A conceptual and pragmatic review of regression analysis for predictive analytics
Document Type
Book Chapter
Publication Date
2017
Department/School
Technology Studies
Publication Title
Decision management: Concepts, methodologies, tools, and applications
Abstract
Regression analysis and modeling are powerful predictive analytical tools for knowledge discovery through examining and capturing the complex hidden relationships and patterns among the quantitative variables. Regression analysis is widely used to: (a) collect massive amounts of organizational performance data such as Web server logs and sales transactions. Such data is referred to as Big Data" and (b) improve transformation of massive data into intelligent information (knowledge) by discovering trends and patterns in unknown hidden relationships. The intelligent information can then be used to make informed data-based predictions of future organizational outcomes such as organizational productivity and performance using predictive analytics such as regression analysis methods. The main purpose of this chapter is to present a conceptual and practical overview of simple- and multiple- linear regression analyses.
Link to WorldCat Entry
Recommended Citation
Kalaian, S., Kasim, R. M., & Kasim, N. R. (2017). A conceptual and pragmatic review of regression analysis for predictive analytics. In Information Resources Management Association (Ed.), Decision management: Concepts, methodologies, tools, and applications. IGI Global. https://doi.org/10.4018/978-1-5225-1837-2