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# 161

### Statistics

161.101 Statistics for Business 15 credits
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 Statistics 15 credits
Statistical literacy, the ability to understand and reason with statistics and data, is becoming increasingly important as our world becomes more and more data-rich. This course focuses on developing statistical literacy in real-world contexts. We teach students to use software (Excel and RStudio) to summarise, display and analyse data. We explore data collection techniques including sampling methods and experimental design. We introduce statistical inference methods (confidence intervals, hypothesis testing and regression) with an emphasis on communicating results in context.
161.120 Introductory Statistics 15 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 Statistics 15 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 Biostatistics 15 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-Statistics 15 credits
An introduction to statistics in an agricultural context, including the presentation, analysis and interpretation of quantitative data.
161.200 Statistical Models 15 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 Analysis 15 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 Models 15 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 Experiments 15 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, pseudo-replication.
161.223 Introduction to Data Mining 15 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 15 credits
Biology, psychology, and other sciences require statistical methods for analysing and visualising data. This course is designed to be accessible to students from any discipline, first building a deeper understanding of fundamental statistical concepts, then teaching a range of practical approaches for exploring statistical relationships, testing hypotheses, evaluating models, and presenting conclusions.
161.251 Regression Modelling 15 credits
Common data analysis and regression techniques 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; non-linear and weighted regression.
161.303 Probability and Random Processes 15 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.
161.304 Statistical Modelling 15 credits
This course covers the ideas underlying statistical modelling, its implementation through computational methods, and links to practical applications. Topics include probability and random variables, models for discrete and continuous data, model selection, model fitting and goodness of fit, model inference, and introduction to stochastic processes.
161.305 Statistical Inference 15 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.
161.306 Advanced Data Analysis 15 credits
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 Learning 15 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 Design 15 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 Experiments 15 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 Analysis 15 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 Mining 15 credits
A practical approach to data mining with large volumes of complex data; prepare, cleanse and visualise data; supervised and unsupervised modelling; ensemble and bundling techniques; use of leading software tools.
161.325 Statistical Methods for Quality Improvement 15 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 Models 15 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 Biostatistics 15 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 Series 15 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 Project 15 credits
The course provides an opportunity for students to gain statistical research experience. Working with an academic staff member, students will develop a short research proposal, carry out the proposed research, and present their findings in an agreed manner.
161.382 Statistical Analysis Project 30 credits
The course provides an opportunity for students to gain statistical research experience. Working with an academic staff member, students will develop a short research proposal, carry out the proposed research, and present their findings in an agreed manner.
161.390 Special Topic 15 credits
161.391 Special Topic 15 credits
161.704 Bayesian Statistics 15 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.
161.705 Advanced Statistical Inference 15 credits
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 Theory 15 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 Control 15 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 Statistics 15 credits
A topic in the application of statistics such as non-parametric statistics, multiple comparisons, analysis of complex sample survey data.
161.731 Biostatistics for Researchers 15 credits
Statistical techniques for the biological, medical and other life sciences. Case studies are used to demonstrate topics such as experimental design, multivariate methods, linear models with normal and non-normal errors, nonlinear regression, and mixed effects models. Emphasis is placed on application of appropriate statistical techniques through extensive use of statistical software, and implementation of these techniques in a research project.
161.743 Statistical Reliability and Survival Analysis 15 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 Genetics 15 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 Data 15 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 Consulting 15 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 Mining 15 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 Project 15 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 Project 30 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 1 45 credits
A supervised and guided independent study resulting in a published work.
161.872 Thesis 90 Credit Part 2 45 credits
A supervised and guided independent study resulting in a published work.
161.875 Thesis 90 credits
A supervised and guided independent study resulting in a published work.
161.893 Research Report 60 credits
161.897 Thesis 120 Credit Part 1 60 credits
A supervised and guided independent study resulting in a published work.
161.898 Thesis 120 Credit Part 2 60 credits
A supervised and guided independent study resulting in a published work.
161.899 Thesis 120 credits
A supervised and guided independent study resulting in a published work.
161.900 PhD Statistics 120 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.