STATISTICS APPLIED WITH R USING ARTIFICIAL INTELLIGENCE

Main Article Content

MGS. EDWIN FERNANDO MEJÍA PEÑAFIEL, MSC. RAQUEL VIRGINIA COLCHA ORTIZ, MGS. WILLIAN GEOVANNY YANZA CHAVEZ, MSC. MARCO ANTONIO GAVILANES SAGÑAY, DRA. GLADYS LOLA LUJÁN JOHNSON

Abstract

Machine learning within R using inferential statistics in academic fields today is already a reality, that research is directed towards the world of artificial intelligence as an aid within engineering based on tools such as machine learning, in this case with supervised  learningand unsupervised learning. It is important because it can be applied  in engineering, such as in statistics and the world of artificial intelligence to automate  statistical processesof various samples in this area.


Objective. Perform adescriptive statistic using quantitative data  in R through structured programming compared with  supervised and unsupervised learning algorithms for decision making in engineering.


Methodology. This research is descriptive, uses the Methodology of  Construction of an Algorithm for the systemic Learning of students of the first semester of the subject of ICTs (Mejía E. et al, 2018) adapted  to the use of artificial intelligence algorithms, which provides effective methods that allow to implement adescriptive statistic,  to  perform programs using functions and procedures within R. Tests were carried out  with third-semester students of  the ESPOCH Statistics career to determine with structured programming (before) and artificial intelligence algorithms (after).


Results. Students using structured programming to obtain these statistics only 36.84% reach n to complete the work, while students using artificial intelligence algorithms reach 84.21%, to conclude the work.


Conclusion. It is concluded that under the parameters of use of artificial intelligence algorithms to obtain a descriptive statistic for engineering, makes decisions with percentages that favor these techniques provided by the researchersis, this result  He tells us that the second option is the best, obtaining  in statistical terms very favorableconditions to insert this technique and methodology in engineering environments. Students with these algorithms that use supervised and unsupervised learning will have an extra plus when performing this type of statistics in the professional environment.

Article Details

Section
Education Law
Author Biography

MGS. EDWIN FERNANDO MEJÍA PEÑAFIEL, MSC. RAQUEL VIRGINIA COLCHA ORTIZ, MGS. WILLIAN GEOVANNY YANZA CHAVEZ, MSC. MARCO ANTONIO GAVILANES SAGÑAY, DRA. GLADYS LOLA LUJÁN JOHNSON

1MGS. EDWIN FERNANDO MEJÍA PEÑAFIEL, 2 MSC. RAQUEL VIRGINIA COLCHA ORTIZ, 3MGS. WILLIAN GEOVANNY YANZA CHAVEZ, 4 MSC. MARCO ANTONIO GAVILANES SAGÑAY, 5 DRA. GLADYS LOLA LUJÁN JOHNSON
1ORCID https://orcid.org/ 0000-0001-6888-4621
Facultad de Ciencias, Docente-Investigador de la “Escuela Superior Politécnica de Chimborazo (ESPOCH)” Riobamba 060103, Ecuador.
2ORCID https://orcid.org/ 0000-0002-3252-9158
Facultad de Administración de Empresas
Docente-Investigador de la “Escuela Superior Politécnica de Chimborazo (ESPOCH)” Riobamba 060103, Ecuador.
3ORCID ID https://orcid.org/0000-0002-9688-7309
Facultad de Administración de Empresas
Docente-Investigador de la “Escuela Superior Politécnica de Chimborazo (ESPOCH)” Riobamba 060103, Ecuador.
4ORCID https://orcid.org/0000-0002-7470-3732
Facultad de Administración de Empresas
Docente-Investigador de la “Escuela Superior Politécnica de Chimborazo (ESPOCH)” Riobamba 060103, Ecuador.
5Escuela de Posgrado Universidad César Vallejo, Sede Piura Perú
ORCID https://orcid.org/0000-0002-4727-6931

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