Linear & Logistic Regression

Subject POPH90144 (2011)

Note: This is an archived Handbook entry from 2011.

Credit Points: 12.50
Level: 9 (Graduate/Postgraduate)
Dates & Locations:

This subject has the following teaching availabilities in 2011:

July, Parkville - Taught on campus.
Pre-teaching Period Start not applicable
Teaching Period not applicable
Assessment Period End not applicable
Last date to Self-Enrol not applicable
Census Date not applicable
Last date to Withdraw without fail not applicable


Timetable can be viewed here. For information about these dates, click here.
Time Commitment: Contact Hours: 24 hours. One 4-hour seminar and practical session per week over the first 6 weeks of semester
Total Time Commitment: Students will be expected to undertake additional tasks, reading and preparation equivalent to an average of 80 to 90 hours of additional time commitment
Prerequisites: Students must have either POPH90142 and POPH90143 OR POPH90014 and POPH90013
Study Period Commencement:
Credit Points:
Semester 1
Corequisites: None
Recommended Background Knowledge: Special computer skills required: Students are expected to have experience using the Stata statistical package
Non Allowed Subjects: None
Core Participation Requirements: For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Students Experiencing Academic Disadvantage Policy, academic requirements for this subject are articulated in the Subject Description, Subject Objectives, Generic Skills and Assessment Requirements of this entry.
The University is dedicated to provide support to those with special requirements. Further details on the disability support scheme can be found at the Disability Liaison Unit website.


Assoc Prof Julie Simpson


Centre for Molecular, Environmental, Genetic and Analytic (MEGA) Epidemiology
Melbourne School of Population Health
Tel: +61 3 8344 0671


Academic Programs Office
Melbourne School of Population Health
Tel: +61 3 8344 9339
Fax: +61 3 8344 0824

Subject Overview:

This subject is compulsory for students doing a Master of Epidemiology or a Master of Science – Epidemiology. The subject covers linear regression methods for continuous outcome variables and logistic regression methods for binary outcome variables. The subject equips students with the practical skills to apply these regression methods to data from epidemiological studies using the statistical package Stata. Also covered is how to adjust for confounding and investigate effect modification using regression models. The focus is on the practical interpretation of the measures of association estimated by these regression models.

Objectives: On completion of this subject, students are expected to:
  • Recognise when it is appropriate to use linear and logistic regression models
  • Demonstrate practical skills in fitting linear and logistic regression models in the statistical computing package, Stata.
  • Interpret the measures of association (mean differences and odds ratios) estimated by linear and logistic regression models.
  • Describe and demonstrate how to adjust for confounding and identify variables that modify measures of association using these regression methods.

A written assignment (involving calculations and diagrams) of not more than 8 pages due at the start of the 4th week of the subject (30%), a written assignment of not more than 10 pages due two weeks after the intensive delivery period (40%) and a 1.5-hour open-book examination (administered by the School) to be held during the examination period at the end of semester 2 (30%).

Prescribed Texts: BR Kirkwood and JAC Sterne, Essential Medical Statistics Second Edition, Blackwell Science, 2003.
Breadth Options:

This subject is not available as a breadth subject.

Fees Information: Subject EFTSL, Level, Discipline & Census Date
Generic Skills: Upon completion of this subject, students will have developed skills in:

  • Critical thinking and analysis,
  • Finding, evaluating and using relevant information,
  • Problem-solving,
  • Written communication,
  • Using computers.
Links to further information:

Related Course(s): Master of Epidemiology
Master of Public Health
Master of Science (Epidemiology)
Related Majors/Minors/Specialisations: Epidemiology and Biostatistics
Gender && Women's Health
Health Economics && Economic Evaluation
Public Health

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