MXQ500 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: | MXQ500 |
|---|---|
| Antirequisite(s): | MXN500 |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $555 |
| Domestic tuition unit fee | $3,348 |
| International unit fee | $4,392 |
Unit Outline: Session 1 2024, QUT Online, Online
| Unit code: | MXQ500 |
|---|---|
| Credit points: | 12 |
| Equivalent: | MXN500 |
Overview
Appropriate application of statistical methods is essential in many quantitative roles. The focus of this unit is on applying mathematical and statistical methods in real-world contexts. You will look for meaningful patterns and model data to increasing levels of complexity. In particular, we will cover data and variables, visualisation, basic probability, hypothesis testing, and linear regression. You will also learn how to select and apply appropriate quantitative methods using software such as R, an open source statistical software. You will practice your quantitative skills using real data from scientists, business, and governments. This unit is appropriate for those requiring an introduction to, or a refresher in, statistics.
Learning Outcomes
On successful completion of this unit you will be able to:
- Demonstrate an understanding of fundamental statistical models and methods.
- Apply fundamental statistical modelling techniques in an appropriate way.
- Use statistical software packages such as R to model and analyse data.
Content
The mathematical/statistical content of this unit includes:
1. Data Summarisation: graphical and numerical methods including summary statistics, location, spread, shape and correlation.
2. Data Gathering Issues: Design, Representativeness and Bias, Accuracy and Confounding.
3. Introduction to Distribution Theory: Normal and t-distribution, Law of Large Numbers and the Central Limit Theorem.
4. Linear Models: Least Squares estimation.
5. Parameter estimation, inference, and the likelihood function.
All data analytics will be performed using the R statistical software package.
Learning Approaches
The material presented will be context-based utilising examples from a range of real-world applications and purely mathematical scenarios. The emphasis will be on learning by doing, learning in groups and as individuals, written and oral communication, and developing skills and attitudes to promote life-long learning. Appropriate approaches to the communication of mathematical and statistical information to diverse audiences will be explored via the examples presented in lectures and workshops. You are expected to consolidate the material presented by working a wide variety of exercises, problems and online learning activities in your own time.
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 Submission and Extensions
Assessment items submitted after the due date without an approved extension will not be marked and will receive a grade of 1 or 0%. If special circumstances prevent you from meeting the assessment due date, you can apply for an extension. If you don't have an approved extension you should submit the work you have done by the due date and it will be marked against the assessment criteria. QUT's assessment submission requirements reflect the expectations of professional practice where you will need to meet deadlines.
Assessment
Overview
Formative assessment will be conducted throughout the semester in the practical classes. There will be progressive summative assessment related to the general topics covered in this unit, followed by a final examination. All assessment items will focus on the key concepts and methodologies taught in this unit with a required level of effective written communication.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Workbook
You will be required to complete problem solving exercises where you will apply your operational knowledge and problem solving skills in analysing real world datasets. The tasks will reflect the material taught throughout the unit.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Examination (invigilated)
This assessment will consist of questions relating to material taught throughout the whole semester, with an emphasis on using techniques and interpreting output and results in context.
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
There are no set texts 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.
Moore, D.S, McCabe, G.P. and Craig, B.A. (2009). Introduction to the Practice of Statistics. 6th Edition. W.H. Freeman and Company.
Risk Assessment Statement
There are no out of the ordinary risks associated with this unit. You are referred to the University policy on health and safety.
http://www.mopp.qut.edu.au/A/A_09_01.jsp
Unit Outline: Session 3 2024, QUT Online, Online
| Unit code: | MXQ500 |
|---|---|
| Credit points: | 12 |
| Equivalent: | MXN500 |
Overview
Appropriate application of statistical methods is essential in many quantitative roles. The focus of this unit is on applying mathematical and statistical methods in real-world contexts. You will look for meaningful patterns and model data to increasing levels of complexity. In particular, we will cover data and variables, visualisation, basic probability, hypothesis testing, and linear regression. You will also learn how to select and apply appropriate quantitative methods using software such as R, an open source statistical software. You will practice your quantitative skills using real data from scientists, business, and governments. This unit is appropriate for those requiring an introduction to, or a refresher in, statistics.
Learning Outcomes
On successful completion of this unit you will be able to:
- Demonstrate an understanding of fundamental statistical models and methods.
- Apply fundamental statistical modelling techniques in an appropriate way.
- Use statistical software packages such as R to model and analyse data.
Content
The mathematical/statistical content of this unit includes:
1. Data Summarisation: graphical and numerical methods including summary statistics, location, spread, shape and correlation.
2. Data Gathering Issues: Design, Representativeness and Bias, Accuracy and Confounding.
3. Introduction to Distribution Theory: Normal and t-distribution, Law of Large Numbers and the Central Limit Theorem.
4. Linear Models: Least Squares estimation.
5. Parameter estimation, inference, and the likelihood function.
All data analytics will be performed using the R statistical software package.
Learning Approaches
The material presented will be context-based utilising examples from a range of real-world applications and purely mathematical scenarios. The emphasis will be on learning by doing, learning in groups and as individuals, written and oral communication, and developing skills and attitudes to promote life-long learning. Appropriate approaches to the communication of mathematical and statistical information to diverse audiences will be explored via the examples presented in lectures and workshops. You are expected to consolidate the material presented by working a wide variety of exercises, problems and online learning activities in your own time.
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 Submission and Extensions
Assessment items submitted after the due date without an approved extension will not be marked and will receive a grade of 1 or 0%. If special circumstances prevent you from meeting the assessment due date, you can apply for an extension. If you don't have an approved extension you should submit the work you have done by the due date and it will be marked against the assessment criteria. QUT's assessment submission requirements reflect the expectations of professional practice where you will need to meet deadlines.
Assessment
Overview
Formative assessment will be conducted throughout the semester in the practical classes. There will be progressive summative assessment related to the general topics covered in this unit, followed by a final examination. All assessment items will focus on the key concepts and methodologies taught in this unit with a required level of effective written communication.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Workbook
You will be required to complete problem solving exercises where you will apply your operational knowledge and problem solving skills in analysing real world datasets. The tasks will reflect the material taught throughout the unit.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Examination (invigilated)
This assessment will consist of questions relating to material taught throughout the whole semester, with an emphasis on using techniques and interpreting output and results in context.
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
There are no set texts 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.
Moore, D.S, McCabe, G.P. and Craig, B.A. (2009). Introduction to the Practice of Statistics. 6th Edition. W.H. Freeman and Company.
Risk Assessment Statement
There are no out of the ordinary risks associated with this unit. You are referred to the University policy on health and safety.
http://www.mopp.qut.edu.au/A/A_09_01.jsp