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Title:
A REGRESSION ANALYSIS ON THE GLOBAL ECONOMY

Authors:
Michael Cho

Journal: International Journal of Social Science and Economic Research (IJSSER)
ISSN: 2455-8834
Volume: 7
Issue: 6
Publication Date: June 2022
DOI: 10.46609/IJSSER.2022.v07i06.018

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Michael Cho
Hong Qiao International School - Rainbow Bridge International School

MLA 8
Cho, Michael. "A REGRESSION ANALYSIS ON THE GLOBAL ECONOMY." Int. j. of Social Science and Economic Research, vol. 7, no. 6, June 2022, pp. 1718-1728, doi.org/10.46609/IJSSER.2022.v07i06.018. Accessed June 2022.
APA 6
Cho, M. (2022, June). A REGRESSION ANALYSIS ON THE GLOBAL ECONOMY. Int. j. of Social Science and Economic Research, 7(6), 1718-1728. Retrieved from https://doi.org/10.46609/IJSSER.2022.v07i06.018
Chicago
Cho, Michael. "A REGRESSION ANALYSIS ON THE GLOBAL ECONOMY." Int. j. of Social Science and Economic Research 7, no. 6 (June 2022), 1718-1728. Accessed June, 2022. https://doi.org/10.46609/IJSSER.2022.v07i06.018.

References

[1]. Linear Regression Using R: An Introduction to Data Modeling, David J. Lilja, 2016
[2]. Country of the world. https://gsociology.icaap.org/dataupload.html
[3]. All of Statistics: A Concise Course in Statistical Inference, Larry Wasserman, Springer, 2010
[4]. R for Data Science, Hadley Wickham, Garret Grolemund, 2017https://r4ds.had.co.nz/

ABSTRACT:
The economics of a nation has usually been regarded as one of the key indications of a nation's compressive national power and as a factor to gauge a country's quality of life. In this study, I use a linear regression model to investigate the statistical connection between many national characteristics and national GDP levels. According to the study's findings, the technology factor and the health factor have the biggest beneficial effects on economic development among all the other potential national characteristics. 2. Significantly hindering economic growth are the birth rate and agricultural-related factors. A comprehensive statistical analysis is conducted in the result section to consolidate the linear regression conclusion

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