- Code STAT2014
- Unit Value 6 units
- Offered by Rsch Sch of Finance, Actuarial Studies & App Stats
- ANU College ANU College of Business and Economics
- Course subject Statistics
- Areas of interest Actuarial Studies, Statistics
- Academic career UGRD
- Dr Dale Roberts
- Mode of delivery In Person
- Co-taught Course
Second Semester 2019
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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.
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.
- Typical assessment may include, but is not restricted to: assignments and a final exam. (null) [LO null]
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WorkloadStudents 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
Preliminary ReadingThere 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 KnowledgeThe 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.
Tuition fees are for the academic year indicated at the top of the page.
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- Student Contribution Band:
- 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.
- Domestic fee paying students
- International fee paying students
Offerings, Dates and Class Summary Links
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Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
|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|