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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Curto, J. & Pinto, J. (2014). The explanatory power tests: R-square based analysis. Advances in Computer Science and Engineering. 12 (1), 31-60
Exportar Referência (IEEE) 
J. J. Curto and J. C. Pinto,  "The explanatory power tests: R-square based analysis", in Advances in Computer Science and Engineering, vol. 12, no. 1, pp. 31-60, 2014
Exportar BibTeX 
@article{curto2014_1714624755612,
	author = "Curto, J. and Pinto, J.",
	title = "The explanatory power tests: R-square based analysis",
	journal = "Advances in Computer Science and Engineering",
	year = "2014",
	volume = "12",
	number = "1",
	pages = "31-60",
	url = "http://www.pphmj.com/journals/articles/1206.htm"
}
Exportar RIS 
TY  - JOUR
TI  - The explanatory power tests: R-square based analysis
T2  - Advances in Computer Science and Engineering
VL  - 12
IS  - 1
AU  - Curto, J.
AU  - Pinto, J.
PY  - 2014
SP  - 31-60
SN  - 0973-6999
UR  - http://www.pphmj.com/journals/articles/1206.htm
AB  - The coefficient of determination R-square has an important role in empirical accounting and finance when the purpose is to compare the explanatory power of the same model in different samples or between models in the same sample. For this reason, it became a standard statistical procedure to test if the differences in sampling R-square's are statistically significant. Therefore, and in order to check if the difference in the R-square from two linear regression models with the same dependent variable is statistically significant, we propose EViews programs to compute the value of two tests for independent and nonindependent samples. These tests are commonly used in empirical accounting and finance and they cannot be computed in a standard way by the Econometric Views software.
ER  -