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July 2018
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Statistical Modelling and Inference

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Description

Statistical methods are important to all areas that rely on data including science, technology, government and commerce. To deal with the complex problems that arise in practice requires a sound understanding of fundamental statistical principles together with a range of suitable modelling techniques. Computing using a high level statistical package is also an essential element of modern statistical practice. This course provides an introduction to the principles of statistical inference and the development of linear statistical models with the statistical package R. Topics covered are: Point estimates, unbiasedness, mean-squared error, confidence intervals, tests of hypotheses, power calculations, derivation of one and two-sample procedures; simple linear regression, regression diagnostics, prediction; linear models, ANOVA, multiple regression, factorial experiments, analysis of covariance models, model building; likelihood based methods for estimation and testing, goodness of fit tests; sample surveys, population means, totals and proportions, simple random samples, stratified random samples.


Objective

To provide an introduction to the principles of statistical inference and linear statistical models using the freeliy available statistical package R.


Content

Topics covered are: point estimates, unbiasedness, mean-squared error, confidence intervals, tests of hypotheses, power calculations, derivation of one and two-sample procedures: simple linear regression, regression diagnostics, prediction: linear models, analysis of variance (ANOVA), multiple regression, factorial experiments, analysis of covariance models, model building; likelihood-based methods for estimation and testing and goodness-of-fit tests.

 
YearSemesterLevelUnits
2013223
Patty Solomon
Lecturer for this course

Graduate attributes


Linkage future

This course is not recorded as prequisite for other courses.


Recommended text

None.