title: Statistical analysis of investment attractiveness of China’s regions
metadata.name: dc.contributor.author
metadata.value: Qi, Dongfang
metadata.lang: -
metadata.name: dc.contributor.author
metadata.value: Bure, Vladimir M.
metadata.lang: -
metadata.name: dc.date.accessioned
metadata.value: 2022-06-23T15:34:34Z
metadata.lang: -
metadata.name: dc.date.available
metadata.value: 2022-06-23T15:34:34Z
metadata.lang: -
metadata.name: dc.date.issued
metadata.value: 2022-03
metadata.lang: -
metadata.name: dc.identifier.citation
metadata.value: Qi D., Bure V. M. Statistical analysis of investment attractiveness of China’s regions. Vestnik of Saint Petersburg University. Applied Mathematics. Computer Science. Control Processes, 2022, vol. 18, iss. 1, pp. 188–194.
metadata.lang: en_GB
metadata.name: dc.identifier.other
metadata.value: https://doi.org/10.21638/11701/spbu10.2022.116
metadata.lang: -
metadata.name: dc.identifier.uri
metadata.value: http://hdl.handle.net/11701/36943
metadata.lang: -
metadata.name: dc.description.abstract
metadata.value: 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.
metadata.lang: en_GB
metadata.name: dc.description.sponsorship
metadata.value: This work was supported by the Ministry of Science and Education of the Russian Federation
  (agreement N 075-15-2020-805).
metadata.lang: en_GB
metadata.name: dc.language.iso
metadata.value: en
metadata.lang: en_GB
metadata.name: dc.publisher
metadata.value: St Petersburg State University
metadata.lang: en_GB
metadata.name: dc.relation.ispartofseries
metadata.value: Vestnik of St Petersburg University. Applied Mathematics. Computer Science. Control Processes;Volume 18; Issue 1
metadata.lang: -
metadata.name: dc.subject
metadata.value: investment attractiveness
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: stepwise regression algorithm
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: multiple linear regression models
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: significant coefficients
metadata.lang: en_GB
metadata.name: dc.subject
metadata.value: least squares method
metadata.lang: en_GB
metadata.name: dc.title
metadata.value: Statistical analysis of investment attractiveness of China’s regions
metadata.lang: en_GB
metadata.name: dc.type
metadata.value: Article
metadata.lang: en_GB
resources.filename: 188-194.pdf
resources.url: https://dspace.spbu.ru/bitstream/11701/36943/1/188-194.pdf
resources.format: Adobe PDF
resources.size: 385,96 kB