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. In particular, 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. In this unit, each topic is covered by a one hour statistics lecture, a one hour SAS lecture, a one hour SAS practical and a one hour statistics tutorial to discuss the interpretation of the results. Each fortnight there is an exercise on the material covered in the statistics lecture. The SAS practical covers the necessary statistical computing to answer the questions for the tutorial the following week. The assignments will involve practical analysis and interpretation of a data set. 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/distance mode. All students must have access to a computer with Microsoft Windows 7 or later and a good internet connection.


1x 4 page assignment (30%) and 1x 10 page assignment (70%)


Course notes are provided.

Faculty/department permission required?


Unit of study rules



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.