Multiple Regression and Stats Computing (PUBH5211)


Students will learn how to analyse data using multiple linear regression. Multiple linear regression is a powerful statistical method for analysing a continuous outcome variable with several explanatory variables. This unit will cover how to compare more than two groups, adjust for confounders, test for effect modification, calculate adjusted means, conduct appropriate model checking, and teaches strategies for selecting the 'best' regression model. Students will learn how to apply these methods using the statistical package called SAS. The focus of this unit is on the application of fitting appropriate linear regression models and interpreting the results. The material in this unit is covered by lectures, tutorials, course notes and online discussions. This unit is the prerequiste for learning other types of regression models, such as logistic regression (PUBH5212) and survival analysis (PUBH5213).

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Further unit of study information


2hrs per week for 13 weeks. This unit may be undertaken in face to face or online mode. All students must have regular access to a reliable internet connection capable of streaming or downloading video recorded lectures.


Quizzes (10%); 1x 4 page assignment (20%); and 1x 10 page assignment (70%). The assignments will involve analysing data. Students must pass the final assignment to pass this unit of study.


Course notes are provided.

Faculty/department permission required?


Unit of study rules



Additional Information

The statistical software package we shall be using in this unit is web-based. There is no cost/fee to use this software.

Study this unit outside a degree

Non-award/non-degree study

If you wish to undertake one or more units of study (subjects) for your own interest but not towards a degree, you may enrol in single units as a non-award student.

Cross-institutional study

If you are from another Australian tertiary institution you may be permitted to underake cross-institutional study in one or more units of study at the University of Sydney.