General Information
The theory and practice of advanced regression techniques is the focus of this subject. Topics such as regularisation, limited dependent variable models, generalised linear models, random and mixed effects models, splines, additive models and tree-based regression will be covered. The programming language R will be used in this course.
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Details
Academic unit: Bond Business School Subject code: DTSC13-302 Subject title: Statistical Learning and Regression Models Subject level: Undergraduate Semester/Year: May 2023 Credit points: 10.000 -
Delivery & attendance
Timetable: https://bond.edu.au/timetable Delivery mode: Standard Workload items: - Forum: x12 (Total hours: 24) - Forum
- Computer Lab: x12 (Total hours: 24) - Computer Lab
- Personal Study Hours: x12 (Total hours: 72) - Recommended study time & reviewing materials
Attendance and learning activities: Attendance at all class sessions is expected. Students are expected to notify the instructor of any absences with as much advance notice as possible. -
Resources
Prescribed resources: Others
- James, Witten, Hastie and Tibshirani (2013). An Introduction to Statistical Learning with Applications in R. Springer Available at: http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Sixth%20Printing.pdf
- Faraway (2006). Extending the linear model with R. Chapman & Hall/CRC Available at: http://www.ievbras.ru/ecostat/Kiril/R/Biblio/R_eng/Faraway%20-%20Extending%20the%20Linear%20Model%20with%20R%20%96%202006.pdf
- Hastie, Tibshirani and Friedman (2009). The Elements of Statistical Learning. Springer Available at: http://statweb.stanford.edu/~tibs/ElemStatLearn/
iLearn@Bond & Email: iLearn@Bond is the Learning Management System at Bond University and is used to provide access to subject materials, class recordings and detailed subject information regarding the subject curriculum, assessment, and timing. Both iLearn and the Student Email facility are used to provide important subject notifications.
Additionally, official correspondence from the University will be forwarded to students’ Bond email account and must be monitored by the student.
To access these services, log on to the Student Portal from the Bond University website as www.bond.edu.au
| Academic unit: | Bond Business School |
|---|---|
| Subject code: | DTSC13-302 |
| Subject title: | Statistical Learning and Regression Models |
| Subject level: | Undergraduate |
| Semester/Year: | May 2023 |
| Credit points: | 10.000 |
| Timetable: | https://bond.edu.au/timetable |
|---|---|
| Delivery mode: | Standard |
| Workload items: |
|
| Attendance and learning activities: | Attendance at all class sessions is expected. Students are expected to notify the instructor of any absences with as much advance notice as possible. |
| Prescribed resources: | Others
|
|---|---|
| iLearn@Bond & Email: | iLearn@Bond is the Learning Management System at Bond University and is used to provide access to subject materials, class recordings and detailed subject information regarding the subject curriculum, assessment, and timing. Both iLearn and the Student Email facility are used to provide important subject notifications. Additionally, official correspondence from the University will be forwarded to students’ Bond email account and must be monitored by the student. To access these services, log on to the Student Portal from the Bond University website as www.bond.edu.au |
Enrolment requirements
| Requisites: |
Nil |
|---|---|
| Assumed knowledge: |
Assumed knowledge is the minimum level of knowledge of a subject area that students are assumed to have acquired through previous study. It is the responsibility of students to ensure they meet the assumed knowledge expectations of the subject. Students who do not possess this prior knowledge are strongly recommended against enrolling and do so at their own risk. No concessions will be made for students’ lack of prior knowledge. Assumed Prior Learning (or equivalent):Possess demonstrable knowledge in the theory and application of simple and multiple linear regression models to the level of a unit such as ECON12-200 Linear Models and Applied Econometrics as well as basic data science concepts and techniques to the level of a unit such as DTSC12-200 Data Science |
| Restrictions: |
Nil |
Assurance of learning
Assurance of Learning means that universities take responsibility for creating, monitoring and updating curriculum, teaching and assessment so that students graduate with the knowledge, skills and attributes they need for employability and/or further study.
At Bond University, we carefully develop subject and program outcomes to ensure that student learning in each subject contributes to the whole student experience. Students are encouraged to carefully read and consider subject and program outcomes as combined elements.
Program Learning Outcomes (PLOs)
Program Learning Outcomes provide a broad and measurable set of standards that incorporate a range of knowledge and skills that will be achieved on completion of the program. If you are undertaking this subject as part of a degree program, you should refer to the relevant degree program outcomes and graduate attributes as they relate to this subject.
Subject Learning Outcomes (SLOs)
On successful completion of this subject the learner will be able to:
- Demonstrate knowledge of the limitations of normal linear regression models and the ability to appropriately interpret linear model outputs considering these limitations.
- Evaluate and appropriately choose between applications of a variety of linear and generalised linear regression modelling structures, including regularisation, dimension reduction and sequential variable selection.
- Apply non-normal dependent variable regression and classification models and identify their structural differences.
