Module Descriptions (Updated on 06 August 2009)

The prerequisites for the modules below are all "pass" prerequisites.

GEM2900 Understanding Uncertainty & Stats Thinking
Modular Credits: 4 
Workload: 4-0-0-3-3 
Prerequisites: nil 
Preclusion: Not for Statistics Major students 
Cross-listing: nil 

This module, using a minimum of mathematical or statistical prerequisites, aims to help the student make rational decisions in an uncertain world. Uncertainty, variability and incomplete information are inherent; to a greater or lesser extend, in all disciplines. One approach to dealing with this is through statistical and probabilistic ideas about information. The student will, throughout the module, gain an understanding of the strengths and weaknesses of such a data based approach and learn how and when such an approach is appropriate. The student will also learn practical skills in interpreting statistical information and gain the ability to critically evaluate statistically based arguments. 

GEM2901 Reporting Statistics in the Media 
Modular Credits: 4 
Workload: 4-0-0-3-3 
Prerequisites: NIL 
Preclusion: NIL 
Cross-listing: NIL 

Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write' (H.G. Wells). In the Information Age every educated person is surrounded by statistical information of all kinds. This information comes frequently through the media from governmental, scientific and commercial worlds. This module, using a minimum of mathematical or statistical prerequisites, aims to make the student statistically literate in reading and understanding such information. The course will be based on real world case studies of issues of current importance and relevance. The students' objectives in this course are as follows: (1) Students will learn to read, critically analyze, write about and present reports about all types of quantitative information. (2) Students will learn the strengths and weaknesses of using quantitative information in different circumstances. (3) Students will study a number of case studies of current interest. They will be able to compare and contrast the statistical treatments from different sources. 

ST1131 Introduction to Statistics
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisite: GCE 'AO' level or H1 Pass in Mathematics or its equivalent
Preclusions: ST1131A, ST1232, ST2334, CE2401, CN3421, EC2231, EC2303, PR2103. FBA students. Engineering students except ISE and SDE students

This module introduces students to the basic concepts and the methods of statistics. A computer package is used to enhance the effect of learning and to enable students to analyse complicated data. Topics include descriptive statistics, basic concepts of probability, sampling distribution, statistical estimation, hypothesis testing, linear regression. This module is targeted at students interested in Statistics and are able to meet the pre-requisite. It is also an essential module for students in the Department of Industrial and Systems Engineering and School of Design and Environment. Precludes students from Department of Mechanical Engineering.

To learn more about the module, please click here.


ST1131A Introduction to Statistics
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisite: FBA students
Preclusions: ST1131, ST1232, ST2334, CE2401, CN3421, EC2231, EC2303, PR2103

This module introduces students to the basic concepts and the methods of statistics. A computer package is used to enhance the effect of learning and to enable students to analyse complicated data. Topics include descriptive statistics, basic concepts of probability, sampling distribution, statistical estimation, hypothesis testing, linear regression. This module is essential to students from School of Business.

ST1232 Statistics for Life Sciences
Modular Credits: 4
Workload: 3-1-0-3-3
Prerequisite: GCE 'AO' level or H1 Pass in Mathematics or its equivalent
Preclusion: ST1131, ST1131A, ST2334, CE2401, CN3421, EC2231, EC2303, PR2103

An introduction to the basic principles and methods of biostatistics designed specifically for life sciences students who wish to learn modern research methods for analysing and extracting information from biological, biomedical and genomic data. Relevant examples will be used throughout the course to illustrate various techniques. A computer package will be used to enhance learning and to enable students to analyze real life data sets. Topics include tabular and graphical display of data, probability, probability distributions, sampling distributions, confidence intervals and regression analysis, distribution free tests, categorical data analysis, logistic and Poisson distribution, introduction to Bayesian inference. This module is essential to students of the Life Sciences.

