# 161

### Statistics

An introduction to the presentation, analysis and interpretation of quantitative data. Topics include the construction of charts and summary statistics, probability, sampling, hypothesis testing, regression, time series analysis and quality management.
161.111 Applied Statistics15 credits
Statistical literacy emphasising applications in the sciences and social sciences. Use of graphs and numbers to summarise and interpret data; data collection with surveys and experiments; sampling distributions to describe variability; introduction to statistical inference.
161.120 Introductory Statistics15 credits
Applied statistics emphasising applications in the sciences and social sciences. Use of graphs and numbers to summarise and interpret data; data collection with surveys and experiments; elementary probability and sampling distributions to describe variability; inference for means, proportions, contingency tables and regression.
161.122 Statistics15 credits
Statistical literacy and data collection. Descriptive statistics and the interpretation of data, probability, random variables and probability distributions, sampling and estimation, hypothesis testing, correlation and regression, use of R software.
161.130 Introductory Biostatistics15 credits
Applied statistics with emphasis on biology. Exploratory data analysis. Surveys and experiments. Elementary probability and sampling variability. Inference for means, proportions, contingency tables and regression.
161.140 Agri-Statistics15 credits
An introduction to statistics in an agricultural context, including the presentation, analysis and interpretation of quantitative data.
161.200 Statistical Models15 credits
The theory behind statistical modelling, and its links to practical applications. The course covers: basic probability and random variables, models for discrete and continuous data, estimation of model parameters, assessment of goodness-of-fit, model selection, confidence interval and test construction.
161.220 Data Analysis15 credits
Understanding, visualising and analysing data in a practical context using R/RStudio. Topics are selected from: data collection including experimental designs, observational studies, and surveys, data cleaning and preparation, exploratory analysis, visualisation of multivariate and time series data, regression, analysis of variance and covariance, autoregressive models and categorical data modelling.
161.221 Applied Linear Models15 credits
Statistical linear models for application in science, business and social science. Topics include simple and multiple regression; linear models with categorical explanatory variables; model diagnostics; inference for linear models; polynomial regression; models for time dependence; methods for variable selection; and weighted regression.
161.222 Design and Analysis of Experiments15 credits
The planning, conduct and analysis of scientific experiments, using examples from chemical, biological, genomic, and engineering sciences. Manipulation and visualisation of experimental data; advantages and disadvantages of various designs; coping with missing data and practical constraints. Introduction to design techniques and concepts including randomisation, blocking, structured treatments, balance and orthogonality, crossed and nested effects, pseudoreplication.
161.223 Introduction to Data Mining15 credits
An introduction to data mining techniques; analysis of moderate to large sized datasets; data preparation; handling missing data; statistical graphics and exploratory data analysis; prediction and classification by regression modelling, neural network and tree-based methods; cluster analysis; association mining with market basket methods; extensive use of a leading software tool.
161.250 Data Analysis for Biologists15 credits
Biology and other sciences require statistical methods to analyse data. This course provides a practical approach to the use and interpretation of statistical methods and software to analyse biological data arising in a variety of scientific contexts. Topics covered may include: data visualisation, experimental design, the central limit theorem, t-tests, randomisation tests, ANOVA, chi-squared tests, regression, and ANCOVA.
161.251 Regression Modelling 15 credits
Common data analysis and regression techniques for application in science, business and social science. Simple and multiple regression; model diagnostics and transformations; ANOVA, ANCOVA and general linear models with interactions; polynomial and piecewise regression; serial correlation; nonlinear and weighted regression; methods for non-normal responses; methods for variable selection; multicollinearity.
161.303 Probability and Random Processes15 credits
The principles of the theory of probability and its applications. Topics include the axioms of probability, conditional probability and independence of events; random variables and their properties; laws of large numbers and central limit theorem; simulation of random variables; theory and applications of random processes, including time series, Markov chains, the Poisson process and Brownian motion.
