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Unit of study_
STAT3022: Applied Linear Models
2025 unit information
Linear models are core to a wide range of real-world data analyses, for example in agriculture, health, sport and business. This unit provides an in-depth exploration of various linear models outlining when they can be applied, and how to assess if they are appropriate. The unit will introduce the fundamental concepts of analysis of data from both observational studies and experimental designs using classical linear methods, together with concepts of collection of data and design of experiments. You will consider linear models and robust regression methods with diagnostics for checking appropriateness of models and strategies for performing feature selection. You will learn to design and analyse experiments considering notions of replication, randomisation and ideas of factorial designs. You will apply, construct and interpret multi-way ANOVA models and make inferences, including post-hoc tests and making corrections for multiple comparisons. Throughout the unit you will use the R statistical package to perform analyses and generate statistical graphics. By completing this unit you will learn how to generate, interpret, visualise and critique linear models.
Unit details and rules
Managing faculty or University school:
Science
| Study level | Undergraduate |
|---|---|
| Academic unit | Mathematics and Statistics Academic Operations |
| Credit points | 6 |
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Prerequisites:
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STAT2X11 and (DATA2X02 or STAT2X12) |
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Corequisites:
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None |
| Prohibitions:
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STAT3912 or STAT3012 or STAT3922 or STAT4022 |
| Assumed knowledge:
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Introductory knowledge to linear algebra (MATH1X02 or equivalent) |
At the completion of this unit, you should be able to:
- LO1. Apply and formulate appropriate linear models to analyse data from diverse settings, including cross-sectional studies, experiments, panel studies
- LO2. Apply, analyse, and implement least square estimators for parameters in linear models
- LO3. Calculate and interpret interval estimators, including confidence intervals and prediction intervals, for appropriate quantities in linear models
- LO4. Conduct appropriate hypothesis testings for parameters in linear models
- LO5. Conduct appropriate diagnostics to check model assumptions and detect unusual observations
- LO6. Apply, implement, and interpret the results from likelihood-based methods to fit linear mixed effects models
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