• Offered by Rsch Sch of Finance, Actuarial Studies & App Stats
  • ANU College ANU College of Business and Economics
  • Course subject Statistics
  • Areas of interest Statistics
  • Academic career UGRD
  • Course convener
    • Ian McDermid
  • Mode of delivery In Person
  • Offered in Second Semester 2019
    See Future Offerings

Course Description: 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. 

The R statistical computing package is used as an integral part of the course.

For students enrolled in STAT7030, there may be alternative assignment or examination problems.

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. Communicate the role of generalised linear modelling techniques (GLMs) in modern applied statistics and implement methodology.
  2. Explain 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.

Other Information

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

Indicative Assessment

Typical assessment may include, but is not restricted to: assignments and a final exam.

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 are expected to commit at least 10 hours per week to completing the work in this course. This will include at least 3 contact hours per week and up to 7 hours of private study time.

Requisite and Incompatibility

To enrol in this course you must have completed STAT2008 or STAT2014.

Majors

Minors

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
2019 $3840
International fee paying students
Year Fee
2019 $5460
Note: Please note that fee information is for current year only.

Offerings, Dates and Class Summary Links

ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.

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
7470 22 Jul 2019 29 Jul 2019 31 Aug 2019 25 Oct 2019 In Person View

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