Application of the Method of Statistical Equations of Dependences to Assess the Dynamics of Regional Human Development Indx for Khmelniytsk Region - KulynychR. О.; Prydannykova, Yu. E. (Scientific Bulletin of the National Academy of Statistics, Accounting and Audit, 2017)
A statistical approach to assessment of the factor values required to have necessary (planned, predicted) levels of a resulting indicator achieved, including fo r purposes o f regional socio-economic programs, is developed, by applying the method o f statistical equations o f dependences. The main problems that can be solved by use o f the method o f statistical equations o f dependences are: direct and inverse problem; computing the factors ’ contributions in the resulting indicator; constructing graphs o f multiple relation and computing the shares o f influence (the weights o f selectedfactors); analysis o f functional and correlation dependences, etc. The developed approach is used to assess the dynamics o f Regional Human Development Index (RHD) for Khmelniytsk region (Ukraine) and its constituent factors, in 2011—2015. The computations show that the factors with the largest contribution in RDI o f Khmelniytsk region are as follows: “number o f minimal food baskets that can be purchasedfor average per capita income in the region” (62.91%), "housing in cities (square area per person) ” (20.27%), and “total birth rate” (5.33%). The contributions o f factors like “planned capacity o f ambulatories and policlinics per 10 thousand population ” or “coverage o f children in school age by secondary education ” range from 5.26 to 0.14%. It is concluded that the proposed approach to the application ofthe method o f statistical equations o f dependences fo r modeling offactor and resulting indicators contributing to human development parameters at regional level can be usedfor assessments at sectoral level, with modifying the nomenclature o f indicators measuring the socio-economic development and the financial and economic performance o f business entities in an economic sector.