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Statistics

161.101 Statistics for Business15 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.

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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.

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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.

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161.140 Agri-Statistics15 credits
An introduction to statistics in an agricultural context, including the presentation, analysis and interpretation of quantitative data.

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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.

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161.220 Data Analysis15 credits
Understanding data is essential in the natural and social sciences, business, and industry. This course is practical and uses modern statistical software to analyse real-world data. Topics are selected from: data collection, data displays, exploratory analysis, regression, ANOVA, chi-squared tests, non-parametric tests, time series and forecasting.

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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.

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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.

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161.250 Data Analysis for Biologists15 credits
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 contexts, including ecology, zoology and marine biology. Topics covered may include: the central limit theorem, t-tests, randomisation tests, ANOVA, chi-squared tests, experimental design, regression and ANCOVA.

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161.304 Advanced Statistical Modelling15 credits
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.

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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.

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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.

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161.323 Multivariate Analysis15 credits
Methods to understand patterns and structures inherent in data sets containing more than one variable. The fundamentals of ordination, clustering and testing methods for the analysis of several variables, with examples taken from a range of applications.

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161.324 Data Mining15 credits
A practical approach to data mining with real life applications and case studies; analysis of moderate to large volumes of data; data warehousing and cleansing; descriptive and predictive modelling; classification and regression trees; neural networks; memory-based reasoning; dimension reduction; cluster analysis including self-organising maps; ensemble models with hybrid, bagging and boosting; basics of text mining; rare event prediction and time oriented analysis; extensive use of modern data mining software tools.

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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.

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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.

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161.331 Biostatistics15 credits
The biological sciences typically yield data that fail to satisfy the assumptions of traditional linear modelling tools. This course teaches a range of advanced statistical techniques for analysing biological data, including a review of linear models, non-linear regression, generalised linear models, and random-effects models. Emphasis is placed on developing practical experience with real biological data using modern statistical software.

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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.

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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.

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161.381 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.

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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.

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161.390 Special Topic15 credits
 

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161.391 Special Topic15 credits
 

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161.702 Theory of Linear Models15 credits
The derivation of the distributions and matrices arising from the linear models. The matrix theory approach will be presented geometrically and illustrated with numerical examples covering estimation, distribution theory, hypothesis testing, confidence intervals, analysis of variance and analysis of covariance.

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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.

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161.705 Advanced Statistical Inference15 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.

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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.

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161.721 Design and Analysis of Experiments15 credits
Traditional balanced, blocked and multistrata experiments. Recovery of inter-block information. Efficiency and more general, unbalanced blocking schemes.

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161.723 Theory of Multivariate Statistics15 credits
Real-life research problems in areas as diverse as archaeology and psychology often require the simultaneous measurement and analysis of a number of variables for their adequate description and resolution. This course develops the theory and methods of multivariate investigation. Emphasis will be placed on the practical aspects of the description and interpretation of pattern and structure in multivariate data.

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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.

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161.726 Extensions to the Linear Model15 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.

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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.

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161.742 Time Series Analysis15 credits
Principles and practical applications of univariate and multivariate time series analysis: stationarity, detrending, autocorrelation and partial autocorrelation; cross-correlation; linear filtering; spectral analysis; Fourier transform; periodogram; smoothing; peak significance; coherence; impulse-response functions; linear filtering; ARIMA and SARIMA modelling; model selection criteria; regression with correlated errors; multivariate regression; vector autoregressive models; transfer function models; econometric and financial modelling; state space models and the Kalman filter.

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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.

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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.

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161.749 Topics in Applied Probability15 credits
A topic in probabilistic modelling such as stochastic networks, dynamic stochastic systems, population theory.

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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.

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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.

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161.771 Analysis of Experiments for Researchers15 credits
Successful research in the natural and physical sciences requires the design, implementation and analysis of directed sampling programmes and experiments. This course covers the logic of scientific investigations, stratified random sampling, ANOVA designs, fixed and random factors, nested hierarchies, interactions, mixed models, inference spaces and estimation of variance components in a research context.

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161.772 Multivariate Analysis for Researchers15 credits
Research methods suitable for the analysis of data containing more than one variable. The fundamentals of ordination, clustering and testing methods for the analysis of several variables, with examples taken from a range of applications. Special emphasis will be placed on achieving a conceptual understanding of the methods in order to implement and interpret the outcomes of multivariate analyses in applied research.

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161.773 Regression for Researchers15 credits
Fitting simple and multiple regression models. Diagnostic plots. Inference, including analysis of variance. General linear models, including transformations, polynomials, models with categorical explanatory variables, interactions, weighted regression. Variable selection and multicollinearity. Extensions to nonlinear, logistic, and econometric regression models. A practical course using appropriate software, with illustrative examples taken from recent research literature.

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161.774 Time Series for Researchers15 credits
A practical approach to modelling and forecasting univariate and multivariate time series for non-specialists with illustrative examples taken from recent research literature. Topics selected from: ARIMA modelling; model selection criteria; spectral analysis; regression with correlated errors; ARCH and GARCH models; multivariate regression; vector autoregressive models; cointegration and error correction models; transfer function models; state space modelling; the Kalman filter.

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161.775 Sample Surveys15 credits
This course covers a broad range of situations in which sampling is used with emphasis placed on sample surveys. Topics include: stratification, clustering, multistage, unequal probabilities of selection. The effects of the design on the variance of estimates. Examples from recent research literature will be used to illustrate techniques.

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161.776 Statistical Modelling for Researchers15 credits
Advanced stochastic modelling techniques for applied research 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.

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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.

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161.778 Biostatistics for Researchers15 credits
Statistical techniques for the biological, medical and other life sciences. Case studies are used to demonstrate topics such as experimental design, multivariate methods; survival analysis, linear models with non-normal errors, and nonlinear regression. Emphasis is placed on application of appropriate statistical techniques through extensive use of statistical software.

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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.

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161.781 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.

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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.

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161.784 Industrial Statistics Project30 credits
A supervised industrially-based Statistical problem-solving project based in a client company culminating in the provision of expert advice via a project report.

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161.790 Special Topic15 credits
 

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161.791 Special Topic15 credits
 

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161.871 Thesis 90 Credit Part 145 credits
A supervised and guided independent study resulting in a published work.

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161.872 Thesis 90 Credit Part 245 credits
A supervised and guided independent study resulting in a published work.

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161.875 Thesis90 credits
A supervised and guided independent study resulting in a published work.

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161.891 Thesis 120 Credit Part 160 credits
A supervised and guided independent study resulting in a published work.

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161.892 Thesis 120 Credit Part 260 credits
A supervised and guided independent study resulting in a published work.

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161.893 Research Report60 credits
 

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161.895 Thesis120 credits
A supervised and guided independent study resulting in a published work.

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161.897 Thesis 120 Credit Part 160 credits
A supervised and guided independent study resulting in a published work.

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161.898 Thesis 120 Credit Part 260 credits
A supervised and guided independent study resulting in a published work.

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161.899 Thesis120 credits
A supervised and guided independent study resulting in a published work.

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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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