Logistic Regression
Logistic regression underpins analytical decision-making where outcomes are uncertain and require classification. How variables are selected, modelled, and interpreted shapes the reliability of conclusions drawn from data. Application and evaluation of logistic regression therefore play a key role in supporting evidence-based decisions.
Technique Overview
Logistic Regression Definition
Logistic regression is a statistical modelling technique used to analyse the relationship between a categorical response variable and one or more explanatory variables. It estimates the probability of an outcome by modelling the log-odds of the response as a function of the predictors. Logistic regression may include multiple explanatory variables, allowing the combined influence of factors to be examined while supporting interpretation through probabilities and odds ratios (Hosmer, Lemeshow and Sturdivant, 2013; Sperandei, 2014).
Logistic Regression Description *
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Business Evidence
Strengths, weaknesses and examples of Logistic Regression *
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Business Application
Implementation, success factors and measures of Logistic Regression *
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Further Reading
Logistic Regression web and print resources *
Logistic Regression references (4 of up to 20) *
- Bewick, V., Cheek, L. and Ball, J. (2005). Statistics review 14: Logistic regression. Critical Care, [online] 9(1), p.112. doi:https://doi.org/10.1186/cc3045
- Sperandei, S. (2014). Understanding Logistic Regression Analysis. Biochemia Medica, [online] 24(1), pp.12–18. doi:https://doi.org/10.11613/bm.2014.003
- Stoltzfus, J.C. (2011). Logistic Regression: A Brief Primer. Academic Emergency Medicine, [online] 18(10), pp.1099–1104. doi:https://doi.org/10.1111/j.1553-2712.2011.01185.x
- Peng, C.-Y.J., Lee, K.L. and Ingersoll, G.M. (2002). An Introduction to Logistic Regression Analysis and Reporting. The Journal of Educational Research, [online] 96(1), pp.3–14. doi:https://doi.org/10.1080/00220670209598786
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