The use of modern computational statistical tools to solve real-world problems. Topics include: the basics of stochastic modelling, Markov chains, simulation methods, likelihood and Bayesian approaches, and the Markov chain Monte Carlo method of model fitting.
161.305 Statistical Inference15 credits
The theory underlying the methods used in statistical inference. Topics include estimation, hypothesis testing, goodness-of-fit test, likelihood, maximum likelihood estimation, likelihood ratio tests, confidence intervals and Bayesian inference.
Advanced tools for statistical analysis of complex situations where data may be non-normal and sampling may not be independent, identically distributed. Examples include: logistic and Poisson regression; contingency table analysis; mixed effect models for observational and experimental data; nonlinear regression; multivariate techniques; analysis of complex survey data; time series.
161.312 Statistical Machine Learning15 credits
An introduction to fundamental techniques of machine learning; analysis of large datasets; supervised and unsupervised learning; reinforcement and evolutionary learning; extensive use of programming software suitable for machine learning.
161.321 Sampling and Experimental Design15 credits
The implementation of appropriate sampling and experimental designs is a fundamental tool for successful research in many natural and human sciences. Topics include: the logic of scientific investigations, stratified random sampling, simple and complex ANOVA designs, fixed and random factors, nested hierarchies, interactions, mixed models, inference spaces and estimation of variance components.
161.322 Design and Analysis of Surveys and Experiments15 credits
Types of data collection; limits to statistical analysis in the absence of sound statistical design. Non-sampling aspects of sample surveys, bias, design of stratified and clustered samples, analysis of survey data, and design effects for complex surveys. Principles of experimental design and analysis of variance, including randomisation, blocking, structured treatments, fixed and random effects, and crossed and nested effects.
161.323 Multivariate Analysis15 credits
This course teaches methods to understand patterns and structures inherent in data sets containing many variables. The fundamentals of data visualisation, clustering, and dimension reduction with examples taken from a range of applications.
161.324 Data Mining15 credits
This course teaches methods to understand patterns and structures inherent in data sets containing many variables. The fundamentals of data visualisation, clustering, and dimension reduction with examples taken from a range of applications.
161.325 Statistical Methods for Quality Improvement15 credits
A comprehensive introduction to statistical process control, industrial experimentation and other methods of quality improvement and management. Topics covered include a brief introduction to quality, total quality management, simple tools for quality improvement and ISO 9000. The major topics covered are control charts, process capability, factorial experiments, fractional replication of 2^k design, response surface methods, Taguchi methods and acceptance sampling. Special emphasis will be given to the use of appropriate statistical software.
161.327 Generalised Linear Models15 credits
Fitting models where Normality cannot be assumed. Applications include exponential lifetimes, binary survivals, Poisson accidents and contingency tables. Practical examples will be analysed with a computer package.
161.331 Biostatistics15 credits
Sciences such as biology and medicine yield data that require a wide range of statistical techniques, including standard linear models and their extensions. Case studies are used to demonstrate topics such as nonlinear regression, linear models for binary and count data, and mixed effects models. Emphasis is placed on application of appropriate statistical techniques through extensive use of statistical software.
161.342 Forecasting and Time Series15 credits
A practical course on analysing data that arise sequentially in time (e.g. sales figures, precipitation, crime rates, census figures, share prices, etc.). Detecting trends and underlying seasonal patterns; Box-Jenkins methodology, autoregressive and moving average processes; exponential smoothing, classical decomposition and regression methods; introduction to multivariate time series; simulation.
161.380 Statistical Analysis Project15 credits
The course provides an opportunity for Graduate Diploma in Statistics students to gain statistical research experience. Under supervision of academic staff, students will develop a short research proposal, carry out the proposed research, and write a research report.
161.382 Statistical Analysis Project30 credits
The course provides an opportunity for Graduate Diploma in Statistics students to gain statistical research experience. Under supervision of academic staff, students will develop a short research proposal, carry out the proposed research, and write a research report.
