(in Polish)Įlster, C., Toman, B.: Bayesian uncertainty analysis for a regression model versus application of GUM Supplement 1 to the least-squares estimate. Academy Warsaw WAT LVII(2), 143–152 (2008)ĭorozhovets, M.: Uncertainty of Orthogonal Linear Regression. Zięba, A.: Analysis of Experimental Data in Science and Technology PWN 2013 (in Polish, extended version to be published in 2021 by Cambridge Scholars Publishing, Newcastle GB) (2013)ĭorozhovets, M., Warsza, Z.L.: Uncertainty type A evaluation of autocorrelated measurement observations. Weisberg Sanford Applied Linear Regression, 3rd edn. Piotrowski, J.: Theory of Physical and Technical Measurement PWN-Elsevier (1992) BIPM Parisĭraper, R.D., Smith H.: Applied Regression Analysis, 3rd edn. GUM JCGM100:2008, Evaluation of measurement data – Guide to the expression of uncertainty in measurement.+ GUM-S1:JCGM101:2008 Supplement 1 – Propagation of distributions using a Monte Carlo method +GUM-S2: JCGM102:2011 Supplement 2 – Extension to any number of output quantities. Considerations are illustrated with four numerical examples of the measurement points with the same coordinates, but different absolute and relative uncertainties. For known values of X variable, the parameters and uncertainty bands of regression line are determined for measurements of uncorrelated values of Y with Type A and Type B uncertainties.
The case of random changes of variable Y and the criteria used in linear regression are discussed in detail. The introduction presents the essence of the uncertainty calculations used in GUM. The impact of Type B measurement uncertainties is included, which is omitted in the statistical literature on the accuracy of regression method. Recommendations of the international Guide to the Expression of Uncertainty in Measurement (GUM) are used. This work concerns on the estimation of the accuracy of function determined by the linear regression method for the description of noncorrelated measured data of Y.
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