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# A Comprehensive Study on the Factors Impacting the GDP (per capita) of Major Economies around the Globe using Regression Analysis

10.5120/ijca2020920560 |

Nimisha Bhide and Saurabh Khanolkar. A Comprehensive Study on the Factors Impacting the GDP (per capita) of Major Economies around the Globe using Regression Analysis. *International Journal of Computer Applications* 175(10):26-30, August 2020. BibTeX

@article{10.5120/ijca2020920560, author = {Nimisha Bhide and Saurabh Khanolkar}, title = {A Comprehensive Study on the Factors Impacting the GDP (per capita) of Major Economies around the Globe using Regression Analysis}, journal = {International Journal of Computer Applications}, issue_date = {August 2020}, volume = {175}, number = {10}, month = {Aug}, year = {2020}, issn = {0975-8887}, pages = {26-30}, numpages = {5}, url = {http://www.ijcaonline.org/archives/volume175/number10/31489-2020920560}, doi = {10.5120/ijca2020920560}, publisher = {Foundation of Computer Science (FCS), NY, USA}, address = {New York, USA} }

### Abstract

Financial Architecture aims at sustainability of an Economy. This is done by ensuring a consistent growth rate. GDP is a strong indicator of the growth of an economy. A Higher GDP of an economy reflects a robust growth. This leads to the definition of GDP (per capita). This study focuses on the GDP (per capita) as an indicator of a nation’s prosperity. The ratio of the GDP of an economy to its population is termed as the GDP (per capita). This study considers GDP (per capita) as a function of 17 factors. Further on, out of these 17 factors, 5 of the most statistically significant factors are identified using the Backward Elimination Algorithm. Thus, a statistically significant regression model is designed and the impact of each of the 5 factors on the GDP (per capita) is gauged. It was found that the combination of the aforementioned 5 statistically significant variables could explain 83% of the variance in the GDP (per capita) of the economies. The F statistic increased from 51.13(before applying Backward Elimination Algorithm); to 168.6 (after the application of the Algorithm) and hence, signifying the increase in the overall significance of the model. The authors firmly believe that that this study will form a foundation to the higher level policy making in the future.

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### Keywords

Multiple Regression, Backward Elimination, GDP (per capita), Regression Analysis, Correlation, Hypothesis testing.