Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Beta regresija× | Logistiskā regresija× | Parastā mazāko kvadrātu (OLS) regresija× | |
|---|---|---|---|
| Nozare≠ | Statistika | Pētniecības statistika | Ekonometrija |
| Saime≠ | Regression model | Process / pipeline | Regression model |
| Izcelsmes gads≠ | 2004 | 1958 | 2019 |
| Autors≠ | Ferrari & Cribari-Neto | David Roxbee Cox | Wooldridge (textbook treatment); classical least squares |
| Tips≠ | Generalized linear model (beta distribution) | Method | Linear regression |
| Pirmavots≠ | Ferrari, S. L. P. & Cribari-Neto, F. (2004). Beta Regression for Modelling Rates and Proportions. Journal of Applied Statistics, 31(7), 799–815. DOI ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Citi nosaukumi≠ | beta regression model, proportion regression, Beta Regresyonu | logit model, binomial logistic regression, LR | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Saistītās≠ | 4 | 3 | 5 |
| Kopsavilkums≠ | Beta regression is a generalized linear model introduced by Ferrari and Cribari-Neto (2004) for outcomes that are rates or proportions confined to the open interval (0,1). It models the mean of a beta-distributed response through a link function, making it the natural choice for fractions, probability scores, and proportion indices. | 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. | 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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