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In the article, a modification of the new criterion of the adequacy of regression equations introduced by the author-the "consistency of behavior" criterion or the so-called CB-criterion is introduced. It is based, unlike the traditional adequacy criteria in the regression analysis, not on the analysis of the approximation errors of the equation, but on the correlation of the signs of the increments of the actual and calculated values of the dependent variable of the equation by the observation numbers. Therefore, even for equations with high values of classical verification criteria, the CB criterion may have low significance. In contrast to the SP criterion, the generalized criterion for consistency of behavior (GCB criterion) proposed in this article assumes the correlation of the indicated increments for pairs of observations with arbitrary numbers, which makes it possible to reveal the complete picture in accordance with the behavior of the actual and calculated values of the dependent variable of the equation throughout the sample, all possible cross-links. In addition, the paper proposes an algorithm for maximizing the value of an GCB test with a fixed or slightly degraded value chosen by the researcher for the loss function as a sum of the absolute values of the approximation errors corresponding to the Manhattan distance or the method of the smallest modules. This algorithm allows us to reduce this problem to the problem of partially-boolean linear programming of low dimensionality. It also provides for the possibility of combining the GCB criterion with the loss function by forming their linear convolution. In this case, it is possible to give each of its components a different weight, depending on which criterion the decision-maker considers more or less important. With the software implementation of this algorithm, the LPsolve program can be effectively used on the Internet.

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