Bayesian linear regression approach in response surface methodology

In this study, Response Surface Methodology, an essential approach off-line quality improvement, and Bayesian Linear Regression, an approach which uses the prior information to make a more efficient inference, are considered together. Results for different estimators compared. In this context, results for an estimator are examined which seems to be not used before as an estimator for Bayesian regression with response surfaces in the literatüre. In response surface models, one of the main reasons for using the Bayesian Regression rather than Classic Regression, is to achieve a better model by explaining the uncertainty of the response function with prior distributions. Bayesian approach can give better answers to daily life problems since it takes the unknown parameter as a random variable. Also, the studies in the literature shows that the estimation becomes more powerful and realistic while using the prior distribution information. By using Bayesian regression, in response surface moethodology, it seems that more realistic results can be obtained by comparison with the results derived using classic regression.