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| ANOVA Bayesiana× | Regression with Ordinary Least Squares (OLS)× | |
|---|---|---|
| Campo≠ | Bayesiano | Econometria |
| Famiglia≠ | Bayesian methods | Regression model |
| Anno di origine≠ | 2012 | 2019 |
| Ideatore≠ | Rouder, Morey, Speckman & Province | Wooldridge (textbook treatment); classical least squares |
| Tipo≠ | Bayesian hypothesis test / group comparison | Linear regression |
| Fonte seminale≠ | Rouder, J. N., Morey, R. D., Speckman, P. L. & Province, J. M. (2012). Default Bayes Factors for ANOVA Designs. Journal of Mathematical Psychology, 56(5), 356–374. DOI ↗ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 |
| Alias | bayesian analysis of variance, bayes factor ANOVA, JZS ANOVA, Bayesçi ANOVA — Bayes Faktörü ile Grup Karşılaştırması | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu |
| Correlati≠ | 4 | 5 |
| Sintesi≠ | Bayesian ANOVA, formalised by Rouder, Morey, Speckman and Province (2012), tests whether group means differ by quantifying the evidence for the alternative hypothesis relative to the null using the Bayes Factor (BF₁₀). Unlike classical ANOVA, it can also measure evidence in favour of the null hypothesis, making it equally informative when groups do not differ. | 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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