The several possibihties described in Section 8.3 may seem confnsing bnt in practice the problem is not usually too comphcated for analytical methods based on mass spectrometric detection, as a resnlt of the excellent degree of linearity observed for mass spectrometric response over ranges of several orders of magnitnde in the concentration (amonnt) of anal5d e introdnced. The best way to proceed will vary with the details of the reqnired analysis and the [Pg.416]

By linear least-squares regression we mean fitting experimental data (Xj,Yi) to a theoretically justified function Yipred = f(Xj, A, that is such that the partial derivatives (e.g., d S,jl)/8A) with respect to each of the [Pg.417]

An example of a more complex function that is linear in this sense is Yi pred = A + 5.exp(x ), but Yj p d = A + 5.exp(C.X ) can not be fitted by linear least-squares since d(S, j )/dC is not linear in C in such cases there is no simple algebraic solution for the parameters as there is for the linear case (e.g., Equations [8.21]-[8.24]), so we must use nonlinear regression techniques that involve successive iterations to the final best result. [Pg.417]

If iteration starts here result is a false minimum [Pg.417]

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