Introduction to Linear and Logistic Regression Models
An online short course
This course aims to provide an understanding of the statistical principles behind, and the practical application of, univariable and multivariable linear and logistic regression in medical, epidemiological and health services research.
|Course date||8 - 12 March 2021|
|Course Organisers||Dr Stephanie MacNeill|
Please ensure you meet the following prerequisites before booking:
|Knowledge||You should have knowledge of statistical methods and their implementation in Stata of at least the level achieved in the Introduction to Statistics short course.|
|Software||You must have Stata (version 15 or higher)* installed in advance of the course.
*Internal University of Bristol participants will be provided with access to Stata v16 on the first day of the course.
This 5-day course will be online and consist of a mix of live and pre-recorded sessions and practical work. Over the five days there will be approximately 14 hours of lectures and 15 hours of practicals.
By the end of the course, students will:
- have a thorough conceptual understanding of linear and logistic regression;
- appreciate the common threads running through these methods, including stratified analysis, different options for handling explanatory variables, and concepts such as confounding and interaction;
- have a working knowledge of the Stata commands to run these models, and a thorough understanding of the output generated from such a package;
- know the basis on which analytical strategy and model choice is made, and how the results should be interpreted.
Who the course is intended for
The course is intended for people analysing data from medical, epidemiological, and health services research, who have used simple methods such as t-tests and chi-square tests, and who now wish to use multivariable methods to control confounding, accommodate interaction, and increase statistical power.
The course will include:
- Revision of basic methods in a statistical modelling framework
- Terminology in regression modelling
- Indicator variables to incorporate categorical explanatory variables
- Univariable and multivariable regression
- Common features of linear and logistic regression models
- Interpretation of model coefficients as differences in means or odds ratios
- Adjustment for confounding and exploring effect modification (interaction) in multivariable regression
- Comparing models with Wald tests and likelihood ratio tests
- Model assumptions
- Analytical strategies
Online Course Bookings
Bookings are open for online courses running in 2021.
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We may need to make responsive changes to our courses at short notice in order to follow the latest Public Health, Government and University guidance on coronavirus (COVID-19).