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| Regressione Logistica× | Regression with Ordinary Least Squares (OLS)× | |
|---|---|---|
| Campo≠ | Statistica per la ricerca | Econometria |
| Famiglia≠ | Process / pipeline | Regression model |
| Anno di origine≠ | 1958 | 2019 |
| Ideatore≠ | David Roxbee Cox | Wooldridge (textbook treatment); classical least squares |
| Tipo≠ | Method | Linear regression |
| Fonte seminale≠ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Alias≠ | logit model, binomial logistic regression, LR | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Correlati≠ | 3 | 5 |
| Sintesi≠ | 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. | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). |
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