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DTSC71-302: Statistical Learning and Regression Models

Description

This subject covers the theory and practice of modern statistical learning, regression and classification modelling. Techniques covered range from traditional model selection and generalised linear model structures to modern, computer-intensive methods including generalised additive models, splines and tree methods. Methods to handle continuous, ordinal and nominal response variables and assessment of fit via cross-validation and residual diagnostics are also considered.  All techniques will be investigated via practical application on real data using the statistical software package R.

Subject details

Type: Postgraduate Subject
Code: DTSC71-302
EFTSL: 0.125
Faculty: Bond Business School
Semesters offered:
  • May 2022 [Standard Offering]
Credit: 10
Study areas:
  • Actuarial Science and Data Analytics
  • Business, Commerce, and Entrepreneurship
Subject fees:
  • Commencing in 2021: $5,110.00
  • Commencing in 2022: $5,170.00
  • Commencing in 2023: $5,300.00
  • Commencing in 2021: $5,570.00
  • Commencing in 2022: $5,570.00
  • Commencing in 2023: $5,710.00

Learning objectives

1. Demonstrate advanced knowledge of the limitations of linear regression models and the ability to develop an appropriate regression model. 2. Evaluate and choose between a variety of regression models. 3. Demonstrate advanced knowledge of the role of regularisation and the ability to use the concept to develop a variety of regression models. 4. Apply regression models for limited dependent variables (i.e., binomial, ordered and count data). 5. Apply generalised linear models, including proper use and assessment of model diagnostic techniques. 6. Develop regression models utilising splines, additive models and tree-based methods. 7. Correctly and concisely communicate the results and implications of a regression analysis in a professional written report.

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 ECON71-200 Linear Models and Applied Econometrics as well as basic data science concepts and techniques to the level of a unit such as DTSC71-200 Data Science

Restrictions:

Subject dates

  • Standard Offering
    Enrolment opens: 20/03/2022
    Semester start: 16/05/2022
    Subject start: 16/05/2022
    Cancellation 1: 30/05/2022
    Cancellation 2: 06/06/2022
    Last enrolment: 29/05/2022
    Withdraw - Financial: 11/06/2022
    Withdraw - Academic: 02/07/2022
    Teaching census: 10/06/2022
Standard Offering
Enrolment opens: 20/03/2022
Semester start: 16/05/2022
Subject start: 16/05/2022
Cancellation 1: 30/05/2022
Cancellation 2: 06/06/2022
Last enrolment: 29/05/2022
Withdraw - Financial: 11/06/2022
Withdraw - Academic: 02/07/2022
Teaching census: 10/06/2022