# Search Course Outline

### Showing 25 course outlines from 3702 matches

2851

#### STATS 731

: Bayesian Inference2024 Semester Two (1245)

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

2852

#### 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

2853

#### STATS 731

: Bayesian Inference2022 Semester Two (1225)

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 210 or 225

2854

#### STATS 731

: Bayesian Inference2021 Semester Two (1215)

Prerequisite: STATS 210 or 225

2855

#### STATS 731

: Bayesian Inference2020 Semester One (1203)

Prerequisite: STATS 210 or 225

2856

#### STATS 732

: Foundations of Statistical Inference2024 Semester One (1243)

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

2857

#### 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

2858

#### STATS 732

: Foundations of Statistical Inference2022 Semester One (1223)

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

2859

#### STATS 732

: Foundations of Statistical Inference2021 Semester One (1213)

Prerequisite: STATS 210 or 225, and 15 points from MATHS 208, 250

Restriction: STATS 310

Restriction: STATS 310

2860

#### STATS 732

: Foundations of Statistical Inference2020 Semester One (1203)

Prerequisite: STATS 210 or 225, and 15 points from MATHS 208, 250

Restriction: STATS 310

Restriction: STATS 310

2861

#### STATS 740

: Sample Surveys2024 Semester One (1243)

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.

Prerequisite: 15 points from STATS 240, 330, 340, and 15 points from Stage II Mathematics

2862

#### STATS 740

: Sample Surveys2022 Semester Two (1225)

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.

Prerequisite: 15 points from STATS 240, 330, 340, and 15 points from Stage II Mathematics

2863

#### STATS 740

: Sample Surveys2021 Semester Two (1215)

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.

Prerequisite: 15 points from STATS 240, 330, 340, and 15 points from Stage II Mathematics

2864

#### STATS 740

: Sample Surveys2020 Semester Two (1205)

Prerequisite: 15 points from STATS 340, 741 and 15 points from STATS 310, 732

2865

#### STATS 747

: Statistical Methods in Marketing2021 Semester Two (1215)

Stochastic models of brand choice, applications of General Linear Models in marketing, conjoint analysis, advertising media models and marketing response models.

Prerequisite: 15 points from BIOSCI 209, STATS 201, 207, 208, 210, 707

2866

#### STATS 747

: Statistical Methods in Marketing2020 Semester Two (1205)

Stochastic models of brand choice, applications of General Linear Models in marketing, conjoint analysis, advertising media models and marketing response models.

No pre-requisites or restrictions

2867

#### STATS 760

: A Survey of Modern Applied Statistics2020 Semester One (1203)

A survey of techniques from modern applied statistics. Topics covered will be linear, non-linear and generalised linear models, modern regression including CART and neural networks, mixed models, survival analysis, time series and spatial statistics.

Prerequisite: STATS 310, 330

2868

#### STATS 762

: Regression for Data Science2024 Semester One (1243)

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: 15 points from STATS 210, 225, 707, and 15 points from ENGSCI 314, STATS 201, 207, 208

Restriction: STATS 330

Restriction: STATS 330

2869

#### 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

2870

#### STATS 762

: Regression for Data Science2022 Semester One (1223)

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

2871

#### STATS 762

: Regression for Data Science2021 Semester One (1213)

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

2872

#### STATS 762

: Regression for Data Science2020 Semester One (1203)

Restriction: STATS 330

2873

#### STATS 763

: Advanced Regression Methodology2024 Semester Two (1245)

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

2874

#### 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

2875

#### STATS 763

: Advanced Regression Methodology2022 Semester One (1223)

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

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