Abstract
Herein, an empirical example of this new research will be shown. Two machine learning techniques are used for this example during unsupervised learning with clustering and supervised learning with artificial neural networks (ANNs). A basic example has been produced and confirmed using a reliable dataset from Bureau of Labor Statistics. In part 1, the research methodology and data are described.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bureau of Labor Statistics. (2014). The NLSY79 sample: An introduction. Retrieved from https://www.nlsinfo.org/content/cohorts/nlsy79/intro−to−the−sample/nlsy79−sample−introduction.
Deacon, R. E., & Firebaugh, F. M. (1988). Family resource management: Principles and applications (2nd ed.). Boston, MA: Allyn & Bacon.
Kaufman, L., & Rousseeuw, P. (1990). Finding groups in data: An introduction to cluster analysis. New York, NY: Wiley.
Mirkin, B. (2011). Core concepts in data analysis: Summarization, correlation and visualization. New York, NY: Springer.
National Governors Association. (2015). Governors roster 2010: Governor’s political affiliations & terms of office. Retrieved from http://www.nga.org/files/live/sites/NGA/files/pdf/GOVLIST2010.PDF.
US Census Bureau. (2015). Composition of Congress by politician party. Retrieved from http://www.census.gov/compendia/statab/2011/tables/11s0404.pdf.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2020 The Author(s)
About this chapter
Cite this chapter
Heo, W. (2020). Empirical Analysis Part 1 Methodology and Data: Empirical Example of Predicting the Demand for Life Insurance by Using the Dynamic Systemic Framework. In: The Demand for Life Insurance. Palgrave Pivot, Cham. https://doi.org/10.1007/978-3-030-36903-3_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-36903-3_5
Published:
Publisher Name: Palgrave Pivot, Cham
Print ISBN: 978-3-030-36902-6
Online ISBN: 978-3-030-36903-3
eBook Packages: Economics and FinanceEconomics and Finance (R0)