Introduction to General Linear Models (GLM): Online - (2 days)

This course is designed as an introduction to general linear models (GLMs).

GLMs give you a common way to specify different models using a common procedure, thereby enabling researchers to easily adjust models to allow for different types of outcomes (e.g., non-Gaussian or discrete).



This course will be offered online via Zoom And will run to the following timetable:


  • 9.30am -11.00am: Instructional Zoom session
  • 11.00am-11.30am: Break
  • 11.30am-12.30pm: Instructional Zoom session
  • 12:30pm-1.30pm: Lunch
  • 1.30pm-3.00pm: Instructional Zoom session
  • 3.00pm-3.30pm: Break
  • 3.30pm-4.30pm:  Exercises and discussion


Please note: Courses will run on Australian Eastern Standard Time (GMT +10)



Master Class - runs over 2 days

Dr Joanna Dipnall is a biostatistician with the School of Public Health and Preventative Medicine (SPHPM) at Monash University and Honorary Research Fellow with School of Medicine at Deakin University. She holds a B.Ec(Honours) from Monash University, and a PhD from the School of Medicine at Deakin University. She also lectures and tutors with the Department of Statistics, Data Science and Epidemiology at Swinburne University. Joanna has developed a novel Risk Index for Depression (RID) utilising SEM and machine learning techniques that brought together five key determinants of depression. She has been a teacher of Stata software for over 15 years, training across Australia and overseas and was a member of the Scientific Committee for the Oceania Stata Users Group Meeting in 2017.

About this course: 

GLMs give you a common way to specify different models using a common procedure thereby enabling researchers to easily adjust models to allow for different types of outcomes (e.g., non-Gaussian or discrete).


There are many occasions when researchers encounter binary and count outcomes, and the GLM enables them to test different models for their outcome. These models have been widely used in areas such as pricing techniques in the insurance industry and research on remote working of IT and E-Commerce industry employees during the Coronavirus (Covid-19) Pandemic.


This course will provide participants with the ability to identify the need to use a GLM for their analysis and the correct interpretation and checking their models.


Discussion of some of the uses of GLMs in publications will be discussed at the end of the course.


Course syllabus: 

This course is broken up into the following sections:


Part I: Introduction to GLMs
Part II: Models for Binary Data
Part III: Models for Count Data
Part IV: GLM with the Gamma distribution
Part V: GLMs in Publications


Participants will be given time to do some exercises on their own to practise what they have learned.


Exercises and solutions will be provided in Stata, R and Python software.


Course format: 

This workshop will take place online using Zoom.

You will need your own computer with Stata, or Python and/or R installed, and an internet connection.

A second screen/monitor is recommended.

Recommended Background: 

This course assumes that participants have:


(1) Sound familiarity with at least one of the three software packages Stata, R and/or Python.

(2) sufficient understanding of statistics to be able to comprehend the material covered in the course outline, such as a basic grounding in multiple regression (e.g., linear, logistic, Poisson).

(3) access to either Stata or R and/or Python.

(4) some experience in using Microsoft Word and Excel or their equivalent.

(5) experience using a text editor such as Notepad.


Recommended Texts: 

Course notes will be supplied. Please include a shipping address when you enrol. Your notes will be express posted to this address.


No specific references are suggested but a number will be supplied with the notes handed out for the course.

Program where course next likely to be offered: 
Master-class November 2021: General Linear Models: Online