Linganisha mbinu

	Usuli wa Regresi ya Beta	Regresheni ya Logistiki	Urejeshaji wa Njia ya Viwango Vidogo vya Kawaida (OLS)
Nyanja	Takwimu	Takwimu za Utafiti	Ekonometriki
Familia	Regression model	Process / pipeline	Regression model
Mwaka wa asili	2004	1958	2019
Mwanzilishi	Ferrari & Cribari-Neto	David Roxbee Cox	Wooldridge (textbook treatment); classical least squares
Aina	Generalized linear model (beta distribution)	Method	Linear regression
Chanzo asilia	Ferrari, S. L. P. & Cribari-Neto, F. (2004). Beta Regression for Modelling Rates and Proportions. Journal of Applied Statistics, 31(7), 799–815.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Majina mbadala	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
Zinazohusiana	4	3	5
Muhtasari	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).