This is a course in applied statistics that studies the use of regression techniques for examining relationships between variables. Ordinary linear models and generalised linear models are covered. The course emphasizes the principles of statistical modelling through the iterative process of fitting a model, examining the fit to assess imperfections in the model and suggest alternative models, and continuing until a satisfactory model is reached. Both steps in this process require the use of a computer: model fitting uses various numerical algorithms, and model assessment involves extensive use of graphical displays. The R statistical computing package is used as an integral part of the course.
Learning Outcomes
Upon successful completion, students will have the knowledge and skills to:
- Demonstrate a thorough understanding of the R statistical computing language, particularly the graphical capabilities;
- Fit simple linear regression models, interpret model parameters and relate these back to the underlying research question;
- Analyse and interpret relationships between a response variable and a covariate;
- Analyse and interpret relationships between a response variable and several covariates;
- Assess and refine simple and multiple linear regression models based on diagnostic measures, including identifying and discuss the implications of outlying and influential data points;
- Explore and discuss a useful multiple linear regression model from a number of competing models; and,
- Define and describe the features of a Generalised Linear Model (GLM), fit GLM models, assess and refine the models based on diagnostic measures, and interpret model output.
Indicative Assessment
- The research-based assessment will consist of a nested project. (40) [LO 1,2,3,4,5,6,7]
- The other assessment may include but is not restricted to exams, quizzes, presentations and other assessment tasks as appropriate. (60) [LO 1,2,3,4,5,6,7]
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Workload
Students are expected to commit 130 hours of work in completing this course. This includes time spent in scheduled classes and self-directed study time.
Inherent Requirements
Not applicable
Requisite and Incompatibility
Prescribed Texts
Information about the prescribed textbook will be available via the Class Summary
Assumed Knowledge
The course uses the R statistical package, which uses matrix algebra to implement the regression modelling techniques. An understanding of matrix algebra (equivalent to an introductory mathematics course such as MATH1113) would be helpful in understanding how the R routines work, but such knowledge is not a required prerequisite.Fees
Tuition fees are for the academic year indicated at the top of the page.
Commonwealth Support (CSP) Students
If you 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). More information about your student contribution amount for each course at Fees.
- Student Contribution Band:
- 1
- Unit value:
- 6 units
If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found 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 |
Course fees
- Domestic fee paying students
| Year | Fee |
|---|---|
| 2024 | $4440 |
- International fee paying students
| Year | Fee |
|---|---|
| 2024 | $6360 |
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.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
First Semester
| Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|
| 2742 | 19 Feb 2024 | 26 Feb 2024 | 05 Apr 2024 | 24 May 2024 | In Person | View |
Second Semester
| Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|
| 7728 | 22 Jul 2024 | 29 Jul 2024 | 31 Aug 2024 | 25 Oct 2024 | In Person | N/A |
