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General neutronic and photonic Monte Carlo calculations have by now become quite popular. Monte Carlo calculation of sensitivities, nevertheless, necessitates special methods. Yet these methods have so far been only applied to certain particular problems such as reaction rates in a given spatial region, and are not widely known in the user community. One such method, the Differential Operator Method due to MCG Hall, has been extended to the calculation of sensitivities of point detectors. This incidentally enables the concurrent calculation of the detector response and its sensitivities. A brief exposition of the method is presented, followed by its application to several deeppenetration benchmark experiments for which the sensitivities to iron cross sections of some relevant responses are evaluated.

The purpose of this discourse is elaboration of the least-squares formalism for averaging any number of correlated data. A procedure is considered, in a rather general formulation, which serves to determine m parameters by n correlated experimental data on possibly different functions of the parameters. If m > n, the very same procedure applies to the adjustment of a given library of m correlated parameters by a set of n relevant correlated data which might even be correlated to the given parameters. However, casual or indifferent application of the procedure might yield erroneous or even meaningless results. A very simple, and relevant, example is the combination of averages over partially overlapping sets of data. The combination yields a result which is different from the true average of the data, i.e. the average over the union of partial data sets. Further, when the measured quantities are nonlinear functions of the parameters, special care should be taken in order not to exceed the range of validity of the conventional, i.e. linearized, algorithm. One should also consider the mutual consistency of the given data, for even few outliers, which in a limited data set are highly improbable, might severely bias the resulting estimates of the unknown parameters. We discuss several observations and offer some comments on the applicability of the least-squares combination of correlated data.

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This note offers what we believe is the natural derivation of the explicit prescriptions incorporated in least-squares adjustment codes. We do not pretend to present any really new results, except perhaps for underlining the central role of the Lagrange multipliers,