ST2131 Probability
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisite: MA1102 or MA1102R or MA1312 or MA1507 or MA1505 or MA1505C or MA1521 or CE2401
Preclusions: MA2216, ST2334
Cross Listing: MA2216

Counting methods, sample space and events, axioms of probability, conditional probability, independence, random variables, discrete and continuous distributions, joint and marginal distributions, conditional distribution, independence of random variables, expectation, conditional expectation, moment generating function, central limit theorem, the weak law of large numbers. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites. It is an essential module for students from Department of Industrial and Systems Engineering.

ST2132 Mathematical Statistics
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisite: ST2131 or ST2334 or MA2216
Preclusions: Nil

Random sample and statistics, method of moments, maximum likelihood estimate, Fisher information, sufficiency and completeness, consistency and unbiasedness, sampling distributions, x2-, t- and F-distributions, confidence intervals, exact and asymptotic pivotal method, concepts of hypothesis testing, likelihood ratio test, Neyman-Pearson lemma. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

To learn more about the module, please click here.

ST2137 Computer Aided Data Analysis (Elective, 4MC)
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisite: ST1131 or ST1131A or ST1232 or ST2131 or ST2334 or MA2216

This module provides a basic introduction to software and methodology that are the computational tools for performing statistical data analysis. The topics concentrate in the use of statistical computer packages with main focus on SAS, Splus and SPSS. Topics also include pseudorandom number generation, generating discrete and continuous random variables, data access, transformations, estimation, testing hypotheses, ANOVA, performing resampling methods and simulations. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

To learn more about the module, please click here.

ST2238 Introductory Biostatistics
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisite: ST1232
Preclusions: Not offered to Statistics Major and Minor students

An introduction to the basic principles and methods of biostatistics designed specifically for life sciences students who wish to learn modern research methods for analyzing and extracting information from biological, biomedical and genomic data. Relevant examples will be used throughout the course to illustrate various techniques. A computer package such as SAS or SPSS will be used to enhance learning and to enable students to analyze real life data sets. Topics include tabular and graphical display of data comparing two or more means, two sample and paired t test, rank test, analysis of variance, multiple comparison, basic experimental designs, randomization, replication, blocking and the use of balance, complete randomized and randomized complete block design, correlation and regression analysis, confidence and prediction intervals, multiple regression, regression diagnostics, categorical data anaylsis, prospective, cross-sectional and retrospective studies, chi-square and Fisher's exact test, McNemar test, odds and odds ratio, logistics and Poisson regression. This module is essential to students of the Life Sciences.

ST2334 Probability and Statistics
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisite: MA1306 or MA1102 or MA1102R or MA1505 or MA1505C or MA1521 or MA1312 or MA1507
Preclusion(s): ST1131, ST1131A, ST1232, ST2131, MA2216, CE2401, EC2231, PR2103, EC2303. ME students taking or having taken ME4273. All ISE students
Cross-listing(s): Nil

Basic concepts of probability, conditional probability, independence, random variables, joint and marginal distributions, mean and variance, some common probability distributions, sampling distributions, estimation and hypothesis testing based on a normal population. This module is targeted at students who are interested in Statistics and are able to meet the prerequisites. Preclude ME students taking or have taken ME4273.

ST2335 Statistical Methods
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisite: ST1131 or ST2334
Preclusions: ST3131

Descriptive statistics, conditional expectation, correlation coefficient, bivariate normal distribution, simple linear regression, analysis of variance, nonparametric methods. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

ST2288 Basic Undergraduate Research in Statistics and Applied Probability I
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisite: NIL

For details, please refer to the “Undergraduate Research Opportunities Programme in Science” (UROPS).

ST2289 Basic Undergraduate Research in Statistics and Applied Probability II
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisite: NIL

For details, please refer to the “Undergraduate Research Opportunities Programme in Science” (UROPS).

ST3131 Regression Analysis
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisite: ST2131 or ST2334 or MA2216
Preclusions: ST2335, EC3231, EC3303

This module focuses on data analysis using multiple regression models. Topics include simple linear regression, multiple regression, model building and regression diagnostics. One and two factor analysis of variance, analysis of covariance, linear model as special case of generalized linear model. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

To learn more about the module, please click here.

