Дата поступления: 
01.05.2021
Год: 
2021
Номер журнала (Том): 
УДК: 
519.683.8, 519.233.5
DOI: 

10.26731/2658-3704.2021.3(11).35-46

Файл статьи: 
Страницы: 
35
46
Аннотация: 

This paper describes what tools of the Python programming language were used to develop the application. The instructions for using the "Statistical Correlation and Regression Calculator", designed to automate and simplify the process of correlation and regression analysis of data, are given. In this article, the methods of analysis of linear pair regression and linear multiple regression were considered. Formulas for finding the coefficients of the regression line and plane equations using the least squares method are presented.

Список цитируемой литературы: 

1.

1. Gorlov, A. I. Opredelenie geneticheskih korrelyacij selekcionnyh priznakov cherez chastnye korrelyacii [Determination of genetic correlations of breeding traits through partial correlations]. Sbornik nauchnyh trudov Stavropol'skogo nauchno-issledovatel'skogo instituta zhivotnovodstva i kormoproizvodstva [Collection of scientific papers of the Stavropol Research Institute of Animal Husbandry and Feed Production.]. 2009, T.2, no. 2-2, pp. 25-29.

2. Yashina, N. I. Instrumentarij prognozirovaniya finansovogo sostoyaniya organizacij na osnove teorii regressionnogo analiza, metodov Pareto i rangovoj korrelyacii [Tools for forecasting the financial condition of organizations based on the theory of regression analysis, Pareto methods and rank correlation]. Finansy i kredit [Finance and Credit]. 2004. no. 5, vol. 143.

3. Dergunov, V. V. Analiz dinamiki VNP metodom linejnoj regressii [Analysis of DNP dynamics by linear regression method]. Vestnik Finansovoj akademii [Bulletin of the Financial Academy]. 1999, no. 4, vol. 12, pp. 98-108.

4. Gefan G.D. Statisticheskij metod i osnovy ego primeneniya [Statistical method and the basics of its application]. Uchebnoe posobie [Student textbook]. Irkutsk: IrGUPS, 2003, p. 208.

5. Math — Mathematical functions [Electronic resource]. – Mode of access: https://docs.python.org/3/library/math.html (accessed 07.06.2021)

6. KIVY documentation [Electronic resource]. – Mode of access: https://kivy.org/doc/stable/ (accessed 07.06.2021)

7. Matplotlib: Visualization with Python [Electronic resource]. – Mode of access: https://matplotlib.org/ (accessed 07.06.2021)