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

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2020
Authors:
Nimisha Bhide, Saurabh Khanolkar
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.