SPRING SUBJECTS

Linear Models
Statistical Inference
Applied Bayesian Methods

Overview

Various year 3, honours, and masters subjects are shared between the various sites supported by the ASEARC project. These subjects are made available to provide the best learning experiences in the topic and to provide students with a wider subject selection for a career in statistics. The sharing between universities is reflected by both the means of delivery, the access grid technology (interactive media) or by distance learning, and by the sharing of content, where the university having just delivered the subject updates the notes and passes the updated notes to another university for the next session, and the exchange perpetuates.

These subjects from other universities are made available to students in addition to the normal core units. And year four and honors students may take these subjects as electives. Regardless, it is essential that students liaise with thier course coordinator concerning subjects to be taken.

What are they?

Linear & Generalised Linear Models

Year 3 subject, delivered by each University to students in that University, where the notes have been updated by another university.

AND

Post graduate subject, delivered similar to STAT332, but with additional content.

This subject considers how to investigate relationships between variables arising from observational studies and designed experiments. Topics include: Model fitting as an approach to statistical analysis; Exponential family of distributions; Maximum likelihood estimation; Inference methods based on model fitting; Models for multiple linear regression, estimation and analysis, diagnostics and model selection; Generalised linear models for categorical data: logistic regression for nominal and ordinal data, Poisson regression and log-linear models; Additive models.

This subject is identified as STAT3010 in the University of Newcastle handbook and as STAT332 and STAT921 (post grad) in the University of Wollongong handbook.

Statistical Inference

Year 3 subject STAT332, delivered by each University to students in that University, where the notes have been updated by another university.

AND

Post graduate subject, delivered similar to STAT332, but with additional content.

This subject considers how to make inferences about unknown quantities from observed data. Topics covered include:

• Estimation methods: maximum likelihood and minimum variance unbiased estimation
• Hypothesis Testing; likelihood ratio, score and Wald tests,
• Evaluating tests
• Monte Carlo Simulation methods for inference
• Randomisation tests
• Monte Carlo Markov Chain
• Jackknife methods
• Bootstrap methods

This subject is identified as STAT3010 in the University of Newcastle handbook and as STAT333 and STAT922 (post grad) in the University of Wollongong handbook.

Applied Bayesian Methods

Year 3 subject, delivered from the University of Newcastle to other universities using access grid room technology.

This subject introduces students to Bayesian thinking and methods from an applied point of view; covering the use of prior information, Bayes rule and inference in standard situations such as proportions, means and relationships between variables. An applied view on Markov chain Monte Carlo methods will also be given. These methods are becoming popular among applied statisticians and analysts from disciplines such as, Economics, Quantitative finance, Health, Environmental science, Engineering and other applied areas, especially because prior information can be incorporated directly into analyses in a sensible way.This subject is identified as STAT3120 in the University of Newcastle handbook and as STAT373 in the University of Wollongong handbook.Assumed knowledge for enrolling to this subject is completion of the UoN STAT2010 subject or completion of the second year inference course, as well as an appreciation of regression and ANOVA models from a Bayesian perspective.

The Access Grid (AGR)

The AGR technology has been described as video-conferencing on steroids. The lecturer in one location (say Wollongong) will have a class of local students as well as a "virtual" classroom in another location (say Newcastle). While students in Wollongong have the usual visual and audio interaction with the lecturer, students in Newcastle will be able to view the lecturer on a big screen, hear what is being said, and instantly view notations on a whiteboard in their room as the notations are written on a whiteboard in Wollongong. Likewise, the lecturer and students in Wollongong will be able to hear and see the students in Newcastle and receive any notes made on whiteboard in Newcastle. This may seem a little strange at first but the technology aims to simulate the usual classroom experience and students in previous classes seemed to adapt very quickly.

For further description on the AGR, please refer to the information on how the subjects will be run, please refer to the "Subjects delivered by interactive media" page.

SPRING SUBJECTS

Linear Models
Statistical Inference
Applied Bayesian Methods

Overview

Various year 3, honours, and masters subjects are shared between the various sites supported by the ASEARC project. These subjects are made available to provide the best learning experiences in the topic and to provide students with a wider subject selection for a career in statistics. The sharing between universities is reflected by both the means of delivery, the access grid technology (interactive media) or by distance learning, and by the sharing of content, where the university having just delivered the subject updates the notes and passes the updated notes to another university for the next session, and the exchange perpetuates.

These subjects from other universities are made available to students in addition to the normal core units. And year four and honors students may take these subjects as electives. Regardless, it is essential that students liaise with thier course coordinator concerning subjects to be taken.

What are they?

Linear & Generalised Linear Models

Year 3 subject, delivered by each University to students in that University, where the notes have been updated by another university.

AND

Post graduate subject, delivered similar to STAT332, but with additional content.

This subject considers how to investigate relationships between variables arising from observational studies and designed experiments. Topics include: Model fitting as an approach to statistical analysis; Exponential family of distributions; Maximum likelihood estimation; Inference methods based on model fitting; Models for multiple linear regression, estimation and analysis, diagnostics and model selection; Generalised linear models for categorical data: logistic regression for nominal and ordinal data, Poisson regression and log-linear models; Additive models.

This subject is identified as STAT3010 in the University of Newcastle handbook and as STAT332 and STAT921 (post grad) in the University of Wollongong handbook.

Statistical Inference

Year 3 subject STAT332, delivered by each University to students in that University, where the notes have been updated by another university.

AND

Post graduate subject, delivered similar to STAT332, but with additional content.

This subject considers how to make inferences about unknown quantities from observed data. Topics covered include:

• Estimation methods: maximum likelihood and minimum variance unbiased estimation
• Hypothesis Testing; likelihood ratio, score and Wald tests,
• Evaluating tests
• Monte Carlo Simulation methods for inference
• Randomisation tests
• Monte Carlo Markov Chain
• Jackknife methods
• Bootstrap methods

This subject is identified as STAT3010 in the University of Newcastle handbook and as STAT333 and STAT922 (post grad) in the University of Wollongong handbook.

Applied Bayesian Methods

Year 3 subject, delivered from the University of Newcastle to other universities using access grid room technology.

This subject introduces students to Bayesian thinking and methods from an applied point of view; covering the use of prior information, Bayes rule and inference in standard situations such as proportions, means and relationships between variables. An applied view on Markov chain Monte Carlo methods will also be given. These methods are becoming popular among applied statisticians and analysts from disciplines such as, Economics, Quantitative finance, Health, Environmental science, Engineering and other applied areas, especially because prior information can be incorporated directly into analyses in a sensible way.This subject is identified as STAT3120 in the University of Newcastle handbook and as STAT373 in the University of Wollongong handbook.Assumed knowledge for enrolling to this subject is completion of the UoN STAT2010 subject or completion of the second year inference course, as well as an appreciation of regression and ANOVA models from a Bayesian perspective.

The Access Grid (AGR)

The AGR technology has been described as video-conferencing on steroids. The lecturer in one location (say Wollongong) will have a class of local students as well as a "virtual" classroom in another location (say Newcastle). While students in Wollongong have the usual visual and audio interaction with the lecturer, students in Newcastle will be able to view the lecturer on a big screen, hear what is being said, and instantly view notations on a whiteboard in their room as the notations are written on a whiteboard in Wollongong. Likewise, the lecturer and students in Wollongong will be able to hear and see the students in Newcastle and receive any notes made on whiteboard in Newcastle. This may seem a little strange at first but the technology aims to simulate the usual classroom experience and students in previous classes seemed to adapt very quickly.

For further description on the AGR, please refer to the information on how the subjects will be run, please refer to the "Subjects delivered by interactive media" page.