STATS 330 : Statistical Modelling
2022 Semester Two (1225) (15 POINTS)
STATS 330 further develops ideas introduced in STATS 201/208, giving a synthesis and broader understanding of generalised linear models and related methods. Simulation-based procedures, including bootstrapping and cross-validation, are introduced as a means to provide robust inference, to investigate consequences of assumption violations, and to solve goodness-of-fit and model-selection problems. Particular focus is placed on how the modelling procedure varies depending on whether the analysis aims to explain an underlying process or predict future observations. Students will learn to implement all methods taught in R, the widely used, open-source software environment for statistical computing. Emphasis is on practical application, providing students with a versatile statistical toolbox useful for a range of fields in both academia and industry, including almost all subjects in business and economics, along with any experimental or social science. It is also a useful complement to computer science.
Capabilities Developed in this Course
|Capability 1:||Disciplinary Knowledge and Practice|
|Capability 2:||Critical Thinking|
|Capability 3:||Solution Seeking|
|Capability 4:||Communication and Engagement|
|Capability 5:||Independence and Integrity|
- Define the generalised linear model and describe its underlying assumptions (Capability 1)
- Explain and use a variety of statistical tools and procedures that determine the appropriateness of a fitted statistical model (Capability 1)
- Summarise an appropriate modelling procedure, outlining how this is driven by the aims of the analysis (Capability 1)
- Identify an appropriate candidate model to fit to a particular data set that is capable of answering the questions of interest (Capability 3)
- Evaluate the appropriateness of a fitted statistical model, and take sensible steps to improve a model that is found to be inappropriate (Capability 2 and 3)
- Write their own R code to carry out each step of the modelling procedure (Capability 1 and 5)
- Communicate the findings of an analysis accurately and concisely (Capability 4)
|Final Exam||50%||Individual Examination|
|Assessment Type||Learning Outcome Addressed|
Generalised Additive Models
This course is a standard 15 point course and students are expected to spend 150 hours per semester involved in each 15 point course that they are enrolled in.
For this course, you can expect 3 hours of lectures/week, an optional weekly 2 hour tutorial, and approximately 5 hours of reading and thinking about the content for assignments and/or test/exam preparation.
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Attendance on campus is required for the exam.
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This course is also available for remote students.
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Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.
In this course students may be asked to submit coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. In exceptional circumstances changes to elements of this course may be necessary at short notice. Students enrolled in this course will be informed of any such changes and the reasons for them, as soon as possible, through Canvas.