ST3232 Design and Analysis of Experiments
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisite: ST2132 or ST2334
Preclusions: Nil

This module covers common designs of experiments and their analysis. Topics include basic experimental designs, analysis of one-way and two way layout data, multiple comparisons, factorial designs, 2k-factorial designs, blocking and confounding, fractional factorial design and nested designs. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

ST3233 Applied Times Series Analysis
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisites: ST2132 or ST2334
Preclusions: Nil

This module introduces the modelling and analysis of time series data. A computer package will be used to analyse real data sets. Topics include stationary time series, ARIMA models, estimation and forecasting with ARIMA models This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

To learn more about the module, please click here.

ST3234 Actuarial Statistics
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisites: ST2131 or ST2334 or MA2216
Preclusions: Nil

This module focuses on life contingencies and theory of risk. Topics include survival models and life tables, life annuities, assurances and premiums, reserves, joint life and last survivor statuses, multiple decrement tables, expenses, individual and collective risk theory. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

To learn more about the module, please click here.

ST3235 Statistical Quality Control
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisite: ST2131 or ST2334 or MA2216
Preclusions: All ISE students

Properties, designs and application of control charts, Shewhart charts, straight moving average chart, cumulative sum chart, exponentially weighted moving average chart, basic concepts of acceptance sampling, single, multiple and sequential sampling by attributes, variable sampling. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

ST3236 Stochastic Processes 1
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisites: (MA1101 or MA1101R or MA1311 or MA1508)and (ST2131 or MA2216)
Preclusion: MA3238. All ISE students
Cross-listing: MA3238

This module introduces the concept of modelling dependence and focuses on discrete-time Markov chains. Topics include discrete-time Markov chains, examples of discrete-time Markov chains, classification of states, irreducibility, periodicity, first passage times, recurrence and transience, convergence theorems and stationary distributions. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

To learn more about the module, please click here.

ST3239 Survey Methodology
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisite: ST2131 or ST2334 or MA2216
Preclusion: Nil

This module gives an introduction to the design of sample surveys and estimation procedures, with emphasis on practical applications in survey sampling. Topics include planning of surveys, questionnaire construction, methods of data collection, fieldwork procedures, sources of errors, basic ideas of sampling, simple random sampling, stratified, systematic, replicated, cluster and quota sampling, sample size determination and cost. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

ST3240 Multivariate Statistical Analysis
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisite: ST3131
Preclusion: Nil

This module focuses on the classical theory and methods of multivariate statistical analysis. Topics include distribution theory: multivariate normal distribution, Hotelling's T2 and Wishart distributions, inference on the mean and covariance, principal components and canonical correlation, factor analysis, discrimination and classification. This module is targeted at students who are interested in Statistics, are able to meet the pre-requisites and are matriculated in or after 2002.

ST3241 Categorical Data Analysis I
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisite: ST3131
Preclusion: Nil

Categorical response data and contigency tables, loglinear and logit models, Poisson regression, framework of generalized linear models, model diagnostics, ordinal data. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

ST3242 Introduction to Survival Analysis
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisite: ST2132
Preclusion: Nil

Examples of survival data, concepts and techniques used in the analysis of time to event data, including censoring, hazard rates, estimation of survival curves, parametric and nonparametric models, regression techniques, regression diagnostics. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

ST3243 Statistical Method in Epidemiology
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisites: (ST2131 or MA2216) and (ST2132)
Preclusion: Nil

This course will provide an introduction to the key concepts and principles of epidemiology. It emphasizes a quantitative approach to clinical and public health problems through the statistical analysis of epidemiologic data. The students will be equipped with the skills needed to understand critically the epidemiologic literature. Principles and methods are illustrated with examples. Topics include incidence prevalence and risk, mortality and morbidity rates, types of study designs: prospective, retrospective and cross-sectional study, association and causation, confounding and standardization,
precision and validity of epidemiologic studies, matching, screening, contingency tables, stratified analysis,
logistic regression. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

ST3244 Demographic Method
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisite: ST1131
Preclusion: Nil

