search for




 

Fit of the number of insurance solicitor’s turnovers using zero-inflated negative binomial regression
Journal of the Korean Data & Information Science Society 2017;28:1087-97
Published online September 30, 2017
© 2017 Korean Data & Information Science Society.

Heuiju Chun1

1Department of Statistics & Information, Dongduk Women’s University
Correspondence to: Heuiju Chun
Associate professor, Department of Statistics & Information Science, Dongduk Women’s University, Seoul 02748, Korea. Email: hjchun@dongduk.ac.kr
Received August 7, 2017; Revised September 14, 2017; Accepted September 15, 2017.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
This study aims to find the best model to fit the number of insurance solicitor’s turnovers of life insurance companies using count data regression models such as poisson regression, negative binomial regression, zero-inflated poisson regression, or zero-inflated negative binomial regression. Out of the four models, zero-inflated negative binomial model has been selected based on AIC and SBC criteria, which is due to over-dispersion and high proportion of zero-counts. The significant factors to affect insurance solicitor’s turnover found to be a work period in current company, a total work period as financial planner, an affiliated corporation, and channel management satisfaction. We also have found that as the job satisfaction or the channel management satisfaction gets lower as channel management satisfaction, the number of insurance solicitor’s turnovers increases. In addition, the total work period as financial planner has positive relationship with the number of insurance solicitor’s turnovers, but the work period in current company has negative relationship with it.
Keywords : Number of turnovers, over-dispersion, zero-inflated poisson regression, zeroinflated negative binomial regression