161.390 Special Topic15 credits
161.391 Special Topic15 credits
161.704 Bayesian Statistics15 credits
Introduction to the Bayesian paradigm. Markov Chain Monte Carlo estimation using WinBUGS. Comparison with frequentist statistics. Noninformative and improper priors. Inference and model selection. Linear and generalized linear models. Hierarchical Bayes.
Properties of estimators: unbiasedness, consistency, efficiency and sufficiency. Methods of estimation with particular emphasis given to the method of maximum likelihood. Hypothesis testing and interval estimation. Nonparametric tests. Computationally intensive methods such as numerical likelihood estimation and Monte Carlo inference. Resampling methods.
161.709 Topic in Statistical Theory15 credits
A topic in the theory of statistics, such as probability theory, Bayesian statistical theory, statistical decision theory, martingales and stochastic integrals.
161.725 Statistical Quality Control15 credits
Revision of statistical process control procedures, evaluation of control chart performance and statistical design of charts, control of high quality process, multivariate process control, new process capability indices, statistical intervals. Industrial experimentation topics, evolutionary operation, analysis of means (ANOM) etc. Revision of acceptance sampling, continuous and special purpose sampling plans. Use of statistical packages.
161.729 Topics in Applied Statistics15 credits
A topic in the application of statistics such as non-parametric statistics, multiple comparisons, analysis of complex sample survey data.
161.743 Statistical Reliability and Survival Analysis15 credits
Lifetime data occur in a wide variety of contexts: medical, demographic, industrial, economic. This course gives an introduction to the theory and practice of analysing lifetime data, commonly called survival analysis in medical contexts and reliability analysis in engineering.
161.744 Statistical Genetics15 credits
Statistical methods for biological sequence analysis, analysis of gene expression data, and inference of biological networks. Applications will also be described in evolution and population genetics.
161.762 Multivariate Analysis for Big Data15 credits
Research methods suitable for the analysis of big datasets containing many variables. The fundamentals of data visualisation, customer segmentation, factor analysis and latent class analysis with examples taken from business and health fields. Emphasis will be placed on achieving a conceptual understanding of the methods in order to implement and interpret the outcomes of multivariate analyses.
161.770 Statistical Consulting15 credits
Students are given the opportunity to serve as a consultancy intern with close supervision of staff involved in consultancy activities. Instruction and experience in consultant/client interaction, communication skills, statistical practice, statistical computation and technical writing.
161.777 Practical Data Mining15 credits
A practical approach to data mining with large volumes of complex data; prepare, cleanse and explore data; supervised and unsupervised modelling with association rules and market basket analysis, decision trees, multi-layer neural networks, k-nearest neighbours, k-means clustering and self-organising maps, ensemble and bundling techniques, text mining; use of leading software tools; business examples and research literature.
161.780 Statistical Analysis Project15 credits
The course provides an opportunity to gain statistical research experience. Under supervision of academic staff, students will develop a short research proposal, carry out the proposed research, and write a research report.
161.782 Statistical Analysis Project30 credits
The course provides an opportunity to gain statistical research experience. Under supervision of academic staff, students will develop a short research proposal, carry out the proposed research, and write a research report.
161.871 Thesis 90 Credit Part 145 credits
A supervised and guided independent study resulting in a published work.
161.872 Thesis 90 Credit Part 245 credits
A supervised and guided independent study resulting in a published work.
161.875 Thesis90 credits
A supervised and guided independent study resulting in a published work.
161.893 Research Report60 credits
161.897 Thesis 120 Credit Part 160 credits
A supervised and guided independent study resulting in a published work.
161.898 Thesis 120 Credit Part 260 credits
A supervised and guided independent study resulting in a published work.
161.899 Thesis120 credits
A supervised and guided independent study resulting in a published work.
161.900 PhD Statistics120 credits
Each project is an individualistic effort on the part of the student in collaboration with a supervisor. The type of project and the work to be carried out will be decided jointly by the student and the supervisor.

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