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Logistic Regression/Evidence
Method evidence record

Logistic Regression

Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.

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Source record

Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.

Binary Logistic Regression
Taxonomic method record · process-pipeline / research-statistics
  • Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. · DOI 10.1111/j.2517-6161.1958.tb00292.x
  • Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied Logistic Regression (3rd ed.). John Wiley & Sons. · DOI 10.1002/9781118548387
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Related methods

Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.

Same method familyMultiple Regression Analysismachine-suggested · Relational suggestion, not evidence.Same method familyPropensity Score Matchingmachine-suggested · Relational suggestion, not evidence.Same method familySurvival Analysismachine-suggested · Relational suggestion, not evidence.

Evidence status

Sources recorded, not reviewed

Bibliographic sources are present. Claim-level evidence review has not been performed.

Sources

2 recorded citations, copied from the method source record.

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