Advanced Modelling and Analytics
72 Hours
One Semester or equivalent
Hawthorn
Overview
This unit of study expands on earlier units in the course, introducing students to other aspects of statistical modelling and analytics. In particular, students will be introduced to models that allow for a variety of non-normal distributions and complex data structures.
Requisites
Teaching Periods
Location
Start and end dates
Last self-enrolment date
Census date
Last withdraw without fail date
Results released date
Study Period 3
Location
Hawthorn
Start and end dates
26-August-2024
24-November-2024
24-November-2024
Last self-enrolment date
08-September-2024
Census date
16-September-2024
Last withdraw without fail date
11-October-2024
Results released date
17-December-2024
Learning outcomes
Students who successfully complete this unit will be able to:
- Appraise the structure and nature of data to select an appropriate analysis technique
- Contrast a variety of advanced linear regression techniques allowing for various response distributions and managing missing data appropriately
- Apply various multi-level methods for the analysis of nominal and metric response variables.
- Evaluate and identify risk factors that contribute to survival outcomes
Teaching methods
Hawthorn Online
| Type | Hours per week | Number of weeks | Total (number of hours) |
|---|---|---|---|
| Live Online Class | 1.00 | 12 weeks | 12 |
| Online Directed Online Learning and Independent Learning | 11.50 | 12 weeks | 138 |
| TOTAL | 150 |
Assessment
| Type | Task | Weighting | ULO's |
|---|---|---|---|
| Assignment | Individual | 50% | 1,2,3,4 |
| Final-Semester Test | Individual | 40% | 1,2,3,4 |
| Online Quizzes | Individual | 10% | 1,2,3,4 |
Content
- Analysis of categorical response data:Â binary, ordinal, multinomial, loglinear models
- Generalized linear models (GLM)
- Survival analysis
- Multi-level modelling
- Generalized estimating equation (GEE)
- Mixed models
- Managing missing data
- Graduate Attribute 2 (Communication 2 - Communicating using different media)
- Graduate Attribute 5 (Digital Literacies 1 - Information literacy)
- Graduate Attribute 6 (Digital Literacies 2 - Technical literacy)
Study resources
Reading materials
A list of reading materials and/or required textbooks will be available in the Unit Outline on Canvas.