Karaulova A.V., Bazilevskiy M.P. Application of regression analysis in solving real technical problems // The electronic scientific journal "Young science of Siberia", 2020, no. 3(9). [Accessed 20/10/20]
The article is devoted to the review of articles on the application of regression analysis in the technical sphere of the modern world. Consider the following model: model that reflects the relationship of characteristics of the processed material and technological conditions of laser hardening depth of the surface layer; model density imitation lard; the duration model mixer from the residue (s) in the preparation of grain mixture in the production of feeds; the model for calculating the flash point in closed crucible (TWST); a model of the influence of operating conditions on the surface temperature of an automobile generator; a model used in the development of a new technology for high-volume combined yarn using microwave currents; a model for monitoring train schedules according to the criterion of energy efficiency; a model that helps to ensure the accuracy of the shape of holes during finishing milling; a model for determining rational design parameters of a grooved roll. Almost all the models studied are nonlinear. Based on the analysis of the sources considered, it was concluded that the authors do not pay much attention to their complexity when constructing models
1. Noskov S. I. Technology of modeling objects with unstable functioning and uncertainty in data. - Irkutsk: Oblinformpechat, 1996, 321 p
2. Draper N.R., Smith H. 1998. Applied regression Analysis, 3rd edition. John Wiley & Sons, 736p.
3. Mendenhall W., Sincich T.T. A second course in statistics: regression analysis, 8th edition. – Pearson, 2019. – 848 p.
4. Bazilevsky M. P., Gefan G. D. Econometrics (advanced level): laboratory session. Irkutsk [Irkutsk State Transport University], 2016. – 76 p.
5. Noskov S. I., Bazilevsky M. P. Building regression models using linear-Boolean programming: monograph. – Irkutsk [Irkutsk State Transport University], 2018. – 176 p.
6. Bazilevsky M. P. MNK-estimation of parameters of two-factor regression models specified on the basis of Leontiev functions // South Siberian scientific Bulletin, 2019, No 2 (26). pp. 66-70.
7. Baenkhayeva A.V., Bazilevsky M. P., Noskov S. I. Modeling of the gross regional product of the Irkutsk region based on the application of the method of multiple estimation of regression parameters. Fundamental research, 2016, No 10-, pp. 9-14.
8. Baenkhayeva A.V., Bazilevsky M. P., Noskov S. I. Choice of structural specification of the regression model of the gross regional product of the Irkutsk region // Information technologies and problems of mathematical modeling of complex systems, 2016, No 16, pp. 31-38.
9. Noskov S. I., Kirillova T. K. Regression model for assessing the impact of recreational activities on the socio-economic development of the territory // Bulletin of Irkutsk state technical University, 2013, No 9 (80), pp. 24-28.
10. Chigirinsky Yu. L., Shchepetnov I. A., Chigirinskaya N. V. Regression model of laser hardening process // Fundamental research, 2015, No 6-2, pp. 306-310.
11. Naumov V. A., Korzhavina Yu. N., Shibeko A. G., Singaev V. I., Alyshevsky D. L. a Regression model of the density of imitation lard // Proceedings of KGTU, 2018, No 49, pp. 145-153.
12. Borisova M. V., Titov A. Yu., Novikov V. V., Konovalov V. V. Regression model of low-speed mixer emptying performance // Bulletin of the Bashkir state agrarian University, 2019, No 2 (50), pp. 103-108.
13. Maximova A. Yu., Ivanova A. A., Lozinsky N. S. Regression model for predicting the flash point of diesel fuel in a closed crucible // Informatics and Cybernetics, 2019, Vol. 18, No. 4, pp. 5-13.
14. Puzakov A.V., Osaulko Ya. Yu. Investigation of the influence of operational factors on the thermal state of an automobile generator // Bulletin of the Moscow automobile and road state technical University (MADI), 2018, No 1 (52), pp. 16-23.
15. Kulandin A. S. Kogan A. G. Production of high-volume combined yarn using microwave currents. // Modeling in engineering and Economics: collection of materials international. scientific-practical Conf. / VGTU, Vitebsk, 2016, pp. 101-103.
16. Nezevak V. L. Modeling of power consumption for traction if you change the settings of the schedule of trains on electrified sections III-th and IV-th type of the profile path //. Sovremennyye tekhnologii. Sistemnyy analiz. Modelirovaniye [Modern Technologies. System Analysis. Modeling], 2019, No. 1 (61), pp. 156–166.
17. Stelmakov V. A., Nikitenko A.V., Davydov V. M., Gimadeev M. R. Providing precision forms for finishing holes by milling // Information technologies of the XXI century: collection of scientific papers, 2017, pp. 502-509.
18. Chertov Yu. E., Galatov K. S., Molev M. D. Regression analysis of the process of deformation of the pen rod by a grooved roll // Modern problems of science and education, 2015, No 2-3, p. 49.
19. Bazilevsky M. P. criteria of non-linearity of multi-factor quasilinear regression // Youth and science: actual problems of fundamental and applied research: collection of materials of the II all-Russian national scientific conference of students, postgraduates and young scientists, 2019, pp. 210-213.
20. Bazilevsky M. P. Сriteria for non-linearity of quasilinear regression models // Modeling, optimization and information technologies, 2018, Vol. 6, No 4 (23), pp. 185-195.