MXB107 Introduction to Statistical Modelling
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: | MXB107 |
|---|---|
| Assumed Knowledge: | Specialist Mathematics or MXB100, or concurrently enrolled in MXB100. |
| 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 2 2024, Gardens Point, Internal
| Unit code: | MXB107 |
|---|---|
| Credit points: | 12 |
| Assumed Knowledge: | Specialist Mathematics or MXB100, or concurrently enrolled in MXB100 |
| Coordinator: | Gentry White | gentry.white@qut.edu.au |
Overview
Statistical modelling provides methods for analysing data to gain insight into real-world problems. The aim of this unit is to introduce a wide range of fundamental statistical modelling and data analysis techniques, and demonstrate the role they play in drawing inferences in real-world problems. This unit is designed around the exploration of contemporary and important issues through the analysis of real data sets, while simultaneously and necessarily building your statistical modelling expertise. You will learn how to propose research questions, analyse real data sets to attempt to answer these questions, and draw inferences and conclusions based on your findings. The importance of ethical considerations when dealing with real data sets will be emphasised. The R programming language will be introduced, and you will gain experience and build your expertise in using this industry-leading software to conduct statistical analyses.
Learning Outcomes
On successful completion of this unit you will be able to:
- Demonstrate knowledge of and proficiency with the fundamental techniques of statistical modelling and inference.
- Critically select and apply statistical models, justify decisions, and interpret results in context.
- Communicate in written and graphical formats to specialist and non-specialist audiences.
- Identify assumptions, limitations and ethical considerations relating to statistical analysis.
- Use R statistical software to model and analyse data.
Content
Data Gathering Issues: Design, Representativeness and Bias, Accuracy and Confounding. Data Summarisation: Graphical and Numerical Methods. Sample statistics: mean, variance, proportions and their properties. Law of Large Numbers and the Central Limit Theorem. Introduction to Distribution Theory. Introduction to Likelihood, concept of likelihood, maximum likelihood estimators and their properties. Linear Models: Least Squares and maximum likelihood. The Confidence Interval for model parameters. The Hypothesis Test for model parameters, ANOVA, and model diagnostics.
Learning Approaches
This unit is designed around the exploration of contemporary and important issues through the analysis of real data sets, while simultaneously and necessarily building your statistical modelling expertise. While lectures focus on the required mathematical and statistical content, weekly individual-based computer labs and group-based collaborative learning workshops focus on the essential skills of scientific enquiry. In these sessions you will learn how to propose research questions, manipulate and analyse real data sets to attempt to answer these questions, and draw inferences and conclusions based on your findings. The importance of ethical considerations when dealing with real data sets will be emphasised.
The R programming language will be introduced, and you will gain experience and build your expertise in using this industry-leading software to conduct statistical analyses.
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 learning 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: Assignment
You will be given a scenario involving real world datasets and will be required to analyse and interpret the data and provide recommendations.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Assignment
You will be given a scenario involving real world datasets and will be required to analyse and interpret the data and provide recommendations.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Examination (invigilated)
This examination 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.
The examination will be at a local testing centre. For students enrolled as internal or on-campus, the local testing centre will be on QUT campus. For students enrolled as online, QUT Examinations will provide testing centre information.
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 is no set text for this unit.
There is a recommended text Introduction to Probability and Statistics Metric Edition by Mendenhall, Beaver, and Beaver
In addition to this text, there are many online resources such as lecture notes and some e-books that can be found online.
Resource Materials
Recommended text(s)
https://cengage.com.au/product/title/introduction-to-probability-and-statistics-me/isbn/9780357114469
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
Standards/Competencies
This unit is designed to support your development of the following standards\competencies.
