | Survey Techniques, Sampling Theory and Demography | This course introduces students to sampling and survey methods. Students are introduced to the theory behind sampling, determination of sample sizes under cost and other constraints, determination of enumeration areas. The course covers life tables, measures of mortality and fertility, etc. Galton-Watson process. Stationary populations, closed populations. Characteristics of populations. Rates of increase, Coale’s approximation. Types of population estimates. Population models. |
| HASTS202 | Statistical Computing | This course aims to introduce the student to a variety of modern computing techniques, including use of everyday packages for data storage, management, analysis and reporting. Students learn about hard statistical and actuarial packages including programming. Using Statistical Packages- SPSS, STATA, SAS, MINITAB, EPI-INFO, and CSPro for analysing different types of data. Programming - Using R or SPLUS for data analysis and simulation, Latex for typesetting in preparation for writing up Mathematical papers. Using EXCEL programming to solve various problems by designing algorithms and other new specialised programmes. Computing for Actuarial science students should include analysing actuarial data. |
| HASTS203 | Monitoring, evaluation, Accountability and Learning | This module provides a comprehensive of Monitoring, Evaluation, Accountability and Learning concepts. It also describes methods and techniques for and effective project monitoring and offers and overview of evaluation techniques, standards and criteria. |
| HASTS212 | Design and Analysis of Experiments |
This course introduces students to how experiments should be set and resulting data be analysed. |
| HASTS211 | Time Series Analysis and Econometrics | The course focuses on model building for time series data in time domain. The role of econometrics in Zimbabwe, Review of General linear Model: least squares estimators, correlation matrix, partial correlation coefficients regression coefficients, Significance tests and confidence intervals. Multicollinearity, specification error, dummy variables. Generalised least squares: GLS estimator, heteroscedasticity, pure and mixed estimation, group observations and grouping of equations. Sources, consequences and conventional tests including Durbin-Watson, Thiel BLUS procedure, estimation and prediction. Stochastic Regressors: definition, instrumental variables, errors in variables. Lagged explanatory variables, lagged dependent variables and estimation including Koyck's lag Scheme. Simultaneous equations systems, endogenous and exogenous variables identification problem, restrictions on structural parameters, Two-stage and Three-stage least squares. |
| HASTS215 | Multivariate Methods | The course focuses on analysis of two or more variables. Properties; sampling distribution of sample mean and sample covariance matrix; Wishart distribution. Transformations to near normality; testing for normality. Hotelling's T2 distributions and likelihood ratio test; comparison with one dimensional case; confidence regions and simultaneous comparisons of component means; Bonferroni method of multiple comparisons. Comparing mean vectors from two populations; comparisons of several multivariate population means (one way MANOVA); simultaneous confidence intervals for treatment effects; profile analysis; ideas of two way MANOVA. Multivariate linear regression Review of eigenvalues and eigenvectors; spectral decomposition of symmetric matrix. Principal component analysis. Factor Analysis. Canonical correlation analysis. Discrimination and classification. Discriminant analysis, cluster analysis. |
| HASTS216 | Industrial Quality Control and Quality Management | This course introduces students to the application of statistical procedures in the improvement of the quality of products and services. |
| HASTS380 | Work-Related Learning | Students have to be attached to an institution (ZIMSTAT, research organizations, banks, insurance company, pension fund, government ministries, parastatals) where statistics is applied for the purpose of gaining practical skills and professional experience for at least 9 months. |
| HASTS370 | Work-Related Project |
The project gives opportunity for a student to demonstrate his problem-solving skills in circumstances similar to those existing in everyday life for a Statistician. |
| HASTS402 | Non-Parametric Statistics | This course aims to build on the basic knowledge gained in first and seconds years. The students will study further non parametric methods and will receive introduction to the Bootstrap, Jackknife procedures and to non-parametric regression. Use of R/SPSS/MINITAB/STATA is recommended throughout this course. |
| HASTS401 | General Linear Models | Form a unified underlying theory that covers simple and multiple linear regressions with special emphasis on full rank models. The course also covers aspects on the general linear hypothesis, models of not full rank and introduction to variance components. |
| HASTS408 | Operations Research and Optimisation | Review of Linear programming. Further linear programming: M-technique, revised simplex method, dual linear programming methods, shadow prices. Problem formulation and solution. Network construction, PERT-CPM methods, project control. Single-queuing models (MM/1), Multi-server queuing models (MM/c), finite queue variation. Optimal or economic Order quantities, deterministic models (single item and multiple item models) probabilistic models (continuous review model) .Bayesian methods, minimax maximin maximum likelihood, maximal opportunity criteria, introduction to Utility Theory. |
| HASTS409 | Panel and Longitudinal Data Analysis | This course introduce concepts of handling panel/longitudinal data in the fields of science, agriculture, medicine, engineering and social sciences. Application of concepts learned aim to solve real-life problems. |
| HASTS410 | Bioassays and Survival Models | This course aims to build on the ANOVA techniques gained earlier on. The students will be introduced to practical experimental design. Real life examples from industry, agriculture and medicine will be emphasised. Use of R/SPPS/MINITAB/STATA is recommended throughout this course. This course also aims at introducing survival models and analyzing survival data using various procedures. Topics covered include introduction to survival analysis, distributions for survival times, modelling survival times, model validation and time dependent variables. |
| HASTS416 | Stochastic Processes | The course covers classification of the general stochastic processes like the Poisson and Brownian Motion or Wiener Processes and review probability, Markov chains, Stochastic models, Queueing Theory, Renewal processes, Brownian motion and Martingales. |
| HASTS418 | Risk Theory | The course provides a further grounding in mathematical and statistical techniques of particular relevance to the work of insurance and finance. Topics covered are utility theory, individual risk models, collective risk models and applications of risk theory, ruin theory and credibility theory. |
| HASTS470 | Dissertation | Application of knowledge of statistics through an in-depth research project to solve problems in society. |