Regional innovation assessment toolkit: correlation-regression analysis

The article continues the cycle of works devoted to instrumental methods of assessing innovation activity in the regions of Russia. The paper considers the feasibility of using correlation and regression analysis, which is widely used in justifying the dependence of one variable on another. The article provides definitions of some terms of correlation and regression analysis, provides a brief overview of the areas of their use by various authors for the analysis of economic processes. The paper presents a comparative correlation analysis of indicators of innovation activity in Russian regions, using data for the period from 2000 to 2019. The article analyzes the indicators of scientific and innovative activity that characterize the effectiveness of various stages of innovation activity. The article presents a model of multiple regression for a set of regions, in which the output parameter is «The share of shipped innovative products in all shipped industrial products». It is shown that with good probabilities of reliable determination of regression coefficients, the most adequate is the use of four input parameters. In this case, the variations of the output parameter are only half related to the variations of the input parameters. The rest of the variations can be explained by the individual characteristics of the innovative development of the regions. In the final part of the paper, a dynamic analysis of indicators in individual regions is presented, which revealed a stochastic distribution of paired correlation coefficients, which indicates that, despite the general economic situation, innovation processes proceed independently. In some regions, correlations between individual indicators of innovation activity are identified, taking into account time lags

Keywords: innovation activity, indicators of innovation activity, correlation analysis, regression equation, coefficient of determination, multiple regression

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