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| Ординална логистична регресия (модел на пропорционалните шансове)× | Метод на най-малките квадрати (МНК)× | |
|---|---|---|
| Област≠ | Статистика | Иконометрия |
| Семейство | Regression model | Regression model |
| Година на възникване≠ | 2010 | 2019 |
| Създател≠ | Agresti (textbook treatment); proportional odds model | Wooldridge (textbook treatment); classical least squares |
| Тип≠ | Ordinal logistic regression | Linear regression |
| Основополагащ източник≠ | 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 |
| Други названия | proportional odds model, ordered logit, ordinal logistic regression, Ordinal Regresyon (Proportional Odds) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Свързани | 5 | 5 |
| Резюме≠ | 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). |
| ScholarGateНабор от данни ↗ |
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