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This subject introduces the theory underlying modern statistical inference and statistical computation. In particular, it demonstrates that many commonly used statistical procedures arise as applications of a common theory. Both classical and Bayesian statistical methods are developed. Basic statistical concepts including maximum likelihood, sufficiency, unbiased estimation, confidence intervals, hypothesis testing and significance levels are discussed. Applications include distribution free methods, goodness of fit tests, correlation and regression; the analysis of one-way and two-way classifications.
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 probability models to data by both estimating and testing hypotheses about model parameters.
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: 10 December 2019