MXB344 Generalised Linear Models
To view more information for this unit, select Unit Outline from the list below. Please note the teaching period for which the Unit Outline is relevant.
| Unit code: | MXB344 |
|---|---|
| Prerequisite(s): | MXB242 |
| Equivalent(s): | MAB624 |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $555 |
| Domestic tuition unit fee | $3,324 |
| International unit fee | $4,296 |
Unit Outline: Semester 1 2024, Gardens Point, Internal
| Unit code: | MXB344 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | MXB242 |
| Equivalent: | MAB624 |
| Coordinator: | James McGree | james.mcgree@qut.edu.au |
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:
- Apply knowledge of the concepts and techniques of generalised linear modelling.
- Use R to carry out statistical analyses using theoretical, technical and computational skills and correctly interpret the output.
- Critically evaluate and interpret statistical analysis to answer real-world questions.
- Communicate statistical conclusions clearly and concisely in written, visual and oral form.
- 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
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.
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.
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.
Academic Integrity
Students are expected to engage in learning and assessment at QUT with honesty, transparency and fairness. Maintaining academic integrity means upholding these principles and demonstrating valuable professional capabilities based on ethical foundations.
Failure to maintain academic integrity can take many forms. It includes cheating in examinations, plagiarism, self-plagiarism, collusion, and submitting an assessment item completed by another person (e.g. contract cheating). It can also include providing your assessment to another entity, such as to a person or website.
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.
Further details of QUT’s approach to academic integrity are outlined in the Academic integrity policy and the Student Code of Conduct. Breaching QUT’s Academic integrity policy is regarded as student misconduct and can lead to the imposition of penalties ranging from a grade reduction to exclusion from QUT.
Resources
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
Unit Outline: Semester 1 2024, Online
| Unit code: | MXB344 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | MXB242 |
| Equivalent: | MAB624 |
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:
- Apply knowledge of the concepts and techniques of generalised linear modelling.
- Use R to carry out statistical analyses using theoretical, technical and computational skills and correctly interpret the output.
- Critically evaluate and interpret statistical analysis to answer real-world questions.
- Communicate statistical conclusions clearly and concisely in written, visual and oral form.
- 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
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.
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.
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.
Academic Integrity
Students are expected to engage in learning and assessment at QUT with honesty, transparency and fairness. Maintaining academic integrity means upholding these principles and demonstrating valuable professional capabilities based on ethical foundations.
Failure to maintain academic integrity can take many forms. It includes cheating in examinations, plagiarism, self-plagiarism, collusion, and submitting an assessment item completed by another person (e.g. contract cheating). It can also include providing your assessment to another entity, such as to a person or website.
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.
Further details of QUT’s approach to academic integrity are outlined in the Academic integrity policy and the Student Code of Conduct. Breaching QUT’s Academic integrity policy is regarded as student misconduct and can lead to the imposition of penalties ranging from a grade reduction to exclusion from QUT.
Resources
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