Regression Analysis

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


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.


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.


  1. Aiken, L. S., & West, S. G. (1991). Multiple regression: testing and interpreting interactions. Thousand Oaks, CA: Sage.Google Scholar
  2. Baum, C. F. (2006). An introduction to modern econometrics using Stata. College Station, TX: Stata Press.Google Scholar
  3. Burnham, K. P., & Anderson, D. R. (2013). Model Selection and multimodel inference: A practical information-theoretic approach (2nd ed.). New York, NJ: Springer.Google Scholar
  4. Cohen, J. (1994). The earth is round (p < .05). The American Psychologist, 49(912), 997–1003.CrossRefGoogle Scholar
  5. Cook, R. D., & Weisberg, S. (1983). Diagnostics for heteroscedasticity in regression. Biometrika, 70(1), 1–10.CrossRefGoogle Scholar
  6. Durbin, J., & Watson, G. S. (1951). Testing for serial correlation in least squares regression, II. Biometrika, 38(1–2), 159–179.CrossRefGoogle Scholar
  7. Fabozzi, F. J., Focardi, S. M., Rachev, S. T., & Arshanapalli, B. G. (2014). The basics of financial econometrics: tools, concepts, and asset management applications. Hoboken, NJ: John Wiley & Sons.CrossRefGoogle Scholar
  8. Field, A. (2013). Discovering statistics using SPSS (4th ed.). London: Sage.Google Scholar
  9. Green, S. B. (1991). How many subjects does it take to do a regression analysis? Multivariate Behavioral Research, 26(3), 499–510.CrossRefGoogle Scholar
  10. Greene, W. H. (2011). Econometric analysis (7th ed.). Upper Saddle River, NJ: Prentice Hall.Google Scholar
  11. Hair Jr., J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Boston, MA: Cengage.Google Scholar
  12. Hill, C., Griffiths, W., & Lim, G. C. (2011). Principles of econometrics (4th ed.). Hoboken, NJ: John Wiley & Sons.Google Scholar
  13. Kelley, K., & Maxwell, S. E. (2003). Sample size for multiple regression: Obtaining regression coefficients that are accurate, not simply significant. Psychological Methods, 8(3), 305–321.CrossRefGoogle Scholar
  14. Mason, C. H., & Perreault Jr., W. D. (1991), Collinearity, power, and interpretation of multiple regression analysis. Journal of Marketing Research, 28(3), 268–280.CrossRefGoogle Scholar
  15. Mooi, E. A., & Frambach, R. T. (2009). A stakeholder perspective on buyer–supplier conflict. Journal of Marketing Channels, 16(4), 291–307.CrossRefGoogle Scholar
  16. O’brien, R. M. (2007). A caution regarding rules of thumb for variance inflation factors. Quality & Quantity, 41(5), 673–690.CrossRefGoogle Scholar
  17. Paternoster, R., Brame, R., Mazerolle, P., & Piquero, A. (1998). Using the correct statistical test for the equality of regression coefficients. Criminology, 36(4), 859–866.CrossRefGoogle Scholar
  18. Ramsey, J. B. (1969). Test for specification errors in classical linear least-squares regression analysis. Journal of the Royal Statistical Society, Series B, 31(2), 350–371.Google Scholar
  19. Treiman, D. J. (2014). Quantitative data analysis: Doing social research to test ideas. Hoboken, NJ: John Wiley & Sons.Google Scholar
  20. VanVoorhis, C. R. W., & Morgan, B. L. (2007). Understanding power and rules of thumb for determining sample sizes. Tutorials in Quantitative Methods for Psychology, 3(2), 43–50.CrossRefGoogle Scholar

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
  2. Iacobucci, D. (2008). Mediation analysis: Quantitative applications in the social sciences. Thousand Oaks, CA: Sage.CrossRefGoogle Scholar
  3. Shmueli, G. (2010). To explain or to predict? Statistical Science, 25(3), 289–310.CrossRefGoogle Scholar
  4. Spiller, S. A., Fitzsimons, G. J., Lynch Jr., J. G., & McClelland, G. H. (2013). Spotlights, floodlights, and the magic number zero: Simple effects tests in moderated regression. Journal of Marketing Research, 50(2), 277–288.CrossRefGoogle Scholar
  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

Personalised recommendations