Uncertainty Assessment of Polynomial Least Squares Fitting Based on MCM
By utilizing the calibration results from instrument calibration certificates,correction values corresponding to measurement values can be obtained.To acquire correction data for any measured value,polynomial least squares fitting is a common method.In the study of uncertainty evaluation for the fitted correction values,it was found that the expanded uncertainty of the measurement values has a significant impact on its assessment.This paper evaluates the uncertainty of the correction values fitted by the polynomial least squares method based on the Monte Carlo Method(CMC).By using Python programming to implement the steps of evaluating its uncertainty with the MCM,the uncertainty results are obtained.Therefore,the MCM provides a technical reference for the uncertainty evaluation of correction value fitting by the polynomial least squares method.
correctionpolynomial least squares fittinguncertainty evaluationmonte carlo methodpython software
万方数据
维普
