Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Usuli wa Regresi ya Binomiali Hasiri× | Regresheni ya Logistiki× | |
|---|---|---|
| Nyanja≠ | Ekonometriki | Takwimu za Utafiti |
| Familia≠ | Regression model | Process / pipeline |
| Mwaka wa asili≠ | 2011 | 1958 |
| Mwanzilishi≠ | Hilbe (textbook treatment); generalized linear model framework | David Roxbee Cox |
| Aina≠ | Generalized linear model for count data | Method |
| Chanzo asilia≠ | Hilbe, J. M. (2011). Negative Binomial Regression (2nd ed.). Cambridge University Press. DOI ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Majina mbadala | NB regression, NB2 regression, negatif binom regresyonu | logit model, binomial logistic regression, LR |
| Zinazohusiana≠ | 4 | 3 |
| Muhtasari≠ | 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. | 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. |
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