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| 순서형 로지스틱 회귀분석 (Ordered Logit/Probit)× | 최소제곱법(OLS) 회귀× | |
|---|---|---|
| 분야 | 계량경제학 | 계량경제학 |
| 계열 | Regression model | Regression model |
| 기원 연도≠ | 1980 | 2019 |
| 창시자≠ | McCullagh (proportional odds / cumulative model) | Wooldridge (textbook treatment); classical least squares |
| 유형≠ | Cumulative ordinal regression | Linear regression |
| 원전≠ | McCullagh, P. (1980). Regression Models for Ordinal Data. Journal of the Royal Statistical Society: Series B, 42(2), 109-142. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| 별칭≠ | ordinal logistic regression, proportional odds model, cumulative logit model, ordered probit | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 관련≠ | 4 | 5 |
| 요약≠ | Ordered logit is a cumulative regression model for an ordinal dependent variable, fitting a logit (or probit) link to the cumulative category probabilities. Developed in McCullagh's 1980 treatment of regression models for ordinal data, it is the standard tool for Likert-scale, rating, and ranked outcomes. | 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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