A first exposure to statistics that builds data handling and literacy skills and develops conceptual thinking through active participation in problems using real data, computer simulations and group work. STATS 100 makes full use of appropriate technology and prepares students to use statistics in their own disciplines.
Intended for anyone who will ever have to collect or make sense of data, either in their career or private life. Steps involved in conducting a statistical investigation are studied with the main emphasis being on data analysis and the background concepts necessary for successfully analysing data, extrapolating from patterns in data to more generally applicable conclusions and communicating results to others. Other topics include probability; confidence intervals, statistical significance, t-tests, and p-values; nonparametric methods; one-way analysis of variance, simple linear regression, correlation, tables of counts and the chi-square test.
The standard Stage I Statistics course for the Faculty of Business and Economics or for Arts students taking Economics courses. Its syllabus is as for STATS 101, but it places more emphasis on examples from commerce.
Probability, conditional probability, Bayes theorem, random walks, Markov chains, probability models. Illustrations will be drawn from a wide variety of applications including: finance and economics; biology; telecommunications, networks; games, gambling and risk.
Examines the uses, limitations and abuses of statistical information in a variety of activities such as polling, public health, sport, law, marketing and the environment. The statistical concepts and thinking underlying data-based arguments will be explored. Emphasises the interpretation and critical evaluation of statistically based reports as well as the construction of statistically sound arguments and reports. Some course material will be drawn from topics currently in the news.
A practical course using the R language in the statistical analysis of data and the interpretation and communication of statistical findings. Includes exploratory data analysis, analysis of linear models including multiple regression and analysis of variance, generalised linear models including logistic regression and analysis of counts, time series analysis.
A practical course using the popular R language in the statistical analysis of data and the interpretation and communication of statistical findings. Includes exploratory data analysis, analysis of linear models including multiple regression and analysis of variance, generalised linear models including logistic regression and analysis of counts, time series analysis.
Probability, discrete and continuous distributions, likelihood and estimation, hypothesis testing.
Explores the processes of data acquisition, data storage and data processing using current computer technologies. Students will gain experience with and understanding of the processes of data acquisition, storage, retrieval, manipulation, and management. Students will also gain experience with and understanding of the computer technologies that perform these processes.
Covers the fundamentals of probability through theory, methods, and applications. Topics should include the classical limit theorems of probability and statistics known as the laws of large numbers and central limit theorem, conditional expectation as a random variable, the use of generating function techniques, and key properties of some fundamental stochastic models such as random walks, branching processes and Poisson point processes.
An introduction to research study design and the analysis of structured data. Blocking, randomisation, and replication in designed experiments. Clusters, stratification, and weighting in samples. Other examples of structured data.
Explores methods for using data to assist in decision making in business and industrial applications. Software packages will be used to solve practical problems. Topics such as linear programming, transportation and assignment models, network algorithms, queues, Markov chains, inventory models, simulation, analytics and visualisation will be considered.
Covers the exploratory analysis of multivariate data, with emphasis on the use of statistical software and reporting of results. Topics covered include: techniques for data display, dimension reduction and ordination, cluster analysis, multivariate ANOVA and associated methods.
Estimation, likelihood methods, hypothesis testing, multivariate distributions, linear models.
Characterisations of and relations between different kinds of random objects including random functions, random paths and random trees. Modes of convergence; the Law of Large Numbers and Central Limit Theorem.
Construction, analysis and simulation of stochastic models, and optimisation problems associated with them. Poisson process, Markov chains, continuous-time Markov processes. Equilibrium distribution, reaching probabilities and times, transient behaviour. Use of R to simulate simple stochastic processes. Examples drawn from a range of applications including operations research, biology, and finance.
Introduction to stochastic processes, including generating functions, branching processes, Markov chains, random walks.
Components, decompositions, smoothing and filtering, modelling and forecasting. Examples and techniques from a variety of application areas.
Application of the generalised linear model and extensions to fit data arising from a range of sources including multiple regression models, logistic regression models, and log- linear models. The graphical exploration of data.
