Factorial Analysis as Tool to Predict the Economic Competitiveness of Mexico
Science Journal of Applied Mathematics and Statistics
Volume 7, Issue 6, December 2019, Pages: 112-120
Received: May 20, 2019;
Accepted: Jul. 12, 2019;
Published: Dec. 19, 2019
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Juan Bacilio Guerrero Escamilla, School of Social Sciences and Humanities, Autonomous University of the State of Hidalgo, Pachuca City, Mexico
Sócrates López Pérez, School of Social Sciences and Humanities, Autonomous University of the State of Hidalgo, Pachuca City, Mexico
Yamile Rangel Martinez, School of Social Sciences and Humanities, Autonomous University of the State of Hidalgo, Pachuca City, Mexico
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In the present research work the essential elements are given to build the Economic Competitiveness Index (ICE) of Mexico in 2015, for which, the technique of factorial analysis of multivariable statistics is used. Of the construction of this indicator, we start with the report presented at the World Economic Forum (WEF) in 2016, in which the variables that must be considered to increase the economic competitiveness of the countries captured. With the development of this indicator, it was possible to predict the effects that technological innovation has on the competitiveness of the country. Added to this, it identifies the limitations that each federal entity has in relation to said concept. The development of this factorial model was done through the programming language R.
Competitiveness, Methodology and Factorial Analysis, Development of Indicator
To cite this article
Juan Bacilio Guerrero Escamilla,
Sócrates López Pérez,
Yamile Rangel Martinez,
Factorial Analysis as Tool to Predict the Economic Competitiveness of Mexico, Science Journal of Applied Mathematics and Statistics.
Vol. 7, No. 6,
2019, pp. 112-120.
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Wackerly, D., Mendenhall, W. & Scheaffer, R. (2010). Mathematic Statistics with Applications. Cengage Learning. United States.
Canavos, G. Probability and Statistics. Applications and Methods. Mc Graw Hill. México 1988.
Mondragón, A. (2002). What are the indicators? Statistics Culture and Geography.
Fuente, S. (2011). Factorial Analysis. Autonomous University of Madrid. Spain.
Vinuesa, P. (2016) Topic 8-Correlation: theory and practice. Autonomus University of Mexico. Mexico.
Ruíz, C. (2008). Mexico: Economic Geography of the innovation. Foreign Trade.
Martínez, M. (2005). The concept of productivity in the economic analysis. Spanish Association of Accounting and Business Administration. Spain.
Aguilar, M. The competitiveness and Indicators of Mexico. UPIICSA 2007.
Cepal. (2016). Productivity and Structural Breaks in Mexico. United Nations. Mexico.
Luna, P. (2005). Information and Technology Systems of Information and the Methodology DEIPHI. University of Sevilla. Spain.
Sarabia, A. (1995). General Theory of Systems. ISDEFE. Spain.
Peña, D. (2002). Mathematics in Social Sciences. Multivariate Analysis of data. University Carlos III of Madrid. Spain.
Cuadras, C. (2012). New Methods of Mutivariate Analysis. CMC Editions. Spain.
Castaño, E. (2005). Introduction to the Analysis of Mutivariate Data in Social Sciences. National University of Colombia. Colombia.
Baillo, A. & Grané, A. 100 Solved Problems in Multivariate Statistics (implemented in Matlab). Delta Publications. Spain 2008.
Carmona, F. (2001). Linear Models. University of Barcelona. Spain.
Alaminos, A., Francés, F., Penalva, C., & Santacreu, O. Multivariate Analysis to Social Sciences I. Distance Indexes, Conglomerates and Factorial Analysis. Pydlos Editions. Ecuador 2015.