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To provide a practical introduction to the fundamentals of linear regression modelling, with emphasis on application to real data and problems.
This course is of interest to anyone majoring in statistics and forestry, as well as students from other disciplines (eg. biology, commerce, etc) who want to increase the breadth of their statistical knowledge base.Regression models are the most widely used statistical tools for examining the relationships among variables. We cover the core concepts in regression modelling, with an emphasis on problem solving as applied to real data. We use R, one of the mostly widely used statistical packages, but no prior knowledge of R is assumed.
On completing this course you will* be able to analyze data using simple and multiple regression models as well as logistic regression* understand the relationship between regression and ANOVA* understand diagnostics for testing modelling assumptions* understand methods for model selection* be able to interpret computer output, and be able to write reports that analyse data and interpret computer output
This course will provide students with an opportunity to develop the Graduate Attributes specified below:
Critically competent in a core academic discipline of their award
Students know and can critically evaluate and, where applicable, apply this knowledge to topics/issues within their majoring subject.
STAT202, FORE210, STAT220, STAT224
Students must attend one activity from each section.
This course is co-coded with STAT202. Students must register for tutorial sessions which commence on the second Monday of the course. There is a direct link from your personal Learn page for the course to the student entry point for My Timetable that enables you to self-allocate into one of three lab streams.
Domestic fee $937.00
International fee $4,363.00
* Fees include New Zealand GST and do not include any programme level discount or additional course related expenses.
This course will not be offered if fewer than 5 people apply to enrol.
For further information see
School of Forestry.