@article{MAKHILLJEAS2017121114517,
    title = {Selection of the Best Regression Model to Explain the Variables that
Influence Labor Accident Electrical Company Case},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {12},
    number = {11},
    pages = {2956-2962},
    year = {2017},
    issn = {1816-949x},
    doi = {jeasci.2017.2956.2962},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2017.2956.2962},
    author = {Noel Varela,Damaise Perez,Omar Bonerge Pineda and},
    keywords = {Labor accident,regression models,multivariate statistics,information criteria,highest percentage,negative binomial models},
    abstract = {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.}
    }