A Conceptual and Pragmatic Review of Regression Analysis for Predictive Analytics

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Author(s): Sema A. Kalaian (Eastern Michigan University, USA), Rafa M. Kasim (Indiana Tech University, USA)and Nabeel R. Kasim (University of Michigan, USA)
Copyright: 2017
Pages: 16
Source title: Organizational Productivity and Performance Measurements Using Predictive Modeling and Analytics
Source Author(s)/Editor(s): Madjid Tavana (La Salle University, USA), Kathryn Szabat (La Salle University, USA)and Kartikeya Puranam (La Salle University, USA)
DOI: 10.4018/978-1-5225-0654-6.ch014

Keywords: Business Information Systems / Business Science Reference / Data Analysis & Statistics / Library & Information Science

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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.

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A Conceptual and Pragmatic Review of Regression Analysis for Predictive Analytics

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