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	Bayesowska prosta regresja liniowa	Regresja metodą najmniejszych kwadratów (OLS)
Dziedzina	Statystyka	Ekonometria
Rodzina	Regression model	Regression model
Rok powstania	Early 19th century; textbook synthesis 2013	2019
Twórca	Laplace, P.-S. (early 19th c.); modern treatment: Gelman et al.	Wooldridge (textbook treatment); classical least squares
Typ	Bayesian linear regression	Linear regression
Źródło pierwotne	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955	Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860
Inne nazwy	Bayesian SLR, Bayesian univariate regression, probabilistic simple linear regression, Bayesian linear model	ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu
Pokrewne	6	5
Podsumowanie	Bayesian Simple Linear Regression models the relationship between a continuous outcome and a single predictor by combining a Gaussian likelihood with prior distributions over the intercept, slope, and error variance. The result is a full posterior distribution over all parameters, providing probabilistic uncertainty quantification rather than a single point estimate.	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).