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| Логистична регресия× | Модел с фиксирани ефекти за панелни данни× | |
|---|---|---|
| Област≠ | Статистика за изследвания | Иконометрия |
| Семейство≠ | Process / pipeline | Regression model |
| Година на възникване≠ | 1958 | 2014 |
| Създател≠ | David Roxbee Cox | Hsiao (textbook treatment); within transformation of panel data |
| Тип≠ | Method | Panel data regression |
| Основополагащ източник≠ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ | Hsiao, C. (2014). Analysis of Panel Data (3rd ed.). Cambridge University Press. DOI ↗ |
| Други названия≠ | logit model, binomial logistic regression, LR | fixed effects model, within estimator, panel fixed-effects regression, Panel Veri — Sabit Etkiler Modeli |
| Свързани≠ | 3 | 5 |
| Резюме≠ | 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. | The Panel Data Fixed Effects model estimates relationships from panel data (the same units observed over several time periods) while controlling for unit- and/or time-specific effects, supporting causal inference. It is developed as the within estimator in standard treatments such as Hsiao's Analysis of Panel Data (2014). |
| ScholarGateНабор от данни ↗ |
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