Introduces Bayesian data analysis using the WinBUGS software package and R. Topics include the Bayesian paradigm, hypothesis testing, point and interval estimates, graphical models, simulation and Bayesian inference, diagnosing MCMC, model checking and selection, ANOVA, regression, GLMs, hierarchical models and time series. Classical and Bayesian methods and interpretations are compared.
Modern predictive modelling techniques, with application to realistically large data sets. Case studies will be drawn from business, industrial, and government applications.
Mean-variance portfolio theory; options, arbitrage and put-call relationships; introduction of binomial and Black-Scholes option pricing models; compound interest, annuities, capital redemption policies, valuation of securities, sinking funds; varying rates of interest, taxation; duration and immunisation; introduction to life annuities and life insurance mathematics.
Statistical programming using the R computing environment. Data structures, numerical computing and graphics.
A structured introduction to the science and craft of data management, including: data representations and their advantages and disadvantages; workflow and data governance; combining and splitting data sets; data cleaning; the creation of non-trivial summary variables; and the handling of missing data. These will be illustrated by data sets of varying size and complexity, and students will implement data processing steps in at least two software systems.
Directed study on a topic from Data Science, Statistics or Probability approved by the Academic Head or nominee.
Provides opportunities to integrate knowledge in statistics and data science, and collaborate with others through a succession of group projects and activities.
A continuation of STATS 301, with more in-depth coverage of programming in the SAS language. Topics covered will include advanced use of the SAS language, advanced data step programming, macros, input and output, connectivity to other software platforms, SAS SQL.
Official statistics, data access, data quality, demographic and health statistics, other social statistics, economic statistics, analysis and presentation, case studies in the use of official statistics.
An advanced introduction to statistics and data analysis, including testing, estimation, and linear regression.
Covers a wide range of research in statistics education at the school and tertiary level. There will be a consideration of, and an examination of, the issues involved in statistics education in the curriculum, teaching, learning, technology and assessment areas.
Predictive modelling forecasts likely future outcomes based on historical and current data. Following an advanced introduction to statistics and data analysis, the course will discuss concepts for modern predictive modelling and machine learning.
Fundamental ideas in probability theory; sigma-fields, laws of large numbers, characteristic functions, the Central Limit Theorem, modes of convergence. Advanced topics may include Poisson random measures, random trees, Lévy processes, random spatial models. Students will undertake assigned individual research projects based on a journal article or advanced textbook, including a detailed explanation of the techniques of probability theory exemplified therein.
Stochastic models and their applications. Discrete and continuous-time jump Markov processes. A selection of topics from point processes, renewal theory, Markov decision processes, stochastic networks, inference for stochastic processes, simulation of stochastic processes, and computational methods using R.
Fundamentals of stochastic processes. Topics include: generating functions, branching processes, Markov chains, and random walks.
Fundamentals of financial mathematics. Topics include: mean-variance portfolio theory; options, arbitrage and put-call relationships; introduction of binomial and Black-Scholes option pricing models; compound interest, annuities, capital redemption policies, valuation of securities, sinking funds; varying rates of interest, taxation; duration and immunisation; introduction to life annuities and life insurance mathematics.
Stationary processes, modelling and estimation in the time domain, forecasting and spectral analysis.
Fundamentals of applied time series analysis. Topics include: components, decompositions, smoothing and filtering, modelling and forecasting. Examples and techniques from a variety of application areas are presented.
Fundamental topics in estimation and statistical inference. Advanced topics in modelling including regression with dependent data, survival analysis, methods to handle missing data. Advanced topics in current statistical practice researched by students. Students will undertake and present individual research projects on assigned topics, consisting in a literature search and a computational application to a data analysis task.
A course in practical Bayesian statistical inference covering: the Bayesian approach specification of prior distributions, decision-theoretic foundations, the likelihood principle, asymptotic approximations, simulation methods, Markov Chain Monte Carlo methods, the BUGS and CODA software, model assessment, hierarchical models, application in data analysis.
Fundamentals of statistical inference including estimation, hypothesis testing, likelihood methods, multivariate distributions, joint, marginal, and conditional distributions, vector random variables, and an introduction to decision theory and Bayesian inference.
