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THE                     JOURNAL OF ECONOMIC SCIENCES: THEORY AND PRACTICE, V.80, # 1, 2023, pp. 94-105

                    Our regression model has an R value of 0.963, it means that there is a strong positive
                    correlation between the dependent variable and the independent variable(s). The R-
                    value ranges between -1 and +1, with 1 indicating a perfect positive correlation and -
                    1 indicating a perfect negative correlation. Therefore, an R-value of 0.963 indicates
                    that there is a very strong positive correlation between the variables.

                    The R-squared value of 0.927 indicates that approximately 92.7% of the variability in
                    the dependent variable is explained by the independent variable(s). The R-squared
                    value ranges between 0 and 1, with 1 indicating that all the variability in the dependent
                    variable is explained by the independent variable(s). Therefore, an R-squared value
                    of 0.927 indicates that the regression model is a good fit for the data and that the
                    independent  variable(s)  can  explain  a  large  proportion  of  the  variability  in  the
                    dependent variable. The Adjusted R-squared value of 0.923 is similar to the R-squared
                    value but takes into account the number of independent variables in the model. The
                    adjusted R-squared value adjusts for the effect of adding more variables to the model
                    and  is  often  a  better  measure  of  the  goodness  of  fit  for  models  with  multiple
                    independent  variables.  In  summary,  our  regression  model  has  a  strong  positive
                    correlation between the dependent and independent variables, a good fit to the data,
                    and a moderate amount of variability in the errors or residuals. Based on our statistical
                    analysis we can conclude that there is a strong relationship between FDI inflows and
                    Azerbaijan’s Non-Oil GDP, that’s why we can accept the Alternative hypothesis.

                    CONCLUSIONS AND RECOMMENDATIONS

                    In conclusion, this study provides evidence that there is a strong relationship between
                    FDI inflows and Azerbaijan’s Non-Oil GDP. The findings suggest that FDI can be an
                    effective  tool  for  promoting  economic  growth  and  diversifying  the  economy  in
                    Azerbaijan. FDI can contribute to economic diversification and growth in non-oil
                    sectors of the economy, which is important for reducing Azerbaijan's dependence on
                    oil  exports.  Therefore,  policies  that  attract  foreign  investment  and  promote
                    diversification  are  likely  to  be  beneficial  for  Azerbaijan's  long-term  economic
                    development. However, the study also highlights the challenges that FDI faces in the
                    country,  including  a  lack  of  infrastructure,  bureaucratic  barriers,  and  political
                    instability. These challenges must be addressed in order to fully realize the potential
                    benefits of FDI for Azerbaijan's economy.
                    Based  on  the  findings  of  this  study,  several  recommendations  can  be  made  for
                    policymakers and investors in Azerbaijan. First, policymakers should prioritize the
                    development  of  infrastructure,  including  transportation,  telecommunications,  and
                    energy systems, in order to improve the business environment for investors. Second,
                    efforts  should  be  made  to  reduce  bureaucratic  barriers  and  streamline  regulatory
                    processes to make it easier for investors to do business in the country.


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