You are viewing this page as a domestic student.
Change to International

You are a domestic student if you are an Australian citizen, a New Zealand citizen or the holder of an Australian permanent visa.

You are an international student whether you are within or outside Australia and you do not meet the domestic student criteria.

COVID-19 (coronavirus): Latest advice for the Bond community.

DTSC13-302: Statistical Learning and Regression Models May 2020 [Standard]

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.


Academic unit:Bond Business School
Subject code:DTSC13-302
Subject title:Statistical Learning and Regression Models
Subject level:Undergraduate
Semester/Year:May 2020
Credit points:10

Delivery & attendance

Delivery mode:


Workload items:
  • Computer Lab: x12 (Total hours: 24) - Laboratory
  • Lecture: x12 (Total hours: 24) - Weekly Lecture
  • Personal Study Hours: x12 (Total hours: 72) - Study time and 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.


Prescribed resources:
  • James, Witten, Hastie and Tibshirani (2013). An Introduction to Statistical Learning with Applications in R. [PDF file] Springer.
  • Faraway (2006). Extending the linear model with R. [PDF file] Chapman & Hall/CRC.
  • Hastie, Tibshirani and Friedman (2009). The Elements of Statistical Learning. [PDF file] Springer.
After enrolment, students can check the Books and Tools area in iLearn for the full Resource List.
[email protected] & Email:[email protected] is the online learning environment at Bond University and is used to provide access to subject materials, lecture 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

Enrolment requirements

Requisites: ?


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.

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 Econometrics as well as basic data science concepts and techniques to the level of a unit such as INFT12-216 Data Science

Restrictions: ?


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.

Find your program

Subject Learning Outcomes (SLOs)

On successful completion of this subject the learner will be able to:
  1. Demonstrate knowledge of the limitations of linear regression models and the ability to develop an appropriate regression model given the circumstance.
  2. Evaluate and choose between a variety of regression models.
  3. Demonstrate knowledge of the role of regularization and the ability to use the concept to develop a variety of regression models.
  4. Estimate limited dependent variable regression models.
  5. Estimate generalised linear models, random effects models and mixed effects models.
  6. Develop regression models utilising splines, kernals, polynomials and additive-methods.


Assessment details

TypeTask%Timing*Outcomes assessed
Capstone Project § Group Project: Part 1 10% Week 1 1, 2, 3.
Capstone Project § Group Project: Part 2 20% Week 1 4, 5, 6.
Computer-Aided Examination (Open) Final Examination 45% Final Examination Period 1, 2, 3, 4, 5, 6.
Computer-Aided Examination (Open) Mid-semester Examination 25% Week 7 (Mid-Semester Examination Period) 1, 2, 3.
  • § Indicates group/teamwork-based assessment
  • * 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 [email protected] 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 in writing by the lead educator, a penalty of 10% per day late starts from the new due date.

Policy on plagiarism

University’s Academic Integrity Policy defines plagiarism as the act of misrepresenting as one’s own original work: another’s ideas, interpretations, words, or creative works; and/or one’s own previous ideas, interpretations, words, or creative work without acknowledging that it was used previously (i.e., self-plagiarism). The University considers the act of plagiarising to be a breach of the Student Conduct Code and, therefore, subject to the Discipline Regulations which provide for a range of penalties including the reduction of marks or grades, fines and suspension from the University.

Bond University utilises Originality Reporting software to inform academic integrity.

Feedback on assessment

Feedback on assessment will be provided to students within two weeks of the assessment submission due date, as per the Assessment Policy.

Accessibility and Inclusion Support

If you have a disability, illness, injury or health condition that impacts your capacity to complete studies, exams or assessment tasks, it is important you let us know your special requirements, early in the semester. Students will need to make an application for support and submit it with recent, comprehensive documentation at an appointment with a Disability Officer. Students with a disability are encouraged to contact the Disability Office at the earliest possible time, to meet staff and learn about the services available to meet your specific needs. Please note that late notification or failure to disclose your disability can be to your disadvantage as the University cannot guarantee support under such circumstances.

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. A peer-evaluation system will be used in this subject to help determine the individual marks for all group assessments. 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

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

1, 2.

This topic covers the issue of selection of appropriate covariates in the presence of multicollinearity via investigation of various subset selection methods.

1, 2.

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)


This topic covers OLS and WLS methods, probit, cloglog and logistic regression and applications as well as binomial discrimination for models of dichotomous outcomes


This topic covers Poisson, negative binomial and zero-inflated models for integer-valued outcomes


Topics 1 - 5

This topic covers models for integer-valued outcomes including contingency tables, multinomial models and ordered-response models


This topic covers extensions to the standard linear mode structure to include non-linear links, exponential family error structure and quasi-likelihood.

2, 5.

This topic covers non-linear models including polynomial regressions, smoothing splines and local regression methods

2, 6.

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)

Approved on: Apr 29, 2020. Edition: 1.1