- Develop prediction models utilising splines, polynomials, recursive partitioning and general additive methods.
- Demonstrate ability to produce creative analytic solutions addressing a specified issue or problem.
- Demonstrate ability to verbally communicate the results of a statistical learning investigation.
Generative Artificial Intelligence in Assessment
The University acknowledges that Generative Artificial Intelligence (Gen-AI) tools are an important facet of contemporary life. Their use in assessment is considered in line with students’ development of the skills and knowledge which demonstrate learning outcomes and underpin study and career success. Instructions on the use of Gen-AI are given for each assessment task; it is your responsibility to adhere to these instructions.
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Assessment details
Type Task % Timing* Outcomes assessed Computer-Aided Examination (Open) Final Examination 45.00% Final Examination Period 1,2,3,4,5 Computer-aided Test (Open) Mid-semester Test – Practical, computer-based analysis of provided datasets covering techniques presented to date. 25.00% Week 7 1,2,3 Project Individual Project: Written Report 15.00% In Consultation 1,2,3,4,5 Project Individual Project: Video Presentation 15.00% In Consultation 6 - * Assessment timing is indicative of the week that the assessment is due or begins (where conducted over multiple weeks), and is based on the standard University academic calendar
- C = Students must reach a level of competency to successfully complete this assessment.
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Assessment criteria
Assessment criteria
High Distinction 85-100 Outstanding or exemplary performance in the following areas: interpretative ability; intellectual initiative in response to questions; mastery of the skills required by the subject, general levels of knowledge and analytic ability or clear thinking. Distinction 75-84 Usually awarded to students whose performance goes well beyond the minimum requirements set for tasks required in assessment, and who perform well in most of the above areas. Credit 65-74 Usually awarded to students whose performance is considered to go beyond the minimum requirements for work set for assessment. Assessable work is typically characterised by a strong performance in some of the capacities listed above. Pass 50-64 Usually awarded to students whose performance meets the requirements set for work provided for assessment. Fail 0-49 Usually awarded to students whose performance is not considered to meet the minimum requirements set for particular tasks. The fail grade may be a result of insufficient preparation, of inattention to assignment guidelines or lack of academic ability. A frequent cause of failure is lack of attention to subject or assignment guidelines. Quality assurance
For the purposes of quality assurance, Bond University conducts an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.
| Type | Task | % | Timing* | Outcomes assessed |
|---|---|---|---|---|
| Computer-Aided Examination (Open) | Final Examination | 45.00% | Final Examination Period | 1,2,3,4,5 |
| Computer-aided Test (Open) | Mid-semester Test – Practical, computer-based analysis of provided datasets covering techniques presented to date. | 25.00% | Week 7 | 1,2,3 |
| Project | Individual Project: Written Report | 15.00% | In Consultation | 1,2,3,4,5 |
| Project | Individual Project: Video Presentation | 15.00% | In Consultation | 6 |
- * Assessment timing is indicative of the week that the assessment is due or begins (where conducted over multiple weeks), and is based on the standard University academic calendar
- C = Students must reach a level of competency to successfully complete this assessment.
Assessment criteria
| High Distinction | 85-100 | Outstanding or exemplary performance in the following areas: interpretative ability; intellectual initiative in response to questions; mastery of the skills required by the subject, general levels of knowledge and analytic ability or clear thinking. |
|---|---|---|
| Distinction | 75-84 | Usually awarded to students whose performance goes well beyond the minimum requirements set for tasks required in assessment, and who perform well in most of the above areas. |
| Credit | 65-74 | Usually awarded to students whose performance is considered to go beyond the minimum requirements for work set for assessment. Assessable work is typically characterised by a strong performance in some of the capacities listed above. |
| Pass | 50-64 | Usually awarded to students whose performance meets the requirements set for work provided for assessment. |
| Fail | 0-49 | Usually awarded to students whose performance is not considered to meet the minimum requirements set for particular tasks. The fail grade may be a result of insufficient preparation, of inattention to assignment guidelines or lack of academic ability. A frequent cause of failure is lack of attention to subject or assignment guidelines. |
Quality assurance
For the purposes of quality assurance, Bond University conducts an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.
Study Information
Submission procedures
Students must check the iLearn@Bond subject site for detailed assessment information and submission procedures.
Policy on late submission and extensions
A late penalty will be applied to all overdue assessment tasks unless an extension is granted by the Lead Educator. The standard penalty will be 10% of marks awarded to that assessment per day late with no assessment to be accepted seven days after the due date. Where a student is granted an extension, the penalty of 10% per day late starts from the new due date.
Academic Integrity
Bond University‘s Student Code of Conduct Policy , Student Charter, Academic Integrity Policy and our Graduate Attributes guide expectations regarding student behaviour, their rights and responsibilities. Information on these topics can be found on our Academic Integrity webpage recognising that academic integrity involves demonstrating the principles of integrity (honesty, fairness, trust, professionalism, courage, responsibility, and respect) in words and actions across all aspects of academic endeavour.