This course will provide an introduction to the fundamental principles and methods of demography. The role of demographic data in describing the health status of a population, spotting trend and making projection will be highlighted. Topics include sources and interpretation of demographic data, rates, proportions and ratios, standardization, complete and abridged life tables, estimation and projection of fertility, mortality and migration, Interrelations among demographic variables, population dynamics, demographic models. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

ST3245 Statistics in Molecular Biology
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisite: ST2131 or ST2334
Preclusion: Nil

The module focuses on how statistics has been used successfully in solving important problems in molecular biology. Major topics covered are: Genetics, basic molecular biology, discrete probability, stochastic processes, design of experiments, parameter estimation, the bootstrap, testing hypotheses, Markov Chain Monte Carlo. This module is targeted at students who are intersted in Statistics and are able to meet the pre-requisites.

ST3246 Statistical Modelling for Actuarial Science
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisite: ST2132

The main objective of this module is to teach students how to apply statistical methods to construct actuarial loss model in insurance fields. Model-based approach is used to introduce those major topics in the module, such as loss distributions, frequency distributions, aggregate loss model, and credibility. Statistical methods and approaches, such as point and interval estimations, test of hypotheses, goodness of fit, maximum likelihood functions, Bayesian estimation, etc. are also discussed in details.

ST3288 Advanced Undergraduate Research in Statistics and Applied Probability I
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisites: Nil

For details, please refer to the “Undergraduate Research Opportunities Programme in Science” (UROPS).

ST3289 Advanced Undergraduate Research in Statistics and Applied Probability II
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisites: Nil

For details, please refer to the “Undergraduate Research Opportunities Programme in Science” (UROPS).

ST4199 Honours Project in Statistics
Modular Credits: 12
Workload: 0-2-0-0-13
Pre-requisite: At least one major at B.Sc./B.Appl.Sc. level; and minimum overall CAP of 3.50 on completion of 100 MCs or more

The objectives of the course are to develop the basic skills for independent scientific research, and to promote an appreciation of problem solving strategies in science. On completion of the course, students will be able to demostrate an appreciation of the current state of knowledge in a particular field of research, to master the basic techniques required for the study of a research question, and to communicate scientific information clearly and concisely in writtena nd spoken English.

ST4231 Computer Intensive Statistical Methods
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisite: ST2132
Preclusion: Nil

Empirical distribution and plug-in principle, general algorithm of bootstrap method, bootstrap estimates of standard deviation and bias, jackknife method, bootstrap confidence intervals, the empirical likelihood for the mean and parameters defined by simple estimating function, Wilks theorem, and EL confidence intervals, missing data, EM algorithm, Markov Chain Monte Carlo methods. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

To learn more about the module, please click here.

ST4232 Nonparametric Statistics
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisite: ST2132
Preclusion: Nil

This module focuses on the theory and methods of making statistical inference based on nonparametric techniques. Students will see the analyses of real data from various areas of applications. Topics include properties of order statistics, statistics based on ranks, distribution-free statistics, inference concerning location and scale parameters for one and two samples, Hajek's projection. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

To learn more about the module, please click here.

ST4233 Linear Models
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisite: ST3131
Preclusion: Nil

Linear regression model, general linear model, prediction problems, sensitivity analysis, analysis of incomplete data, robust regression, multiple comparisons, introduction to generalized linear models This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

To learn more about the module, please click here.

ST4234 Bayesian Statistics
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisite: ST2132
Preclusion: Nil

Bayesian principles: Bayes' theorem, estimation, hypothesis testing, prior distributions, likelihood, predictive distributions. Bayesian computation: numerical approximation, posterior simulation and integration, Markov chain simulation, models and applications: hierarchical linear models, generalized linear models, multivariate models, mixture models, models for missing data, case studies. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

ST4235 Simulation
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisite: ST2132
Preclusion: Nil

Pseudorandom number generation, generating discrete and continuous random variables, simulating discrete events, statistical analysis of simulated data, variance reduction, Markov Chain Monte Carlo methods. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

ST4237 Probability Theory 1
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisites: (MA2216 or ST2131)
Preclusion: Nil

Probability space, weak and strong laws of large numbers, convergence of random series, zero-one laws, weak convergence of probability measures, characteristic function, central limit theorem. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

ST4238 Stochastic Processes 2
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisites: MA3238 or ST3236
Preclusions: MA4251
Cross-listing: MA4251

This module builds on ST3236 and introduces an array of stochastic models with biomedical and other real world applications. Topics include Poisson process, compound Poisson process, marked Poisson process, point process, epidemic models, continuous time Markov chain, birth and death processes, martingale. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.


