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STAT319 is a course in Generalised Linear Models (GLM), suited to anyone with an interest in analysing data. In this course we introduce the components of GLM and other advanced data analysis techniques. We use the free-ware package R. R is becoming the preferred computer package for many statisticians. In this course we will show you how to use the package, enter, manipulate and analyse data in R.
STAT319 is a course in generalised linear models (glms), a very useful and frequently used class of models for practical data analysis. In this course you will earn about the components of glms and related advanced data analysis techniques. We will first review the analysis of data from continuous distributions and then extend this to models for binomial response data, models for count response data, and models for multinomial data (logistic regression, Poisson regression, and log-linear models). These models are then further extended to generalised linear mixed models (glmms), and to generalised additive models (gams). We will also consider data management and visualisation. In this course you will use R, a very widely used open-source statistical software environment. The course is suited to anyone with an interest in practical data analysis.
The course will:introduce generalised linear modelsintroduce advanced data analysis techniques including mixed effects models, repeated measures and additive modelsintroduce the use of the statistical open-source software Rgive you experience in writing scientific and technical reports.You will be able to:describe and conduct appropriate statistical modeling techniques for a wide range of datasetsinterpret model results in such a way that a non-user of statistics can understanduse R competentlywrite a scientific and technical report.
30 points from STAT200-299
Students must attend one activity from each section.
Domestic fee $788.00
International fee $4,438.00
* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.
For further information see
Mathematics and Statistics.