@article{MAKHILLIBM2016102626996,
    title = {Analysis of Regression Relationship Between the Number of
Organisations of the Russian Regional Innovation Infrastructure and the
University Infrastructure and the Gross Regional Product},
    journal = {International Business Management},
    volume = {10},
    number = {26},
    pages = {6026-6035},
    year = {2016},
    issn = {1993-5250},
    doi = {ibm.2016.6026.6035},
    url = {https://makhillpublications.co/view-article.php?issn=1993-5250&doi=ibm.2016.6026.6035},
    author = {Vladimir M.,Sizyoongo,Vadim V.,Stanislav I. and},
    keywords = {Regional innovation potential,regional innovation infrastructure,university infrastructure,Russian regions,correlation,regression correlation,coefficient of determination,benchmarking methodology,pair correlation matrix,gross regional product,linear regression equation,GRP},
    abstract = {We took databases of the National Information and Analytical Center for monitoring innovation
infrastructure of scientific and technological activities and regional innovation systems and the Web portal of
innovation and business information support "innovations and entrepreneurship", webometrics database
according to rankings of all Russian universities as well as the database of the Russian Federal State Statistics
Service on the gross regional product for all regions of Russia as an empirical basis in order to determine the
regression relationship between the number of organisations of the regional innovation and university
infrastructure and the gross regional product. Data on the first two innovation databases had been collected
as of the end of December 2014 and the distribution of universities according to the Russian regions was made
according to Webometrics data (July, 2015) and university websites. Initially high determination coefficients
R2 obtained in the course of searching the relationship between the number of innovation infrastructure
organisations and universities according to two databases for all Russian regions were sharply decreasing,
when excluding the data for Moscow and Saint Petersburg. The obtained results if compared with the gross
regional product and the population of regions, allow planning the allocation of the university and innovation
infrastructure according to regions of Russia. Further, the article also explores linear regression equations
obtained between the above mentioned databases number of organisations of the regional innovation
infrastructure on the one part and the gross regional product on the other part for the years 2007 and
2014. It is obvious that the Russian regional innovation infrastructure is low-developed, that is why it is not
still the engine for economic growth of regions but on the contrary, economic strength of regions their
urban infrastructure and culture are the driver for the development of the regional innovation
infrastructure.}
    }