REGRESSION MODEL FOR SUBSTANTIATION OF SUSTAINABLE STATE POLICY IN A DIGITAL ECONOMY

Abstract
Description
The article discusses the use of digital mathematical models in justifying public policy for sustainable economic development. The wider expediency uses of statistical analysis methods to substantiate public administration decisions in the digital economy is explained. The technique of forming and preparation of experimental data for the construction of a mathematical regression model is described. The hypothesis of normal distribution of the input data was tested according to the statistical criteria of Kolmogorov-Smirnov and Lilliefors and Shapiro-Wilk. Box-Cox transformation was used to normalize the data. The linear multiple regression coefficients are determined by the least squares method. The quality of the resulting model was evaluated using the Student's and Fisher's criteria. The results of the forecast of financial stability of agricultural enterprises for 2020-2022 and the factor indicators indicate the negative dynamics of the effective indicator of their financial condition. The proposed model can be used to continuously monitor the financial condition of agricultural enterprises by linking it to the State Statistics Service of Ukraine
Keywords
digital economy, state policy of sustainable development, financial sustainability, agricultural enterprise, mathematical simulation, linear multiple regression, normal distribution
Citation