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INFT13-326: Statistical Learning and Regression Models

Description

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.

Subject details

TypeUndergraduate
CodeINFT13-326
EFTSL0.125
FacultyBond Business School
Semesters offered
  • September 2019 [Standard Offering]
Credit10
Study areas
  • Business and Commerce
Subject fees
  • Commencing in 2019: $4,290
  • Commencing in 2020: $4,340

Learning outcomes

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.

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

Restrictions: ?

This subject is not available as a general elective. To be eligible for enrolment, the subject must be specified in the students’ program structure.

Subject outlines

Subject dates

Standard Offering
Enrolment opens18/03/2019
Semester start13/05/2019
Subject start13/05/2019
Cancellation 1?27/05/2019
Cancellation 2?03/06/2019
Last enrolment26/05/2019
Withdraw – Financial?08/06/2019
Withdraw – Academic?29/06/2019
Teaching census?07/06/2019
Standard Offering
Enrolment opens14/07/2019
Semester start09/09/2019
Subject start09/09/2019
Cancellation 1?23/09/2019
Cancellation 2?30/09/2019
Last enrolment22/09/2019
Withdraw – Financial?05/10/2019
Withdraw – Academic?26/10/2019
Teaching census?04/10/2019