Statistical Modelling (MAST90084)

Graduate courseworkPoints: 12.5On Campus (Parkville)

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Overview

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Semester 1
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Statistical models are central to applications of statistics and their development motivates new statistical theories and methodologies. Commencing with a review of linear and generalized linear models, analysis of variance and experimental design, the theory of linear mixed models is developed and model selection techniques are introduced. Approaches to non and semiparametric inference, including generalized additive models, are considered. Specific applications may include longitudinal data, survival analysis and time series modelling.

Intended learning outcomes

Upon completion of this subject, students should be able to:

  • Explain the principles of statistical modelling;
  • Apply the learned modelling techniques to real data arising from important applications;
  • Relate the learned modelling techniques to other areas of studies in statistics.

Generic skills

In addition to learning specific skills that will assist students in their future careers in science, they will have the opportunity to develop generic skills that will assist them in any future career path. These include:

  • problem-solving skills: the ability to engage with unfamiliar problems and identify relevant solution strategies;
  • analytical skills: the ability to construct and express logical arguments and to work in abstract or general terms to increase the clarity and efficiency of analysis;
  • collaborative skills: the ability to work in a team;
  • time-management skills: the ability to meet regular deadlines while balancing competing commitments

Last updated: 4 March 2025