Categorical Data & GLMs

Subject POPH90121 (2016)

Note: This is an archived Handbook entry from 2016.

Credit Points: 12.5
Level: 9 (Graduate/Postgraduate)
Dates & Locations:

This subject has the following teaching availabilities in 2016:

Semester 2, Parkville - Taught online/distance.
Pre-teaching Period Start not applicable
Teaching Period 25-Jul-2016 to 23-Oct-2016
Assessment Period End 18-Nov-2016
Last date to Self-Enrol 05-Aug-2016
Census Date 31-Aug-2016
Last date to Withdraw without fail 23-Sep-2016

This subject is only available to students who are currently enrolled in the Graduate Diploma or Master of Biostatistics and whose enrolment in that course commenced prior to 2016.



Timetable can be viewed here. For information about these dates, click here.
Time Commitment: Contact Hours: None
Total Time Commitment:

170 hours

Prerequisites:

505-940 Linear Models (LMR) (may be taken concurrently)

Subject
Study Period Commencement:
Credit Points:
Semester 1, Semester 2
12.50
Semester 1, Semester 2
12.50
Semester 1, Semester 2
12.50
Semester 1, Semester 2
12.50
Corequisites:

None

Recommended Background Knowledge:

None

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.

Coordinator

Prof John Carlin

Contact

john.carlin@unimelb.edu.au

Melbourne School of Population and Global Health

OR

Currently enrolled students:

Future Students:

Subject Overview:

Introduction to and revision of conventional methods for contingency tables especially in epidemiology: odds ratios and relative risks, chi-squared tests for independence, Mantel-Haenszel methods for stratified tables, and methods for paired data. The exponential family of distributions; generalized linear models (GLMs), and parameter estimation for GLMs. Inference for GLMs – including the use of score, Wald and deviance statistics for confidence intervals and hypothesis tests, and residuals. Binary variables and logistic regression models – including methods for assessing model adequacy. Nominal and ordinal logistic regression for categorical response variables with more than two categories. Count data, Poisson regression and log-linear models.

Learning Outcomes:

To enable students to use generalised linear models (GLMs) and other methods to analyse categorical data with proper attention to the underlying assumptions. There is an emphasis on the practical interpretation and communication of results to colleagues and clients who may not be statisticians.

Assessment:

Two written assignments due before the end of semester worth 35% each (approx 8 hours work each).

One written assignment due before the end of semester worth 30% (approx 7 hrs work each)

Prescribed Texts:

None

Special Computer Requirements: Stata statistical software

Resources Provided to Students: Printed course notes and assignment material will be provided to students by mail (including electronic media).

Recommended Texts:

None

Breadth Options:

This subject is not available as a breadth subject.

Fees Information: Subject EFTSL, Level, Discipline & Census Date
Generic Skills:

Independent problem solving, facility with abstract reasoning, clarity of written expression, sound communication of technical concepts.

Links to further information: http://www.mspgh.unimelb.edu.au
Notes:

This subject is not available in the Master of Public Health.

Related Course(s): Graduate Certificate in Biostatistics
Postgraduate Diploma in Biostatistics

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