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.
|Faculty||Bond Business School|
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.
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 Econometrics as well as basic data science concepts and techniques to the level of a unit such as INFT12-216 Data Science
This subject is not available as a general elective. To be eligible for enrolment, the subject must be specified in the students’ program structure.
|Withdraw – Financial?||08/06/2019|
|Withdraw – Academic?||29/06/2019|
|Withdraw – Financial?||05/10/2019|
|Withdraw – Academic?||26/10/2019|