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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
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2020
Nimisha Bhide, Saurabh Khanolkar

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

	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 = {},
	doi = {10.5120/ijca2020920560},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


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