TY  - JOUR
T1  - Selection of the Best Regression Model to Explain the Variables that
Influence Labor Accident Electrical Company Case
AU - Izquierdo, Noel Varela AU - Fernandez, Damaise Perez AU - Lezama, Omar Bonerge Pineda AU - Viloria, Amelec 
JO  - Journal of Engineering and Applied Sciences
VL  - 12
IS  - 11
SP  - 2956
EP  - 2962
PY  - 2017
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2017.2956.2962
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2017.2956.2962
KW  - Labor accident
KW  -regression models
KW  -multivariate statistics
KW  -information criteria
KW  -highest percentage
KW  -negative binomial models
AB  - The present research proposes an alternative to select the best model that explains the relation of the
variables that influence the labor accident in an electric power company. Among the techniques and tools used
are those of occupational safety and health management, multivariate statistics, generalized linear models, the
values of the deviation percentage explained and the adjusted percentage and the Akaike and Bayesian
information criteria. The following variables were identified through the mentioned techniques, management
commitment, compliance with legislation, prevention planning, training in prevention, updating of occupational
risk management and policies that have a significant influence on work accident and through. The percentages
and of the previously mentioned criteria were able to show that the logistic regression is the best model that
explains the labor accident by presenting the highest percentage and the lowest values of the criteria when
compared with the poisson regression and negative binomial models.
ER  -