STAT446-20S1 (C) Semester One 2020

Generalised Linear Models

15 points

Details:
Start Date: Monday, 17 February 2020
End Date: Sunday, 21 June 2020
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Friday, 28 February 2020
  • Without academic penalty (including no fee refund): Friday, 29 May 2020

Description

Generalised Linear Models

STAT446 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.


For a full list of Honours courses, please refer to the School of Mathematics and Statistics Honours Booklet Mathematics and Statistics Honours Booklet

Learning Outcomes

  • The course will:

  • introduce generalised linear models
  • introduce advanced data analysis techniques including mixed effects models, repeated measures and additive models
  • introduce the use of the statistical open-source software R
  • give 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 datasets
  • interpret model results in such a way that a non-user of statistics can understand
  • use R competently
  • write a scientific and technical report.

Pre-requisites

Subject to approval of the Head of School.

Timetable 2020

Students must attend one activity from each section.

Computer Lab A
Activity Day Time Location Weeks
01 Wednesday 09:00 - 11:00 - (22/4-27/5)
Jack Erskine 031 Lecture Theatre (19/2-25/3)
17 Feb - 29 Mar
20 Apr - 31 May
Tutorial A
Activity Day Time Location Weeks
01 Friday 10:00 - 11:00 Rehua 008 Computer Lab 17 Feb - 22 Mar
02 Thursday 11:00 - 12:00 Jack Erskine 436 Computer Lab 17 Feb - 22 Mar
Tutorial B
Activity Day Time Location Weeks
01 Friday 10:00 - 11:00 20 Apr - 31 May

Examination and Formal Tests

Test A
Activity Day Time Location Weeks
01 Friday 08:00 - 20:00 27 Apr - 3 May

Course Coordinator

Jennifer Wilcock

Lecturer

Alasdair Noble

Textbooks / Resources

Recommended Reading:

Crawley, M.J. 2005. Statistics : an introduction using R. 327pp.
Crawley, M.J. 2007. The R book. 942pp.
Faraway, J.J. 2005.  Linear models in R.  229pp.
Faraway, J.J. 2006.  Extending the linear models with R.  301pp.

These are on High Demand in the EPS Library.

Indicative Fees

Domestic fee $989.00

* Fees include New Zealand GST and do not include any programme level discount or additional course related expenses.

For further information see Mathematics and Statistics.

All STAT446 Occurrences

  • STAT446-20S1 (C) Semester One 2020