MXB242 Regression and Design
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: | MXB242 |
|---|---|
| Prerequisite(s): | MXB107 |
| Equivalent(s): | MAB414 |
| 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: | MXB242 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | MXB107 |
| Equivalent: | MAB414 |
| Coordinator: | Leah South | l1.south@qut.edu.au |
Overview
This is an intermediate applied statistics unit addressing the collection (design of experiments), exploration, summarisation, analysis and reporting of continuous data. You will analyse data using general linear models and communicate findings using oral and written methods. You will use mathematical and statistical software, such as R, to enhance your data analysis and develop your statistical programming skills. The application of statistical data analysis is pervasive across Engineering, Science, Health and Business. Hence, this unit is suitable for both Mathematics students and students in other disciplines. This unit is intended for students who have completed foundation studies in statistical data analysis and who wish to develop further skills in applied statistics. MXB344 Generalised Linear Models builds on this unit by considering the analysis of binary, categorical and count data. MXB343 Modelling Dependent Data extends this unit for data that are not independent.
Learning Outcomes
On successful completion of this unit you will be able to:
- Demonstrate knowledge of the principles of fitting and assessing statistical models of independent data via model formulation and parametric estimation.
- Use R to carry out statistical analyses using theoretical, technical and computational skills and correctly interpret the R output.
- Critically design experiments which appropriately address statistical considerations, collect data, and fit statistical models to experimental data.
- Use a rigorous statistical approach to interpret findings and draw conclusions.
- Communicate clearly and concisely in written, graphical and oral formats for specialist audiences.
- Collaborate in a team environment to create and present a report.
Content
Parametric estimation; least squares; basic inference and model choice; inference using Student t- and F-distribution; ANOVA, ANCOVA; design of experiments, replication, blocking, randomisation, completely randomised design, complete block designs, factorial designs; sampling methods; bootstrapping.
Learning Approaches
This unit is available for you to study in either on-campus or online mode. You can expect to spend 10 hours per week involved in preparing for and attending scheduled classes, preparing and completing assessment tasks as well as independent study and consolidation of your learning.
You will be provided with learning resources including pre-recorded videos and formative quizzes that you can access flexibly to prepare for your timetabled learning activities. The pre-recorded videos will provide you with theoretical background and concepts applied in problem solving processes, and the formative quizzes are for you to check your understanding of the new concepts.
The timetabled sessions are an important opportunity for you to interact directly with the teaching team and ask for help or clarification when needed. The timetabled interactive lecture sessions will emphasise important concepts and work through additional example problems relevant for your assessment. In the timetabled workshops you will solve a range of example problems, from purely mathematical exercises to real-world applications in software.
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: Data Collection Plan
Working in groups you will prepare a data collection plan for your project.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Project (applied)
Group project on design, collection and analysis of experimental data including identification of questions of interest; planning, data collection, handling of data; exploratory data analysis, analysis of data; reporting on findings, drawing conclusions.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Examination (invigilated)
Assessment based on work covered during the semester. A significant amount of the material will require the understanding of output from the computing package R.
The examination will require attendance 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 local 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 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.
Weisberg, S. (2005) Applied linear regression. 3rd edition, John Wiley & Sons, Inc., Hoboken, New Jersey.
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: Data Collection Plan, Project (applied), Examination (invigilated)
2: Engineering Application Ability
Relates to: Data Collection Plan, Project (applied), Examination (invigilated)
Relates to: Data Collection Plan, Project (applied), Examination (invigilated)
3: Professional and Personal Attributes
Relates to: Data Collection Plan, Project (applied), Examination (invigilated)
Relates to: Data Collection Plan, Project (applied)
Relates to: Data Collection Plan, Project (applied)
Unit Outline: Semester 1 2024, Online
| Unit code: | MXB242 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | MXB107 |
| Equivalent: | MAB414 |
Overview
This is an intermediate applied statistics unit addressing the collection (design of experiments), exploration, summarisation, analysis and reporting of continuous data. You will analyse data using general linear models and communicate findings using oral and written methods. You will use mathematical and statistical software, such as R, to enhance your data analysis and develop your statistical programming skills. The application of statistical data analysis is pervasive across Engineering, Science, Health and Business. Hence, this unit is suitable for both Mathematics students and students in other disciplines. This unit is intended for students who have completed foundation studies in statistical data analysis and who wish to develop further skills in applied statistics. MXB344 Generalised Linear Models builds on this unit by considering the analysis of binary, categorical and count data. MXB343 Modelling Dependent Data extends this unit for data that are not independent.
Learning Outcomes
On successful completion of this unit you will be able to:
- Demonstrate knowledge of the principles of fitting and assessing statistical models of independent data via model formulation and parametric estimation.
- Use R to carry out statistical analyses using theoretical, technical and computational skills and correctly interpret the R output.
- Critically design experiments which appropriately address statistical considerations, collect data, and fit statistical models to experimental data.
- Use a rigorous statistical approach to interpret findings and draw conclusions.
- Communicate clearly and concisely in written, graphical and oral formats for specialist audiences.
- Collaborate in a team environment to create and present a report.
Content
Parametric estimation; least squares; basic inference and model choice; inference using Student t- and F-distribution; ANOVA, ANCOVA; design of experiments, replication, blocking, randomisation, completely randomised design, complete block designs, factorial designs; sampling methods; bootstrapping.
Learning Approaches
This unit is available for you to study in either on-campus or online mode. You can expect to spend 10 hours per week involved in preparing for and attending scheduled classes, preparing and completing assessment tasks as well as independent study and consolidation of your learning.
You will be provided with learning resources including pre-recorded videos and formative quizzes that you can access flexibly to prepare for your timetabled learning activities. The pre-recorded videos will provide you with theoretical background and concepts applied in problem solving processes, and the formative quizzes are for you to check your understanding of the new concepts.
The timetabled sessions are an important opportunity for you to interact directly with the teaching team and ask for help or clarification when needed. The timetabled interactive lecture sessions will emphasise important concepts and work through additional example problems relevant for your assessment. In the timetabled workshops you will solve a range of example problems, from purely mathematical exercises to real-world applications in software.
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: Data Collection Plan
Working in groups you will prepare a data collection plan for your project.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Project (applied)
Group project on design, collection and analysis of experimental data including identification of questions of interest; planning, data collection, handling of data; exploratory data analysis, analysis of data; reporting on findings, drawing conclusions.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Examination (invigilated)
Assessment based on work covered during the semester. A significant amount of the material will require the understanding of output from the computing package R.
The examination will require attendance 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 local 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 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.
Weisberg, S. (2005) Applied linear regression. 3rd edition, John Wiley & Sons, Inc., Hoboken, New Jersey.
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: Data Collection Plan, Project (applied), Examination (invigilated)
2: Engineering Application Ability
Relates to: Data Collection Plan, Project (applied), Examination (invigilated)
Relates to: Data Collection Plan, Project (applied), Examination (invigilated)
3: Professional and Personal Attributes
Relates to: Data Collection Plan, Project (applied), Examination (invigilated)
Relates to: Data Collection Plan, Project (applied)
Relates to: Data Collection Plan, Project (applied)