Engineers Australia Stage 1 Competency Standard for Professional Engineer
1: Knowledge and Skill Base
Relates to: Assignment, Assignment
Relates to: Assignment, Assignment, Examination (invigilated)
Relates to: Assignment, Assignment, Examination (invigilated)
2: Engineering Application Ability
Relates to: Assignment, Assignment, Examination (invigilated)
Relates to: Assignment, Assignment, Examination (invigilated)
Relates to: Assignment, Assignment, Examination (invigilated)
3: Professional and Personal Attributes
Relates to: Assignment, Assignment
Relates to: Assignment, Assignment, Examination (invigilated)
Relates to: Assignment, Assignment
Unit Outline: Semester 2 2024, Online
| Unit code: | MXB107 |
|---|---|
| Credit points: | 12 |
| Assumed Knowledge: | Specialist Mathematics or MXB100, or concurrently enrolled in MXB100 |
Overview
Statistical modelling provides methods for analysing data to gain insight into real-world problems. The aim of this unit is to introduce a wide range of fundamental statistical modelling and data analysis techniques, and demonstrate the role they play in drawing inferences in real-world problems. This unit is designed around the exploration of contemporary and important issues through the analysis of real data sets, while simultaneously and necessarily building your statistical modelling expertise. You will learn how to propose research questions, analyse real data sets to attempt to answer these questions, and draw inferences and conclusions based on your findings. The importance of ethical considerations when dealing with real data sets will be emphasised. The R programming language will be introduced, and you will gain experience and build your expertise in using this industry-leading software to conduct statistical analyses.
Learning Outcomes
On successful completion of this unit you will be able to:
- Demonstrate knowledge of and proficiency with the fundamental techniques of statistical modelling and inference.
- Critically select and apply statistical models, justify decisions, and interpret results in context.
- Communicate in written and graphical formats to specialist and non-specialist audiences.
- Identify assumptions, limitations and ethical considerations relating to statistical analysis.
- Use R statistical software to model and analyse data.
Content
Data Gathering Issues: Design, Representativeness and Bias, Accuracy and Confounding. Data Summarisation: Graphical and Numerical Methods. Sample statistics: mean, variance, proportions and their properties. Law of Large Numbers and the Central Limit Theorem. Introduction to Distribution Theory. Introduction to Likelihood, concept of likelihood, maximum likelihood estimators and their properties. Linear Models: Least Squares and maximum likelihood. The Confidence Interval for model parameters. The Hypothesis Test for model parameters, ANOVA, and model diagnostics.
Learning Approaches
This unit is designed around the exploration of contemporary and important issues through the analysis of real data sets, while simultaneously and necessarily building your statistical modelling expertise. While lectures focus on the required mathematical and statistical content, weekly individual-based computer labs and group-based collaborative learning workshops focus on the essential skills of scientific enquiry. In these sessions you will learn how to propose research questions, manipulate and analyse real data sets to attempt to answer these questions, and draw inferences and conclusions based on your findings. The importance of ethical considerations when dealing with real data sets will be emphasised.
The R programming language will be introduced, and you will gain experience and build your expertise in using this industry-leading software to conduct statistical analyses.
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 learning 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: Assignment
You will be given a scenario involving real world datasets and will be required to analyse and interpret the data and provide recommendations.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Assignment
You will be given a scenario involving real world datasets and will be required to analyse and interpret the data and provide recommendations.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Examination (invigilated)
This examination 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.
The examination will be at a local testing centre. For students enrolled as internal or on-campus, the local testing centre will be on QUT campus. For students enrolled as online, QUT Examinations will provide testing centre information.
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 is no set text for this unit.
There is a recommended text Introduction to Probability and Statistics Metric Edition by Mendenhall, Beaver, and Beaver
In addition to this text, there are many online resources such as lecture notes and some e-books that can be found online.
Resource Materials
Recommended text(s)
https://cengage.com.au/product/title/introduction-to-probability-and-statistics-me/isbn/9780357114469
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
Standards/Competencies
This unit is designed to support your development of the following standards\competencies.
Engineers Australia Stage 1 Competency Standard for Professional Engineer
1: Knowledge and Skill Base
Relates to: Assignment, Assignment
Relates to: Assignment, Assignment, Examination (invigilated)
Relates to: Assignment, Assignment, Examination (invigilated)
2: Engineering Application Ability
Relates to: Assignment, Assignment, Examination (invigilated)
Relates to: Assignment, Assignment, Examination (invigilated)
Relates to: Assignment, Assignment, Examination (invigilated)
3: Professional and Personal Attributes
Relates to: Assignment, Assignment
Relates to: Assignment, Assignment, Examination (invigilated)
Relates to: Assignment, Assignment