ST4240 Data Mining
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisite: ST3131
Preclusion: Nil

The module covers statistical techniques and tools such as kernel methods for estimating the density and regression functions, machine learning, hidden Markov Chain, EM algorithm, classification, cluster analysis and support vector machines for analyzing large data sets and for searching for unexpected relationships in the data. It also covers model selection for searching through a large collection of potential local models that describe some aspect of the data in an easily understandable way. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

ST4241 Design and Analysis of Clinical Trials
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisite: ST2132 or ST3242
Preclusion: Nil

This course will provide an introduction to the design and analysis of clinical trials. Emphasis is on the statistical aspects. Topics include introduction to clinical trials, phases of clinical trials, objectives and endpoints, the study cohort, controls, randomization and blinding, sample size determination, treatment allocation, monitoring trial progress: compliance, dropouts and interim analyses, monitoring for evidence of adverse or beneficial treatment effects, ethical issues, quality of life assessment, data analysis involving multiple treatment groups and endpoints, stratification and subgroup analysis, intent to treat analysis, analysis of compliance data, surrogate endpoints, multi-centre trials and good practice versus misconduct. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

To learn more about the module, please click here.

ST4242 Analysis of Longitudinal Data
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisite: ST3131
Preclusion: Nil

This course covers modern methods for the analysis of repeated measures, clustered data, correlated outcomes and longitudinal data, with a strong emphasis on applications in the biological and health sciences. Both continuous and discrete response variables will be considered. The use of generalized estimating equations (GEE) will be emphasized. Topics include introduction to longitudinal studies, exploring longitudinal data, analysis of variance for repeated measures, general linear models for longitudinal data, growth curves, models for covariance structure, estimation of individual trajectories, generalized linear models for longitudinal discrete data, marginal models, generalized estimating equations, random effects models and transition models. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

ST4243 Statistical Methods for DNA Microarray Analysis
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisites LSM1102 and ST3240
Preclusion: Nil

This is a level 4000 advance course on the statistical design and analysis of genetic experiments with concentration in DNA microarray experiments. The course covers a variety of statistical methods including basic array designs, statistical models and hypothesis testing, cluster analysis and other multivariate analysis methods that play a role in the analysis of DNA microarray experiments. The students will be required to have the knowledge of statistics and of statistical genetics that is provided by the Pre-requisites or equivalent. The students will have access to real data from microarray experiments and will practice with specialized software. Since this is a new expanding area and the experiments are constantly evolving, emphasis will be placed on gaining the basic knowledge and software expertise for designing new experiments and analyzing the results. The students will gain the knowledge and the practice to be able to analyze data from genetic experiments involving DNA microarrays and similar experiments. Topics include introduction to experimental genetics and DNA microarray techniques, basic design of experiments for microarrays, statistical models, modeling and testing for gene upregulation, principal components analysis and cluster analysis and gene clustering. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisites.

ST4245 Statistical Methods for Finance
Modular Credits: 4
Workload: 3-1-0-3-3
Pre-requisites: ST3131 or QF3101
Preclusion: Nil

The module aims to equip students with a repertoire of statistical analysis and modelling methods that are commonly used in the finance industry. Major topics include statistical properties of returns, regression analysis with applications to single and multi-factor pricing models, multivariate analysis with applications in Markowitz's portfolio management, modelling and estimation of volatilities, calculation of value-at-risk, nonparametric methods with applications to option pricing and interest rate markets. Students are assumed to have had no background in finance or economics and will be acquainted with the foundations of finance such as portfolio optimization and the Capital Asset Pricing Model. This module is targeted at students who are interested in Statistics and are able to meet the pre-requisite.