The design, management and analysis of sample surveys. Topics such as the following are studied. Types of Survey. Revision of statistical aspects of sampling. Preparing surveys. Research entry: problem selection, sponsorship and collaboration. Research design: methodology and data collection; Issues of sample design and sample selection. Conducting surveys: Questionnaires and questions; Non- sampling issues; Project management; Maintaining data quality. Concluding surveys: Analysis; Dissemination.
Design, implementation and analysis of sample surveys and of experiments. This course covers the foundations of both areas.
Stochastic models of brand choice, applications of General Linear Models in marketing, conjoint analysis, advertising media models and marketing response models.
The design and analysis of data from experiments involving factorial and related designs and designs which have the property known as general balance (this includes most of the standard designs), and more general designs with blocking and replication. Response surface methodology. Sequential experimentation.
Linear mixed effect models for the analysis of data from small experiments, particularly those cases where the data are unbalanced. Methods include restricted maximum likelihood for the estimation of variance components.
Application of the generalised linear model to fit data arising from a wide range of sources, including multiple linear regression models, Poisson regression, and logistic regression models. The graphical exploration of data. Model building for prediction and for causal inference. Other regression models such as quantile regression. A basic understanding of vector spaces, matrix algebra and calculus will be assumed.
Generalised linear models, generalised additive models, survival analysis. Smoothing and semiparametric regression. Marginal and conditional models for correlated data. Model selection for prediction and for control of confounding. Model criticism and testing. Computational methods for model fitting, including Bayesian approaches.
Concepts of modern predictive modelling and machine learning such as loss functions, overfitting, generalisation, regularisation, sparsity. Techniques including regression, recursive partitioning, boosting, neural networks. Application to real data sets from a variety of sources, including data quality assessment, data preparation and reporting.
A selection of topics from multivariate analysis, including: advanced methods of data display (e.g., Correspondence and Canonical Correspondence Analysis, Biplots, and PREFMAP) and an introduction to classification methods (e.g., various types of Discriminant Function Analysis).
Fundamentals of exploratory analysis of multivariate data, with emphasis on the use of statistical software and reporting of results. Topics covered include: techniques for data display, dimension reduction and ordination, cluster analysis, multivariate ANOVA and associated methods.
Exploration and regression modelling of longitudinal and clustered data, especially in the health sciences: mixed models, marginal models, dropout, causal inference.
Databases, SQL, scripting, distributed computation, other data technologies.
An introduction to ideas of importance in medical statistics, such as measures of risk, basic types of medical study, causation, ethical issues and censoring, together with a review of common methodologies.
The theory and practice of clinical trials, including: design issues, data management, common analysis methodologies, intention to treat, compliance, interim analyses and ethical considerations.
Fundamentals of the statistical methods that underly capture-recapture, distance sampling and occupancy analysis, focusing on the critical role that p, the probability of detection, plays in estimating n, the number of animals, or psi, the probability of species presence. Extensions to these fundamental tools including spatially explicit, genetic, and hierarchical methods will be covered.
Statistical software, data management, data integrity, data transfer, file processing, symbolic manipulation, document design and presentation, oral presentation, professional ethics.
Students will learn about the practicalities of statistical consulting. Students will carry out a statistical consulting project, including the writing of a report, under the supervision of a member of the academic staff.
Professional skills, advanced statistical programming, numerical computation and graphics.
A practical introduction to modern simulation and Monte Carlo techniques and their use to simulate real situations and to solve difficult statistical inferential problems whose mathematical analysis is intractable.
Data cleaning, missing values, data warehouses, security, fraud detection, meta-analysis, and statistical techniques for data mining such as regression and decision trees, modern and semiparametric regression, neural networks, statistical approaches to the classification problem.
SAS statistical software with an emphasis on using SAS as a programming language for purposes of database manipulation, simulation, statistical modelling and other computer-intensive methods.
Delivers a comprehensive understanding of widely used time series forecasting methods, illustrates how to build models to uncover the structure in time series and perform model diagnostics to assess the fit of models, and develops analytical and computer skills that are necessary for analysing time series data. Familiarity with coding in R is recommended.
Effective visual presentations of data. Topics may include: how to present different types of data; human perception; graphics formats; statistical graphics in R; interactive graphics; visualising high-dimensional data; visualising large data.