Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Ordinální logistická regrese (model proporcionálních šancí)× | Regrese metodou ordinárních nejmenších čtverců (OLS)× | |
|---|---|---|
| Obor≠ | Statistika | Ekonometrie |
| Rodina | Regression model | Regression model |
| Rok vzniku≠ | 2010 | 2019 |
| Tvůrce≠ | Agresti (textbook treatment); proportional odds model | Wooldridge (textbook treatment); classical least squares |
| Typ≠ | Ordinal logistic regression | Linear regression |
| Původní zdroj≠ | Agresti, A. (2010). Analysis of Ordinal Categorical Data (2nd ed.). Wiley. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Další názvy | proportional odds model, ordered logit, ordinal logistic regression, Ordinal Regresyon (Proportional Odds) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Příbuzné | 5 | 5 |
| Shrnutí≠ | Ordinal logistic regression models an ordered categorical outcome — such as a Likert rating, a satisfaction level, or an education tier — as a function of predictors. It is the ordinal extension of logistic regression, developed in standard treatments such as Agresti's Analysis of Ordinal Categorical Data (2010), and in its most common form it is the proportional odds model. | 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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