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Elements of Statistics (MAST90058)

Graduate courseworkPoints: 12.5On Campus (Parkville)

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Overview

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Semester 2
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The analysis of data arising in Bioinformatics and Biostatistics requires the use of sophisticated statistical techniques and computing packages. This subject introduces the basic elements of statistical modelling, computation and data analysis. Students will develop the ability to fit statistical models to data, estimate parameters of interest and test hypotheses. Both classical and Bayesian approaches will be covered. The importance of the underlying mathematical theory of statistics and the use of modern statistical software will be emphasised.

Concepts covered include: descriptive statistics, random sample, statistical inference, point estimation, interval estimation, properties of estimators, maximum likelihood, confidence intervals, hypothesis testing, Bayesian inference. Applications covered include: exploratory data analysis, inference for samples from univariate distributions, simple linear regression, correlation, goodness-of-fit tests, analysis of variance.

The lectures in this subject are co-taught with MAST20005 Statistics; the practice classes are separate.

Intended learning outcomes

Students completing this subject should:



  • Be familiar with the basic ideas of estimation and hypothesis testing
  • Be able to carry out many standard statistical procedures using a statistical computing package
  • Develop the ability to fit statistical models to data by both estimating and testing hypotheses about model parameters.

Generic skills

In addition to learning specific skills that will assist students in their future careers in science, they should progressively acquire generic skills from this subject 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
  • Computer skills: the ability to use statistical computing packages.

 

Last updated: 7 March 2023