Life insurance is an important need for many people in the United States. It is an insurance that
the purchaser will never get to receive the benefit, instead, upon death, their beneficiaries will
receive it to supplement their income. This study will be analyzing the Health and Retirement
Study dataset in order to identify and model the variables that are most related and/or correlated
to the purchasing behavior of life insurance in the United States. Using different mathematical
applications, such as linear modeling through R and correlations through Excel, the data has been
scanned thoroughly to isolate potential significant variables. There is a lot of literature on life
insurance itself and the purchasing behavior of life insurance, however, there is limited literature
using the Health and Retirement Study dataset. Therefore, this is an opportunity to make new
findings on this topic. The key findings show that there were seven variables, such as age and
education level, that most correlated and related to the purchasing behavior of life insurance.
Based on these findings, this study will provide some recommendations that can help life
insurance companies better understand their market and utilize the findings accordingly.