Biochemia Medica· 2014Q2· Review
Understanding logistic regression analysis
- 1,383citations
- Q2SCImago
- 2014year
Short summary
Logistic regression models the impact of multiple explanatory variables on the odds ratio of a binomial outcome, effectively controlling for confounding effects.
AI-generated from the title and abstract; the full text is not read.
Key points
- Logistic regression calculates odds ratios with multiple explanatory variables.
- The response variable in logistic regression is binomial.
- It quantifies the impact of each variable on the odds ratio of an event.
- The method avoids confounding by analyzing variables together.
AI-generated from the title and abstract; the full text is not read.
Abstract
Logistic regression is used to obtain odds ratio in the presence of more than one explanatory variable. The procedure is quite similar to multiple linear regression, with the exception that the response variable is binomial. The result is the impact of each variable on the odds ratio of the observed event of interest. The main advantage is to avoid confounding effects by analyzing the association of all variables together. In this article, we explain the logistic regression procedure using examples to make it as simple as possible. After definition of the technique, the basic interpretation of the results is highlighted and then some special issues are discussed.
The authors' abstract, as published at the source. Biochemia Medica, 2014 · DOI ↗
Continue with a free account
Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.
Continue free on the webSign in with Google or Apple; no card needed. You come back to this paper.
On your phone:
Field: Control and Systems Engineering
Control and Systems EngineeringEngineering