Vertaile menetelmiä

	Logistinen regressio	OLS-regressio (Ordinary Least Squares)
Tieteenala	Tutkimuksen tilastomenetelmät	Ekonometria
Menetelmäperhe	Process / pipeline	Regression model
Syntyvuosi	1958	2019
Kehittäjä	David Roxbee Cox	Wooldridge (textbook treatment); classical least squares
Tyyppi	Method	Linear regression
Alkuperäislähde	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
Rinnakkaisnimet	logit model, binomial logistic regression, LR	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Liittyvät	3	5
Tiivistelmä	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).