• Offered by Rsch Sch of Finance, Actuarial Studies & App Stats
  • ANU College ANU College of Business and Economics
  • Classification Transitional
  • Course subject Statistics
  • Areas of interest Actuarial Studies, Statistics
  • Academic career PGRD
  • Course convener
    • Ian McDermid
  • Mode of delivery In Person
  • Co-taught Course
  • Offered in Second Semester 2016
    See Future Offerings

This course is intended to introduce students to generalised linear modelling methods, with emphasis on, but not limited to, common methods for analyzing categorical data.  Topics covered include a review of multiple linear regression and the analysis of variance, log-linear models for contingency tables, logistic regression for binary response data, Poisson regression, model selection and model checking, mixed effects models and some additional topics to be agreed with students.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

Upon successful completion of the requirements for this course, students should have the
knowledge and skills to:

1. Explain in detail the role of generalised linear modelling techniques (GLMs) in modern applied statistics and implement methodology.
2. Demonstrate an in-depth understanding of the underlying assumptions for GLMs and perform diagnostic checks; appreciate potential problems.
3. Perform statistical analysis using statistical software in addition to learning the
underlying theory and methodologies.
4. Achieve a higher working-level understanding of the statistical techniques used
(beyond ideas and processes involved).

Other Information

See the course outline on the College courses page. Outlines are uploaded as they become available. 

Indicative Assessment

  • Mid semester exam (1 hour) 20%
  • Final exam (3 hours) 80%

The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.

Workload

Students taking this course are expected to commit at least 12 hours a week to completing the work.
This will include:
Attendance at 3 lectures per week and 1 tutorial plus an occasional additional topics lecture.
8 or more hours of private study including time spend reviewing the course materials and completing computing, tutorial work and assignments.

Requisite and Incompatibility

To enrol in this course you must have completed STAT6038 or be enrolled in the Master of Statistics

Specialisations

Fees

Tuition fees are for the academic year indicated at the top of the page.  

If you are a domestic graduate coursework or international student you will be required to pay tuition fees. Tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.

Student Contribution Band:
2
Unit value:
6 units

If you are an undergraduate student and have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). You can find your student contribution amount for each course at Fees.  Where there is a unit range displayed for this course, not all unit options below may be available.

Units EFTSL
6.00 0.12500
Domestic fee paying students
Year Fee
2016 $3480
International fee paying students
Year Fee
2016 $4638
Note: Please note that fee information is for current year only.

Offerings, Dates and Class Summary Links

The list of offerings for future years is indicative only.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.

Second Semester

Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary
7737 18 Jul 2016 29 Jul 2016 31 Aug 2016 28 Oct 2016 In Person N/A

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