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Showing 25 course outlines from 747 matches
576
STATS 330
: Statistical Modelling2023 Summer School (1230)
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.
Prerequisite: ENGSCI 314 or STATS 201 or 208
577
STATS 331
: Introduction to Bayesian Statistics2023 Semester Two (1235)
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.
Prerequisite: ENGSCI 314 or STATS 201 or 208
578
STATS 369
: Data Science Practice2023 Semester Two (1235)
Modern predictive modelling techniques, with application to realistically large data sets. Case studies will be drawn from business, industrial, and government applications.
Prerequisite: STATS 220 and STATS 210 or 225 and 15 points from ECON 221, STATS 201, 208, or ENGSCI 314
Restriction: STATS 765
Restriction: STATS 765
579
STATS 370
: Financial Mathematics2023 Semester Two (1235)
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.
Prerequisite: 15 points at Stage II in Mathematics and 15 points at Stage II in Statistics
Restriction: STATS 722
Restriction: STATS 722
580
STATS 380
: Statistical Computing2023 Semester Two (1235)
Statistical programming using the R computing environment. Data structures, numerical computing and graphics.
Prerequisite: 15 points from ENGSCI 314, STATS 201, 208, 220
581
STATS 380
: Statistical Computing2023 Semester One (1233)
Statistical programming using the R computing environment. Data structures, numerical computing and graphics.
Prerequisite: 15 points from ENGSCI 314, STATS 201, 208, 220
582
STATS 383
: The Science and Craft of Data Management2023 Semester Two (1235)
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.
Prerequisite: ENGSCI 314 or STATS 201 or 208, and COMPSCI 101 or ENGSCI 233 or STATS 220
583
STATS 399
: Capstone: Statistics in Action2023 Semester Two (1235)
Provides opportunities to integrate knowledge in statistics and data science, and collaborate with others through a succession of group projects and activities.
Prerequisite: 30 points at Stage III in Statistics
584
STATS 705
: Topics in Official Statistics2023 Semester Two (1235)
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.
No pre-requisites or restrictions
585
STATS 707
: Computational Introduction to Statistics2023 Semester One (1233)
An advanced introduction to statistics and data analysis, including testing, estimation, and linear regression.
Prerequisite: 15 points from STATS 101, 108 and 15 points from COMPSCI 101, MATHS 162
Restriction: BIOSCI 209, STATS 201, 207, 208, 210, 225
Restriction: BIOSCI 209, STATS 201, 207, 208, 210, 225
586
STATS 708
: Topics in Statistical Education2023 Semester One (1233)
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.
No pre-requisites or restrictions
587
STATS 720
: Stochastic Processes2023 Semester One (1233)
Continuous-time jump Markov processes. A selection of topics from: point processes, renewal theory, martingales, Brownian motion, Gaussian processes and inference for stochastic processes.
Prerequisite: STATS 320 or 325
588
STATS 721
: Foundations of Stochastic Processes2023 Semester Two (1235)
Fundamentals of stochastic processes. Topics include: generating functions, branching processes, Markov chains, and random walks.
Restriction: STATS 325
589
STATS 726
: Time Series2023 Semester Two (1235)
Stationary processes, modelling and estimation in the time domain, forecasting and spectral analysis.
Prerequisite: STATS 210, and 320 or 325
590
STATS 730
: Statistical Inference2023 Semester Two (1235)
Fundamentals of likelihood-based inference, including sufficiency, conditioning, likelihood principle, statistical paradoxes. Theory and practice of maximum likelihood. Examples covered may include survival analysis, GLM's, nonlinear models, random effects and empirical Bayes models, and quasi-likelihood.
Prerequisite: STATS 310 or 732
591
STATS 731
: Bayesian Inference2023 Semester Two (1235)
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.
Prerequisite: STATS 331 and 15 points from STATS 210, 225
592
STATS 732
: Foundations of Statistical Inference2023 Semester One (1233)
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.
Prerequisite: STATS 210 or 225, and 15 points from MATHS 208, 250
Restriction: STATS 310
Restriction: STATS 310
593
STATS 762
: Regression for Data Science2023 Semester One (1233)
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.
Prerequisite: STATS 707 or 210 or 225, and 15 points from STATS 201, 207, 208 or a B+ or higher in BIOSCI 209
Restriction: STATS 330
Restriction: STATS 330
594
STATS 763
: Advanced Regression Methodology2023 Semester Two (1235)
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.
Prerequisite: STATS 210 or 225, and 15 points from STATS 330, 762 and 15 points at Stage II in Mathematics
595
STATS 765
: Statistical Learning for Data Science2023 Semester One (1233)
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.
Prerequisite: 15 points from STATS 201 or 207 or 208 and 15 points from STATS 210 or 225, or STATS 707
Corequisite: May be taken with STATS 707
Restriction: STATS 369
Restriction: STATS 369
596
STATS 766
: Multivariate Analysis2023 Semester Two (1235)
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).
Prerequisite: STATS 310 or 732
597
STATS 767
: Foundations of Applied Multivariate Analysis2023 Semester One (1233)
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.
Prerequisite: 15 points from BIOSCI 209, STATS 201, 207, 208, 707
Restriction: STATS 302
Restriction: STATS 302
598
STATS 768
: Longitudinal Data Analysis2023 Semester Two (1235)
Exploration and regression modelling of longitudinal and clustered data, especially in the health sciences: mixed models, marginal models, dropout, causal inference.
Prerequisite: 15 points from BIOSCI 209, STATS 201, 207, 208, 210, 707
599
STATS 769
: Advanced Data Science Practice2023 Semester Two (1235)
Databases, SQL, scripting, distributed computation, other data technologies.
Prerequisite: 15 points from STATS 220, 369, 380 and 15 points from BIOSCI 209, STATS 201, 207, 208, 707
600
STATS 770
: Introduction to Medical Statistics2023 Semester One (1233)
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.
Prerequisite: 15 points from BIOSCI 209, STATS 201, 207, 208 and 15 points from STATS 210, 225, 707