MXB344 Generalised Linear Models


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Unit Outline: Semester 1 2025, Gardens Point, Internal

Unit code:MXB344
Credit points:12
Pre-requisite:MXB242
Equivalent:MAB624
Coordinator:James McGree | james.mcgree@qut.edu.au
Disclaimer - Offer of some units is subject to viability, and information in these Unit Outlines is subject to change prior to commencement of the teaching period.

Overview

For data that arise in, for example, science and commerce, it is often unreasonable to assume they are continuous random variables from a normal distribution. It is likewise unlikely that data are handed to an analyst in a state ready for advanced statistical techniques. In this unit you will be introduced to modelling techniques and methodology for the explanation of non-normal data. You will also learn, by way of a realistic project, techniques to overcome common issues with shaping data for analysis.  Hence, you will be well prepared in the application of appropriate statistical practice when such data are encountered in the real world.

Learning Outcomes

On successful completion of this unit you will be able to:

  1. Apply knowledge of the concepts and techniques of generalised linear modelling.
  2. Use R to carry out statistical analyses using theoretical, technical and computational skills and correctly interpret the output.
  3. Critically evaluate and interpret statistical analysis to answer real-world questions.
  4. Communicate statistical conclusions clearly and concisely in written, visual and oral form.
  5. Collaborate effectively in a team to solve problems and communicate results.

Content

Development of statistical software (R) programming skills.  Modelling continuous responses using normal regression.  Modelling binary data using Binomial regression. Modelling count data using Poisson regression.  Modelling categorical data using Multinomial regression.  Modelling categorical data using log-linear models.  Extensions to generalised linear mixed models.  Discover and evaluate methods for dealing with missing data.

Learning Approaches

This unit is available for you to study in either on-campus or online mode. 
 
This unit involves pre-recorded lectures, face-to-face lectures, consultations and practical activities conducted in the computer lab.  Pre-recorded and face-to-face lectures will introduce various topics and application areas. The consultations will allow time for discussion and assistance.  The practical will be used as time for you to develop solutions to the various exercises, for you to use statistical software (e.g. R) and discuss various aspects of the analyses with the demonstrator.  You will have access during the semester to course unit material in a variety of forms including lecture notes, selections from texts, worked programming examples, and solutions to exercises.
 
In computer labs and consultations, and in particular while working on your group-based assessment items, you will engage in collaborative activity with peers. You will work with peers to develop effective methods/approaches for communicating, evaluating and presenting information and you will learn how to work effectively within groups with consideration for cultural differences.
 
You can expect to spend 10 - 15 hours per week involved in preparing for and attending all scheduled classes, consultations, completing assessment tasks, and undertaking your own independent study to consolidate your learning.

Feedback on Learning and Assessment

Formative feedback will be provided for the in-semester assessment items by way of written comments on the assessment items, student perusal of the marked assessment piece and informal interview as required.

Summative feedback will be provided throughout the semester with progressive posting of results via Canvas.

Assessment

Overview

The assessment items in this unit are designed to determine your level of competency in meeting the unit outcomes while providing you with a range of tasks with varying levels of skill development and difficulty.

Unit Grading Scheme

7- point scale

Assessment Tasks

Assessment: Model and Report

You will develop and implement a statistical model to appropriately describe a real world data set.  The work will help you understand statistical methodology and further develop communication skills.

This assignment is eligible for the 48-hour late submission period and assignment extensions.

Weight: 40
Individual/Group: Individual
Due (indicative): Mid semester
Related Unit learning outcomes: 1, 2, 3, 4

Assessment: Presentation

You will present the findings of your applied statistical analysis orally.

The late submission period does not apply, and no extensions are available.

Weight: 10
Individual/Group: Individual
Due (indicative): Towards the end of semester
Related Unit learning outcomes: 1, 2, 3, 4, 5

Assessment: Model and Report

You will carry out a statistical project in groups. You will report on your statistical analysis similar as to an industry stakeholder.

This assignment is eligible for the 48-hour late submission period and assignment extensions.

Weight: 50
Individual/Group: Individual and group
Due (indicative): End of semester
Related Unit learning outcomes: 1, 2, 3, 4, 5

Academic Integrity

Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.

The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.

You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.

Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.

Resources

There is no set text for this unit.
There are many reference texts for this unit, many of which can be located in the library. There are also many online resources such as lecture notes and some e-books that can be found online. Example reference texts are listed below.
1. Dobson AJ & Barnett AG (2008), An Introduction to Generalized Linear Models, 3rd edition. Chapman & Hall, Boca Raton.
2. Grolemund G & Wickham H, R for Data Science (2016) <http://r4ds.had.co.nz/>, O'Reiley, Sebastpol.
3. McCullagh P & Nelder JA (1989), Generalized Linear Models, Chapman & Hall. London.
4. Myers RH, Montgomery DC & Vining GG (2002), Generalized Linear Models: With Applications in Engineering and the Sciences, Wiley, New York.
5. Yandell BS (1997), Practical Data Analysis for Designed Experiments, Chapman & Hall. London.
6. Chatfield C (1995), Problem Solving: A Statistician's Guide, 2nd edition, Chapman & Hall. London.

Risk Assessment Statement

There are no out of the ordinary risks associated with this unit, as all classes will be held in ordinary lecture theatres. Emergency exits and assembly areas will be pointed out in the first few lectures. You are referred to the University policy on health and safety.
http://www.mopp.qut.edu.au/A/A_09_01.jsp