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| Regressione Beta× | Regression with Ordinary Least Squares (OLS)× | |
|---|---|---|
| Campo≠ | Statistica | Econometria |
| Famiglia | Regression model | Regression model |
| Anno di origine≠ | 2004 | 2019 |
| Ideatore≠ | Ferrari & Cribari-Neto | Wooldridge (textbook treatment); classical least squares |
| Tipo≠ | Generalized linear model (beta distribution) | Linear regression |
| Fonte seminale≠ | Ferrari, S. L. P. & Cribari-Neto, F. (2004). Beta Regression for Modelling Rates and Proportions. Journal of Applied Statistics, 31(7), 799–815. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Alias≠ | beta regression model, proportion regression, Beta Regresyonu | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Correlati≠ | 4 | 5 |
| Sintesi≠ | 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. | 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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