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:
- 10.00am - 11.30am: Instructional session
- 12.30pm - 2.00pm: Instructional session
- 3.00pm - 4.30pm: Exercises and discussion
Please note: Courses will run on Australian Eastern Daylight Time (GMT +11)
Dr Joanna Dipnall is an applied statistician with interests in the advanced statistical methods, including machine learning and deep learning techniques. She completed her Honours in Econometrics with Monash University and her PhD with IMPACT SRC, School of Medicine, Deakin University. Joanna works extensively with registry and linked medical data and collaborates extensively with the Faculty of IT at Monash to supervise Masters and PhD students to integrate artificial intelligence within health research. Joanna teaches within the Monash Biostatistics Unit and is the Unit Co-coordinator for the Monash Masters of Health Data Analytics course. Joanna has taught advanced statistical methods for many years at universities and for ACSPRI.
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.
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.
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.
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.
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.