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  3. Advanced Elements of Analytics

Advanced Elements of Analytics (MAST90134)

Graduate courseworkPoints: 12.5Not available in 2019

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Overview

Year of offerNot available in 2019
Subject levelGraduate coursework
Subject codeMAST90134
FeesSubject EFTSL, Level, Discipline & Census Date

This subject equips students with the practical skills to apply regression methods to health data using the statistical packages R and Stata, as well as a major emphasis on the interpretation and communication of results. Topics covered include: analysis of continuous outcomes with linear regression; analysis of binary outcomes with logistic and tree-based regression methods; analysis of time-to-event outcomes with Cox and Poisson regression; fitting the aforementioned regression models in the statistical packages R and Stata; interpretation of the different measures of association estimated in each of the regression models; how to adjust for confounding and identify variables that modify measures of association using these regression methods; and purpose of regression modelling (causal vs. predictive).

Intended learning outcomes

  • Demonstrate practical skills when fitting regression models to data using statistical computing software (R and/or Stata)
  • Assess the suitability of a regression model with attention to checking the underlying assumptions
  • Describe and demonstrate how to adjust for confounding and identify variables that modify measures of association using these regression methods
  • Demonstrate the ability to interpret and effectively communicate (including visually) results of regression modelling

Generic skills

  • Independent problem solving
  • Facility with abstract reasoning
  • Clarity of written expression
  • Sound communication of technical concepts

Last updated: 22 January 2019