E.M. Farhadzadeh, A.Z. Muradaliyev, T.K. Rafiyeva, A.A. Rustamova

Èlektron. model. 2019, 41(3):93-104


One of the main requirements for the evaluation of technical and economic performance indicatorsOne of the main requirements for the evaluation of technical and economic performance indicatorsis their independence from one another, traditionally established by comparing the correlationcoefficient calculated from the operational statistics with its critical value. Existing restrictionsfor application of factors of correlation not always are considered. It is shown, that overcomingof difficulties at an estimation of integrated parameters can be reached on the basis ofcomplex application fiducial approach, imitating modelling and theory of check of statistical hypotheses.And overcoming of bulkiness and labour input of the manual account and influences ofthe human factor is reached by transition to the decision of problems of the basis of computertechnologies.


power unit, fiducial, multivariate, hypotheses, interrelation, correlation, sample.


1. Farkhadzade, E.M., Muradaliyev, A.Z., Farzaliyev, Yu.Z. and Abdullayeva, S.A. (2018), “Comparison and ranging steam turbine installations of power units PES on an overall performance”, Teploenergetika, no. 10, pp. 41-50.
2. Orlov, A.M. (2006), Prikladnaya statistika [Applied statistics], Ekzamen, Moscow, Russia. 
3. Kobzar, A.I. (2006), Prikladnaya matematicheskaya statistika [Applied mathematical statistics], Fizmatgiz, Moscow, Russia.
4. (1990), Tipovoy algoritm rascheta tekhniko-ekonomicheskikh pokazateley kondensatsionnykh energoblokov moshchnostiyu 300, 500, 800 i 1200 MVt [Typical algorithm for calculating the technical and economic indicators of condensing units with a capacity of 300, 500,
800 and 1200 MW], VTI.
5. Farkhadzade, E.M., Muradaliyev, A.Z., Farzaliyev Yu.Z., Rafiyeva, T.K. and Abdullayeva, S.A. (2018), “Minimization of risk of the erroneous decision at an estimation of the importance of statistical communications of technical and economic parameters of objects of electro power systems”, Energetika, Vol. 61, no. 3, pp. 193-206.
6. Farhadzadeh, E.M., Muradaliyev, A.Z., Farzaliyev, Y.Z. and Rafiyeva, T.K. (2017), “The comparative analysis of methods of calculation of the integrated parameters describing an
overall performance of objects EES”, Elektronnoye modelirovaniye, no. 3, pp.75-89. 
7. Kolmogorov, A.A. (1942), “Determination of the center of scattering and measures of accuracy
for a limited number of observations”, Seriya matem., no. 6, pp. 3-32.
8. Orlov, A.M. (2004), Matematika sluchaya. Veroyatnost i statistika — osnovnyye faktory. Ucheb. posobiye [Mathematics of the case. Probability and statistics are the main factors. Training allowance], Press, Moscow, Russia.
9. Fisher, R.A. (1935), “The fiducial argument on statistical inference”, Ann. of Engenics, Vol. 5, no. 3, pp. 391-398.
10. Fisher, R.A. (1950), Contributions to Mathematical statistics, Wiley, New York, USA.
11. Fisher, R.A. (1955), Statistical methods and scientific induction. J.Roy. Statist. Soc. Ser. B.17.
12. Orlov, A.M. (2014), “A new paradigm of analysis of statistical and experimental data in the problems of economics and management”, Politematicheskiy setevoy elektronnyy nauchnyy zhurnal Kubanskogo gosudarstvennogo agrarnogo un-ta, no. 98 (14), pp. 34-42.
13. Farhadzadeh, E.M., Muradaliyev, A.Z. and Farzaliyev, Y.Z. (2013), “Comparison methods of modeling continuous random variables on empirical distributions”, Reliability; Theory&applications (RT&A), Vol. 8, no. 2, pp. 49-54.

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