Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Järjestusloogiline regressioon× | Tavaline vähimruutude (OLS) regressioon× | |
|---|---|---|
| Valdkond≠ | Statistika | Ökonomeetria |
| Perekond | Regression model | Regression model |
| Tekkeaasta≠ | 1980 | 2019 |
| Looja≠ | Peter McCullagh | Wooldridge (textbook treatment); classical least squares |
| Tüüp≠ | Ordinal regression / GLM | Linear regression |
| Algallikas≠ | McCullagh, P. (1980). Regression models for ordinal data. Journal of the Royal Statistical Society: Series B (Methodological), 42(2), 109–142. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Rööpnimetused | proportional-odds model, cumulative link model, ordered logit, OLR | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Seotud≠ | 6 | 5 |
| Kokkuvõte≠ | Ordinal logistic regression — most commonly the proportional-odds model — estimates the relationship between one or more predictors and an ordered categorical outcome (e.g., Likert scales, disease severity grades, educational attainment levels). It models cumulative log-odds across the ordered categories while assuming a single shared effect of each predictor at all thresholds. | 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). |
| ScholarGateAndmestik ↗ |
|
|