方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 有序逻辑回归（比例优势模型）× | 普通最小二乘法 (OLS) 回归× | |
|---|---|---|
| 领域≠ | 统计学 | 计量经济学 |
| 方法族 | Regression model | Regression model |
| 起源年份≠ | 2010 | 2019 |
| 提出者≠ | Agresti (textbook treatment); proportional odds model | Wooldridge (textbook treatment); classical least squares |
| 类型≠ | Ordinal logistic regression | Linear regression |
| 开创性文献≠ | 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 |
| 别名 | proportional odds model, ordered logit, ordinal logistic regression, Ordinal Regresyon (Proportional Odds) | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| 相关 | 5 | 5 |
| 摘要≠ | 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). |
| ScholarGate数据集 ↗ |
|
|