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Regression Analysis

  • Marko Sarstedt
  • Erik Mooi
Chapter
Part of the Springer Texts in Business and Economics book series (STBE)

Abstract

We first provide comprehensive, but simple, access to essential regression knowledge by discussing how regression analysis works, the requirements and assumptions on which it relies, and how you can specify a regression analysis model that allows you to make critical decisions for your business, clients, or project. Each step involved in regression analysis is linked to its execution in SPSS. We show how to use a range of SPSS’s easy-to-learn statistical procedures that underlie regression analysis, which will allow you to analyze, chart, and validate regression analysis results and to assess your analysis’s robustness. Interpretation of SPSS output can be difficult, but we make this easier by means of an annotated case study. We conclude with suggestions for further readings on the use, application, and interpretation of regression analysis.

Keywords

Regression Analysis Assumptions Heartland Region Error Regression Model Dependent Variable Sales Sample Size Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Further Reading

  1. Echambadi, R., & Hess, J. D. (2007). Mean-centering does not alleviate collinearity problems in moderated multiple regression models. Marketing Science, 26(3), 438–445.CrossRefGoogle Scholar
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  5. Zhao, X., Lynch, J. G., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(2), 197–206.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Marko Sarstedt
    • 1
  • Erik Mooi
    • 2
  1. 1.Faculty of Economics and ManagementOtto-von-Guericke- University MagdeburgMagdeburgGermany
  2. 2.Department of Management and MarketingThe University of MelbourneParkville, VICAustralia

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