The calibration function calculation uses multiple linear regression to obtain hydrodynamic volume as a polynomial function of elution volume for a given column set. [Pg.133]

The coefficients a are calculated by regression procedures. Alternatively, different linear calibration functions may be used for different concentration ranges. [Pg.659]

If it is not clear, the Mandel-test may be applied for linearity check. We calculate the linear and the 2" order calibration function and the respective residual standard deviations. If the F-test (as described in the shde) delivers a significant difference between the residual standard deviations, this shows that the 2 order calibration function significantly better describes the calibration. So this function should be preferred. If it is not significantly better, we should use the hnear function [Pg.190]

Multi point calibration will be recommended if minimum uncertainty and maximum consistency are required over a wide range of pH(X) values [21, 22]. The calibration function of the electrode is then calculated by linear regression of the difference in cell voltage results from the standard pH values. This calibration procedure is also recommended for characterising the performance of electrode systems. [Pg.210]

If the relationship between the signal and the concentration is not linear, we may apply a second order calibration function. For details about this slightly more difficult calculation see ISO 8466-2. [Pg.189]

For the practical determination of the detection criteria, the standard deviation Syo of the blank value Jq or the standard deviation of the intercept a of the linear calibration function is used, where the standard deviation should be calculated from at least = 6 measuring values. The detection criteria is then regarded as the upper limit of the blank value scatter [Pg.962]

The most common calibration model or function in use in analytical laboratories assumes that the analytical response is a linear function of the analyte concentration. Most chromatographic and spectrophotometric methods use this approach. Indeed, many instruments and software packages have linear calibration (regression) functions built into them. The main type of calculation adopted is the method of least squares whereby the sums of the squares of the deviations from the predicted line are minimised. It is assumed that all the errors are contained in the response variable, T, and the concentration variable, X, is error free. Commonly the models available are Y = bX and Y = bX + a, where b is the slope of the calibration line and a is the intercept. These values are the least squares estimates of the true values. The following discussions are only [Pg.48]

Consequently, the proof of calibration should never be limited to the presentation of a calibration graph and confirmed by the calculation of the correlation coefficient. When raw calibration data are not presented in such a situation, most often a validation study cannot be evaluated. Once again it should be noted that nonlinearity is not a problem. It is not necessary to work within the linear range only. Any other calibration function can be accepted if it is a continuous function. [Pg.104]

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