Advanced Topics in Regression - 2017

To enrol in this unit, you must be accepted into a course from the provider. Read before you start

Unit summary

STA80004

  • Level of Study: Postgraduate
  • Study load: 0.125 EFTSL
  • Delivery method: Fully Online
  • Prerequisites: Yes
  • Duration: 13 weeks
  • Government loans available: FEE-HELP
  • Availability for 2016: SP1
  • Availability for 2017: SP1
  • Assessment: Assignments (40%) , Invigilated Exam (50%) - Learn more

Unit provided by

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2017 Fees
AUD$
Domestic 2,600.00
Domestic continuing 2,472.00
International 2,850.00
International continuing 2,722.00

2017 Swinburne tuition fees are reduced for continuing postgraduate students.
Please login in to view the applicable amount.

This unit provides an introduction to more advanced modeling techniques, including nonlinear regression, ordinal and multinomial regression, logistic regression, multi-level regression analysis and survival analysis.

After completing this unit of study you will be able to: 
  1. Appraise how the type of data and the nature of the research questions affects the appropriate analysis methodology
  2. Contrast a range of advanced regression techniques, testing assumptions appropriately
  3. Evaluate the results obtained from log-linear analyses
  4. Assess logistic regression analyses for binary, ordinal and nominal response variables
  5. Contrast Multi-level Modelling analyses for nested and repeated measures data for nominal and metric response variables
  6. Formulate and report on appropriate Survival Analysis models using Cox Regression and Time Dependent Covariates
  7. Identify the most appropriate method of regression analysis in any particular research context.
  • Assignments (40%)
  • Invigilated Exam (50%)
  • Quizzes (10%)
For more information on invigilated exams see Exams and results

Equivalent units

You cannot enrol in this unit if you have successfully completed any of the following unit(s) because they are considered academically equivalent:

  • HMS793 — Advanced Topics in Regression

Mandatory prerequisites

You must have successfully completed the following unit(s) before starting this unit:

If you have completed equivalent study at another university, please contact a Student Advisor for advice.

  • Broadband access
  • Other special requirement — Students require access to a recent version of SPSS - Grad Pak at the least.

In order to enrol in this unit, you must be accepted into one of the following courses:

Please visit the course details page and read the Requirements tab for more information about eligibility.

This unit addresses the following topics.

NumberTopic
1Regression methods
2Non-linear regression
3Loglinear models for investigating relationships in categorical data
4Logistic regression for binary, nominal and ordinal data
5Introduction to multi-level modeling
6Survival analysis

This unit is delivered using the following methods and materials:

Instructional Methods

  • Discussion Forum/Discussion Board
  • Online Quizzes/Tests
  • Online assignment submission
  • Podcasting/Lecture capture
  • Standard Media
  • Web links

Print based materials

  • Welcome Letter

Online materials

  • Printable format materials

This unit is a core requirement in the following courses:

This unit may be eligible for credit towards other courses:

  1. Many undergraduate courses on offer through OUA include 'open elective' where any OUA unit can be credited to the course. You need to check the Award Requirements on the course page for the number of allowed open electives and any level limitations.
  2. In other cases, the content of this unit might be relevant to a course on offer through OUA or elsewhere. In order to receive credit for this unit in the course you will need to supply the provider institution with a copy of the Unit Profile in the approved format, which you can download here. Note that the Unit Profile is set at the start of the year, and if textbooks change this may not match the Co-Op textbook list.
This unit does not have a prescribed textbook(s).

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