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| Regresja metodą najmniejszych kwadratów (OLS)× | Regresja Poissona i regresja ujemna dwumianowa× | |
|---|---|---|
| Dziedzina | Ekonometria | Ekonometria |
| Rodzina | Regression model | Regression model |
| Rok powstania≠ | 2019 | 1998 |
| Twórca≠ | Wooldridge (textbook treatment); classical least squares | Cameron & Trivedi (textbook treatment); Hilbe (negative binomial) |
| Typ≠ | Linear regression | Generalized linear model for count data |
| Źródło pierwotne≠ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Cameron, A. C. & Trivedi, P. K. (1998). Regression Analysis of Count Data. Cambridge University Press. DOI ↗ |
| Inne nazwy | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | count regression, log-linear count model, negative binomial regression, Poisson / Negatif Binom Regresyon |
| Pokrewne≠ | 5 | 4 |
| Podsumowanie≠ | 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). | Poisson regression is a generalized linear model for count outcomes — events tallied as non-negative integers such as hospital admissions, accidents, or article counts. It models the log of the expected count as a linear function of the predictors, and is developed in the standard count-data treatment of Cameron and Trivedi (1998); when the counts are over-dispersed, the closely related negative binomial model (Hilbe, 2011) is preferred. |
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