MXN600 Advanced Statistical Data Analysis
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: | MXN600 |
|---|---|
| Prerequisite(s): | MXN500 |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| Domestic tuition unit fee | $2,916 |
| International unit fee | $4,104 |
Unit Outline: Semester 2 2020, Gardens Point, Internal
| Unit code: | MXN600 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | MXN500 |
| Coordinator: | James McGree | james.mcgree@qut.edu.au |
Overview
This advanced statistics unit will introduce modern statistical methods of data analytics that are frequently used in industry and government to solve real-world problems. It introduces modelling techniques that can be used when it is unreasonable to assume the data are continuous random variables from a normal distribution and/or that the expected value of the random variable can be modelled as a linear combination of regression parameters. This is a Masters level unit, and the knowledge and skills developed in this unit are relevant to those studying advanced data analytics. Further studies in data analytics and data science will most likely build on this unit by extending your analytical skills through industry or research-based projects.
Learning Outcomes
On successful completion of this unit you will be able to:
- Expertly and critically carry out data analytics using statistical models in the analysis of various data sets and examples.
- Use R to carry out statistical analyses.
- Communicate statistical conclusions clearly and concisely both in written form and orally.
Content
1. Development of statistical software (R) programming skills.
2. Generalised linear models: 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.
3. Extending generalised linear models for handling grouped data using generalised linear mixed models.
Learning Approaches
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. These will be available through QUT Blackboard.
Lectures will introduce various topics and application areas, and 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.
In workshops and lectures, and in particular while working on your group-based assessment items, you will engage in collaborative activity with peers, tutors and lecturers. You will work with peers and with your lecturer and tutor to develop effective methods/approaches for communicating, retrieving, evaluating and presenting information in an ethical way, and you will learn how to work effectively within groups with consideration for cultural differences.
Your work will be context and case based using a wide variety of examples from many different areas of application. The emphasis will be on enabling your learning through experience, both in groups and as individuals, on providing opportunities to enhance your written and oral communication, and on further developing skills and attitudes to promote your lifelong 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.
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 (Theoretical)
Description: This item consists of regular problems where you will develop and implement a statistical model to appropriately describe real world data sets. The work will help you understand statistical methodology and further develop communication skills through engaging with industry representatives.Relates to learning outcomes
1-3
Assessment: Demonstration
You will present an application of your theoretical model to peers for review.Relates to learning outcomes
1,3
Assessment: Report
You will report on your statistical analysis similar to an industry stakeholder.Relates to learning outcomes
1-3
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 are no set texts for this unit. Detailed lecture notes and other course unit material will be made available on the unit Blackboard site.
There are however, reference texts for this unit, many of which can be located in the library. There are also online resources such as lecture notes and some e-books that can be found online. Some example reference texts are
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) , 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