STAT319-22S1 (C) Semester One 2022

Generalised Linear Models

15 points

Details:
Start Date: Monday, 21 February 2022
End Date: Sunday, 26 June 2022
Withdrawal Dates
Last Day to withdraw from this course:
  • Without financial penalty (full fee refund): Sunday, 6 March 2022
  • Without academic penalty (including no fee refund): Sunday, 15 May 2022

Description

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.

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.
    • University Graduate Attributes

      This course will provide students with an opportunity to develop the Graduate Attributes specified below:

      Employable, innovative and enterprising

      Students will develop key skills and attributes sought by employers that can be used in a range of applications.

Prerequisites

30 points from STAT200-299

Timetable 2022

Students must attend one activity from each section.

Lecture A
Activity Day Time Location Weeks
01 Tuesday 09:00 - 11:00 Jack Erskine 445
21 Feb - 10 Apr
2 May - 5 Jun
Tutorial A
Activity Day Time Location Weeks
01 Wednesday 09:00 - 10:00 Ernest Rutherford 212 Computer Lab
21 Feb - 10 Apr
2 May - 5 Jun
02 Wednesday 10:00 - 11:00 Ernest Rutherford 212 Computer Lab
21 Feb - 10 Apr
2 May - 5 Jun

Course Coordinator / Lecturer

Daniel Gerhard

Lecturer

Alasdair Noble

Assessment

Four assignments (10%)
Final exam (60%).

Indicative Fees

Domestic fee $802.00

International fee $4,563.00

* 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 .

All STAT319 Occurrences

  • STAT319-22S1 (C) Semester One 2022