Workers’ compensation insurance (WCI) is the highest cost to an employer following accidents. It is needed to predict the benefits value without taking into account the past records of an employee, which is not readily available in most cases. Employment and workers’ compensation data were acquired from the Bureau of Labor Statistics and the National Academy of Social Insurance, respectively. The statistical model was developed with SAS using multiple regression and the process was simplified using analysis of covariance (ANCOVA). The model predicted future values of workers compensation given a known number of covered workers for all U.S. states. The model is statistically proven to be fit for all states. The states were compared on the basis of percentage deviation from the actual values. By using this model, insurance companies and policymakers can have better understanding of workers’ compensation trend and they can quotes premiums and develop policies more accurately.
| Identifer | oai:union.ndltd.org:ndsu.edu/oai:library.ndsu.edu:10365/28850 |
| Date | January 2018 |
| Creators | Azmi, Muhammad Arsalan Raza |
| Publisher | North Dakota State University |
| Source Sets | North Dakota State University |
| Detected Language | English |
| Type | text/thesis |
| Format | application/pdf |
| Rights | NDSU Policy 190.6.2, https://www.ndsu.edu/fileadmin/policy/190.pdf |
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