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Department of Mathematics & Statistics

STAT341 Regression and Modelling 2

First Semester
18 points
Not available after 2019

We build on the material covered in STAT 241 Regression and Modelling 1. Emphasis will be placed on the practice of applying the techniques to real datasets, using the powerful software R (no previous knowledge of R is required).

Paper details

An introduction to generalised linear models, nonlinear regression models, and mixed effects models, with a mixture of background theory and practice in applying the methods to real datasets.

Potential students

Any student interested in techniques that can be used to model a very broad range of datasets.


STAT 241

Main topics

• Review of the normal linear model

• Generalized linear models

• Model checking and model comparison

• Model selection and model averaging

• Model diagnostics in generalized linear models

• Overdispersion in generalized linear models

• Log-linear models for contingency tables

• Generalized additive models

• Fixed and random effects

• Linear mixed effects models

• Repeated measurements

• Bayesian generalized linear models

Required text


Useful reference

Julian Faraway (2006), Extending the linear model with R, Chapman and Hall


David Fletcher (Room 515)


32 lectures: These are in Room 241 of Mathematics and Statistics (Science III) at 2pm every Monday and Wednesday, plus 2pm Friday in Weeks 1, 3, 5, 7, 9, and 11


Monday 3-5 pm, Computer Lab in B21 (Science III), in Weeks 2-12

Internal Assessment

The internal assessment comes from 5 assignments and a mid-semester test

Exam format

3-hour final exam with 4 questions

Final mark

Your final mark F in the paper will be calculated according to this formula:

F = 0.5E + 0.33A + 0.17T


  • E is the Exam mark
  • A is the Assignments mark
  • T is the Tests mark

and all quantities are expressed as percentages.

Students must abide by the University’s Academic Integrity Policy

Academic integrity means being honest in your studying and assessments. It is the basis for ethical decision-making and behaviour in an academic context. Academic integrity is informed by the values of honesty, trust, responsibility, fairness, respect and courage.

Academic misconduct is seeking to gain for yourself, or assisting another person to gain, an academic advantage by deception or other unfair means. The most common form of academic misconduct is plagiarism.

Academic misconduct in relation to work submitted for assessment (including all course work, tests and examinations) is taken very seriously at the University of Otago.

All students have a responsibility to understand the requirements that apply to particular assessments and also to be aware of acceptable academic practice regarding the use of material prepared by others. Therefore it is important to be familiar with the rules surrounding academic misconduct at the University of Otago; they may be different from the rules in your previous place of study.

Any student involved in academic misconduct, whether intentional or arising through failure to take reasonable care, will be subject to the University’s Student Academic Misconduct Procedures which contain a range of penalties.

If you are ever in doubt concerning what may be acceptable academic practice in relation to assessment, you should clarify the situation with your lecturer before submitting the work or taking the test or examination involved.

Types of academic misconduct are as follows:


The University makes a distinction between unintentional plagiarism (Level One) and intentional plagiarism (Level Two).

  • Although not intended, unintentional plagiarism is covered by the Student Academic Misconduct Procedures. It is usually due to lack of care, naivety, and/or to a lack to understanding of acceptable academic behaviour. This kind of plagiarism can be easily avoided.
  • Intentional plagiarism is gaining academic advantage by copying or paraphrasing someone elses work and presenting it as your own, or helping someone else copy your work and present it as their own. It also includes self-plagiarism which is when you use your own work in a different paper or programme without indicating the source. Intentional plagiarism is treated very seriously by the University.

Unauthorised Collaboration

Unauthorised Collaboration occurs when you work with, or share work with, others on an assessment which is designed as a task for individuals and in which individual answers are required. This form does not include assessment tasks where students are required or permitted to present their results as collaborative work. Nor does it preclude collaborative effort in research or study for assignments, tests or examinations; but unless it is explicitly stated otherwise, each students answers should be in their own words. If you are not sure if collaboration is allowed, check with your lecturer..


Impersonation is getting someone else to participate in any assessment on your behalf, including having someone else sit any test or examination on your behalf.


Falsification is to falsify the results of your research; presenting as true or accurate material that you know to be false or inaccurate.

Use of Unauthorised Materials

Unless expressly permitted, notes, books, calculators, computers or any other material and equipment are not permitted into a test or examination. Make sure you read the examination rules carefully. If you are still not sure what you are allowed to take in, check with your lecturer.

Assisting Others to Commit Academic Misconduct

This includes impersonating another student in a test or examination; writing an assignment for another student; giving answers to another student in a test or examination by any direct or indirect means; and allowing another student to copy answers in a test, examination or any other assessment.

Further information

While we strive to keep details as accurate and up-to-date as possible, information given here should be regarded as provisional. Individual lecturers will confirm teaching and assessment methods.

Length plotted against age for a sample of alligators, together with a fitted nonlinear regression curve.

In clinical trials the time until a certain event is often important. The event might be the death of an individual, the disappearance of a tumour, or the passing from one stage into the next stage of an illness.

The timimg of this event for individuals in the trial is recorded. Sometimes the study is ended before all participants have reached the event, or some participants might leave the study before its completion — these situations provide what is called censored data.

The Kaplan-Meier survival curve is calculated by taking the product of the conditional probabilities of survival from one time-interval to the next. It produces a decreasing stepped function.