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.
Objective
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, 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.
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| Year |
Semester |
Level |
Units |
| 2012 |
2 |
2 |
3 |
Delivery
42 hours of lectures, tutorials and practicals
Assessment
Ongoing assessment 30%, exam 70%.
Graduate attributes
Linkage past
No past linkages have been noted.
Linkage present
No present linkages have been noted.
Linkage future
This course is not recorded as prequisite for other courses.
Restrictions
None.
Recommended text
None.
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