방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 로지스틱 회귀× | 음이항 회귀× | |
|---|---|---|
| 분야≠ | 연구 통계 | 계량경제학 |
| 계열≠ | Process / pipeline | Regression model |
| 기원 연도≠ | 1958 | 2011 |
| 창시자≠ | David Roxbee Cox | Hilbe (textbook treatment); generalized linear model framework |
| 유형≠ | Method | Generalized linear model for count data |
| 원전≠ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ | Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press. DOI ↗ |
| 별칭 | logit model, binomial logistic regression, LR | NB regression, NB2 regression, negatif binom regresyonu |
| 관련≠ | 3 | 4 |
| 요약≠ | Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science. | Negative Binomial Regression is a generalized linear model for count outcomes that extends Poisson regression to handle overdispersion, where the variance of the counts exceeds their mean. Developed in the GLM tradition and treated in depth by Hilbe (2011), it adds a dispersion parameter so that inference stays valid when Poisson would understate the spread of the data. |
| ScholarGate데이터셋 ↗ |
|
|