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Thus, these models differ among themselves in way of a choice of levels of a factor that, obviously, in turn influences possibility of generalization of the received experimental results. For the dispersive analysis of one-factorial distinction of these two models not so, however in the multiple-factor dispersive analysis it can be very important.

In the dispersive analysis not the sums of squares of deviations, and so-called average which are not displaced estimates of dispersions which turn out division of the sums of squares of deviations into the corresponding number of degrees of freedom are analyzed.

Thus, procedure of the one-factorial dispersive analysis consists in check of a hypothesis of H0 that there is one group of uniform experimental data against alternative that is more than such groups, than one. Uniformity is understood as similarity of average values and dispersions in any subset of data. Thus dispersions can be both are known, and are unknown in advance. If reasons to believe are had that known or unknown dispersion of measurements is identical on all data set, the task of the one-factorial dispersive analysis is reduced to research of the importance of distinction of averages in groups of data/1/.

In practice often there are problems of more general character – a problem of check of importance of distinctions of average selective several sets. For example, it is required to estimate influence of various raw materials on quality of the made production, to solve a problem about influence of amount of fertilizers on productivity of agricultural production.

- hierarchical (nested) classification, characteristic for model II in which to everyone to the casual, at random chosen value of one factor there corresponds the subset of values of the second factor.

The hypothesis of H0 is rejected if actually calculated F statistics = S/S is more than critical Fα:K1:K2, on a significance value α at number of degrees of freedom of k1=m-1 and k2=mn-m, and is accepted, if F

Having assumed that in the considered task about a of various m of parties of a product were made on different t machines and it is required to find out, whether are available essential a as products on each factor:

Check of zero hypotheses of HA, HB, HAB of lack of influence on the considered variable of factors of A, B and their interaction of AB is carried out by comparison of the relations, (for model I with the fixed levels of a factor or the relations, (for casual model II) with the corresponding tabular values F – Fischer's criterion – Snedekora. Check of hypotheses concerning factors with the fixed levels is also made for the mixed model III as well as in model II, and factors with casual levels – as in model

In the course of supervision over the studied object qualitative factors randomly or change the set image. Concrete realization of a factor (for example, a certain temperature condition, the chosen equipment or material) is called as a level of a factor or way of processing. Model of the dispersive analysis with the fixed levels of factors call model I, model with random factors - the Thanks to model the variation of a factor can investigate its influence on response size. Now the general theory of the dispersive analysis is developed for models

It should be noted at once that the basic difference between the multiple-factor and one-factorial dispersive analysis is not present. The multiple-factor analysis does not change the general logic of the dispersive analysis, and complicates it as, except taking note on a dependent variable of each of factors separately, it is necessary to estimate also their joint action some. Thus, new that brings the multiple-factor dispersive analysis in the analysis of data, concerns generally opportunity to estimate interfactorial interaction. Nevertheless, still there is an opportunity to estimate influence of each factor separately. In this sense procedure of a multiple-factor dispersive (in option of its computer use) is more economic as for only one start solves at once two problems: influence of each of factors and their interaction/3/is estimated.

Sometimes receive a direct response to introduction of chemical prepararat it can to be complicated by interaction of a preparation with fertilizers, both organic, and mineral. The obtained data allowed to track dynamics of a response of the brought preparations, in all options with chemical means of protection where the suspension of growth of the indicator is noted.