방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 다중 회귀 분석× | 로지스틱 회귀× | |
|---|---|---|
| 분야 | 연구 통계 | 연구 통계 |
| 계열 | Process / pipeline | Process / pipeline |
| 기원 연도≠ | 1801 | 1958 |
| 창시자≠ | Carl Friedrich Gauss | David Roxbee Cox |
| 유형 | Method | Method |
| 원전≠ | Draper, N. R., & Smith, H. (1966). Applied Regression Analysis. John Wiley & Sons. link ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| 별칭 | MLR, multivariate regression, linear regression | logit model, binomial logistic regression, LR |
| 관련≠ | 4 | 3 |
| 요약≠ | Multiple regression analysis is a statistical method for modeling the relationship between a continuous dependent variable and two or more independent variables (predictors). Originating from Gauss's early 19th-century work and formalized by Draper and Smith (1966), it estimates linear equations predicting outcomes from multiple predictors while accounting for confounding relationships, making it indispensable in epidemiology, economics, psychology, and clinical research. | 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. |
| ScholarGate데이터셋 ↗ |
|
|