Дата поступления: 
15.09.2022
Библиографическое описание статьи: 

Bazilevsky M.P., Karaulova A.V. Estimating the degree of nonlinearity for polynomial regression models // Informacionnye tehnologii i matematicheskoe modelirovanie v upravlenii slozhnymi sistemami: ehlektronnyj nauchnyj zhurnal [Information technology and mathematical modeling in the management of complex systems: electronic scientific journal], 2022. No. 3(15). P. 1-6. DOI: 10.26731/2658-3704.2022.3(15).1-6 [Accessed 15/10/22]

Год: 
2022
Номер журнала (Том): 
УДК: 
519.862.6
DOI: 

10.26731/2658-3704.2022.3(15).1-6

Файл статьи: 
Страницы: 
1
6
Аннотация: 

This article is devoted to the development of an approach to estimating the degree of nonlinearity for polynomial regression models. The non-linearity «over the area» criteria proposed earlier by the authors are limited by the fact that they are valid only for functions that do not have either extrema or inflections, therefore, when modeling, it was possible to measure the degree of non-linearity only for models that do not differ much from linear ones. In this paper, for polynomial regression models, a vector criterion for nonlinearity is proposed. A large number of components of this vector close to unity allows us to conclude that the polynomial is significantly non-linear. A straight line is characterized by a vector of one zero component. If the vector consists of several zeros, then the regression function is a broken line. The proposed approach has been successfully demonstrated on a specific example.

Список цитируемой литературы: 
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