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 working knowledge of the R statistical computing language, particularly the graphical capabilities
- Fit Simple Linear regression models and interpret model parameters
- Summarise and analyse relationships between a response variable and a covariate
- Summarise and analyse relationships between a response variable and several covariates
- Assess and refine simple and multiple linear regression models based on diagnostic measures. Identify outlying and influential data points.
- Carry out model selection in a multiple linear regression modelling context.
- 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
- Typical assessment may include, but is not restricted to: assignments and a final exam. (null) [LO null]
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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.Inherent Requirements
Not applicable
Requisite and Incompatibility
Prescribed Texts
None Specified
Preliminary Reading
There is no prescribed text, however the course draws material from:- Faraway, Julian J. (2015) Linear Models with R, 2nd Edn, CRC/Chapman & Hall
- Chester Ismay and Albert Y. Kim. (2017) Modern Dive: An Introduction to Statistical andData Sciences via R. http : //moderndive.com
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.
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 |
Course fees
- Domestic fee paying students
| Year | Fee |
|---|---|
| 2019 | $3840 |
- International fee paying students
| Year | Fee |
|---|---|
| 2019 | $5460 |
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.
Second Semester
| Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|
| 9766 | 22 Jul 2019 | 29 Jul 2019 | 31 Aug 2019 | 25 Oct 2019 | In Person | View |