Staff are required to report suspected misconduct. This includes all types of plagiarism, cheating, collusion, fabrication or falsification of data/content or other misconduct relating to assessment such as the falsification of medical certificates for assessment extensions. The longer term personal, social and financial consequences of misconduct can be severe, so please ask for help if you are unsure.
If your work is subject to an inquiry, you will be given an opportunity to respond and appropriate support will be provided. Academic work under inquiry will not be marked until the process has concluded. Penalties for misconduct include a warning, reduced grade, a requirement to repeat the assessment, suspension or expulsion from the University.
Feedback on assessment
Feedback on assessment will be provided to students according to the requirements of the Assessment Procedure Schedule A - Assessment Communication Procedure.
Whilst in most cases feedback should be provided within two weeks of the assessment submission due date, the Procedure should be checked if the assessment is linked to others or if the subject is a non-standard (e.g., intensive) subject.
Accessibility and Inclusion Support
Support is available to students where a physical, mental or neurological condition exists that would impact the student’s capacity to complete studies, exams or assessment tasks. For effective support, special requirement needs should be arranged with the University in advance of or at the start of each semester, or, for acute conditions, as soon as practicable after the condition arises. Reasonable adjustments are not guaranteed where applications are submitted late in the semester (for example, when lodged just prior to critical assessment and examination dates).
As outlined in the Accessibility and Inclusion Policy, to qualify for support, students must meet certain criteria. Students are also required to meet with the Accessibility and Inclusion Advisor who will ensure that reasonable adjustments are afforded to qualifying students.
For more information and to apply online, visit BondAbility.
Additional subject information
The delivery of this subject will include the use of the R programming language, which is fully open-source. RStudio is the recommended front-end and is also freely available. As part of the requirements for Business School quality accreditation, the Bond Business School employs an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.
Subject curriculum
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Review of statistical learning and linear regression
This topic covers basics of the multiple linear regression model and the principles of statistical learning including the concepts of loss functions, bias-variance trade-off, model fit and model diagnostics.
SLOs included
- Demonstrate knowledge of the limitations of normal linear regression models and the ability to appropriately interpret linear model outputs considering these limitations.
- Evaluate and appropriately choose between applications of a variety of linear and generalised linear regression modelling structures, including regularisation, dimension reduction and sequential variable selection.
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Variable selection in linear regression
This topic covers the issue of selection of appropriate covariates in the presence of multicollinearity via investigation of various subset selection methods.
SLOs included
- Demonstrate knowledge of the limitations of normal linear regression models and the ability to appropriately interpret linear model outputs considering these limitations.
- Evaluate and appropriately choose between applications of a variety of linear and generalised linear regression modelling structures, including regularisation, dimension reduction and sequential variable selection.
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Regularisation methods for linear regression
This topic covers variable selection methods based on regularisation (i.e, Ridge and LASSO regression) or dimension reduction techniques (principal components regression and partial least squares)
SLOs included
- Apply non-normal dependent variable regression and classification models and identify their structural differences.
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Binomial regression and models for dichotomous outcomes
This topic covers OLS and WLS methods, probit, cloglog and logistic regression and applications as well as binomial discrimination for models of dichotomous outcomes.
SLOs included
- Develop prediction models utilising splines, polynomials, recursive partitioning and general additive methods.
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Count regression
This topic covers Poisson, negative binomial and zero-inflated models for integer-valued outcomes.
SLOs included
- Develop prediction models utilising splines, polynomials, recursive partitioning and general additive methods.
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Ordinal and multinomial dependent variable models
This topic covers models for integer-valued outcomes including contingency tables, multinomial models and ordered-response models.
SLOs included
- Develop prediction models utilising splines, polynomials, recursive partitioning and general additive methods.
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Generalised linear models
This topic covers extensions to the standard linear mode structure to include non-linear links, exponential family error structure and quasi-likelihood.
SLOs included
- Evaluate and appropriately choose between applications of a variety of linear and generalised linear regression modelling structures, including regularisation, dimension reduction and sequential variable selection.
- Demonstrate ability to produce creative analytic solutions addressing a specified issue or problem.
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Polynomials and splines
This topic covers non-linear models including polynomial regressions, smoothing splines and local regression methods.
SLOs included
- Evaluate and appropriately choose between applications of a variety of linear and generalised linear regression modelling structures, including regularisation, dimension reduction and sequential variable selection.
- Demonstrate ability to verbally communicate the results of a statistical learning investigation.
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Additive models and tree-based regression
This topic covers models with non-linear and interactive structure via the application of generalised additive models (GAMs) and classification and regresssion tree-models (CART)
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Robust and quantile regression
This topic explores alternative loss function structures to guard against the adverse effects of outliers and includes a shift of focus from modelling expected values to modelling percentiles such as the median.