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Unit of study_
STAT4022: Linear and Mixed Models
2025 unit information
Classical linear models are widely used in science, business, economics and technology. This unit will introduce the fundamental concepts of analysis of data from both observational studies and experimental designs using linear methods, together with concepts of collection of data and design of experiments. You will first consider linear models and regression methods with diagnostics for checking appropriateness of models, looking briefly at robust regression methods. Then you will consider the design and analysis of experiments considering notions of replication, randomisation and ideas of factorial designs. Throughout the course you will use the R statistical package to give analyses and graphical displays. This unit includes material in STAT3022 Applied Linear Models, but has an additional component on the mathematical techniques underlying applied linear models together with proofs of distribution theory based on vector space methods.
Unit details and rules
Managing faculty or University school:
Science
| Study level | Undergraduate |
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| Academic unit | Mathematics and Statistics Academic Operations |
| Credit points | 6 |
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Prerequisites:
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An average mark of 65 or above in 12 credit points from (STAT2X11 or DATA2X02 or STAT3X23 or STAT3X21 or STAT3925 or STAT3888 or DATA3888) |
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Corequisites:
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None |
| Prohibitions:
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STAT3012 or STAT3912 or STAT3022 or STAT3922 or STAT3004 or STAT3904 |
| Assumed knowledge:
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Material in DATA2X02 or equivalent and MATH1002 or MATH1X61 or equivalent; that is, a knowledge of applied statistics and an introductory knowledge to linear algebra, including eigenvalues and eigenvectors |
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
- LO7. Derive and re-create proofs of theoretical aspects of linear models.
Unit availability
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