TY - JOUR

T1 - Статистический анализ инвестиционной привлекательности регионов Китая

AU - Qi, Dongfang

AU - Bure, Vladimir M.

N1 - Funding Information: ∗ This work was supported by the Ministry of Science and Education of the Russian Federation (agreement N 075-15-2020-805). ©c St Petersburg State University, 2022 Publisher Copyright: © 2022 Saint Petersburg State University. All rights reserved.

PY - 2022

Y1 - 2022

N2 - This study is devoted to the application of methods of applied statistics to the study of investment attractiveness of regions of China. Methods of applied statistics are widely used in various engineering research, medicine, economics, sociology, astronomy, ecology, physics, information technology, etc. statistical tools is an integral part of the study. Data taken from the World Bank website and the China Statistical Yearbook. The main topic of this research is: What factors are most strongly associated with investment attractiveness in the economy of modern China? Such a study can only be carried out based on the analysis of economic data, especially in the conditions of the modern digital economy. Consequently, the problem under consideration belongs to the field of applied mathematics. First of all, need to choose a mathematical model and research method. This paper uses the method of regression analysis - one of the most important methods for analyzing economic data. Regression models are mathematical models built from empirical data. Observations (empirical data) are the numerical data on the level of investment in the regions of China, as well as the numerical values of various factors. For each year, a multiple regression model is built using the least squares method, its statistical significance is checked, statistically significant factors are selected (statistically significant coefficients for the factors correspond to them). Calculations are carried out in Excel and SPSS, using subroutines and. Mathematical functions, but at the same time, an algorithm for analyzing the initial data based on a stepwise regression algorithm has been developed, in which only one factor with the least significant coefficient (maximum p-value (t)) is discarded at each step and then an algorithm for choosing the most important factors for investment attractiveness was developed, the essence of this algorithm is that the regression models are compared for each year and how many times each factor was included in the model is calculated algorithms can be attributed to the application of methods informatics in this work. The choice of the most important factors that determine the level of investment attractiveness is made to solve the problem of managing the economy. Attracting investments is important for the effective development of every region, every city, every district. With the receipt of significant investments, it is possible to solve the problems of economic and social development. Determination of the most important factors will make it possible to most effectively solve the problem of managing the economic development of each region, each city, each district.

AB - This study is devoted to the application of methods of applied statistics to the study of investment attractiveness of regions of China. Methods of applied statistics are widely used in various engineering research, medicine, economics, sociology, astronomy, ecology, physics, information technology, etc. statistical tools is an integral part of the study. Data taken from the World Bank website and the China Statistical Yearbook. The main topic of this research is: What factors are most strongly associated with investment attractiveness in the economy of modern China? Such a study can only be carried out based on the analysis of economic data, especially in the conditions of the modern digital economy. Consequently, the problem under consideration belongs to the field of applied mathematics. First of all, need to choose a mathematical model and research method. This paper uses the method of regression analysis - one of the most important methods for analyzing economic data. Regression models are mathematical models built from empirical data. Observations (empirical data) are the numerical data on the level of investment in the regions of China, as well as the numerical values of various factors. For each year, a multiple regression model is built using the least squares method, its statistical significance is checked, statistically significant factors are selected (statistically significant coefficients for the factors correspond to them). Calculations are carried out in Excel and SPSS, using subroutines and. Mathematical functions, but at the same time, an algorithm for analyzing the initial data based on a stepwise regression algorithm has been developed, in which only one factor with the least significant coefficient (maximum p-value (t)) is discarded at each step and then an algorithm for choosing the most important factors for investment attractiveness was developed, the essence of this algorithm is that the regression models are compared for each year and how many times each factor was included in the model is calculated algorithms can be attributed to the application of methods informatics in this work. The choice of the most important factors that determine the level of investment attractiveness is made to solve the problem of managing the economy. Attracting investments is important for the effective development of every region, every city, every district. With the receipt of significant investments, it is possible to solve the problems of economic and social development. Determination of the most important factors will make it possible to most effectively solve the problem of managing the economic development of each region, each city, each district.

KW - investment attractiveness

KW - least squares method

KW - multiple linear regression models

KW - significant coefficients

KW - stepwise regression algorithm

UR - http://www.scopus.com/inward/record.url?scp=85134163123&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/a0860c63-ffb6-3965-acd5-8206281e57a9/

U2 - 10.21638/11701/SPBU10.2022.116

DO - 10.21638/11701/SPBU10.2022.116

M3 - статья

AN - SCOPUS:85134163123

VL - 18

SP - 188

EP - 194

JO - ВЕСТНИК САНКТ-ПЕТЕРБУРГСКОГО УНИВЕРСИТЕТА. ПРИКЛАДНАЯ МАТЕМАТИКА. ИНФОРМАТИКА. ПРОЦЕССЫ УПРАВЛЕНИЯ

JF - ВЕСТНИК САНКТ-ПЕТЕРБУРГСКОГО УНИВЕРСИТЕТА. ПРИКЛАДНАЯ МАТЕМАТИКА. ИНФОРМАТИКА. ПРОЦЕССЫ УПРАВЛЕНИЯ

SN - 1811-9905

IS - 1

ER -