Regression Analysis

Living reference work entry

Abstract

Linear regression analysis is one of the most important statistical methods. It examines the linear relationship between a metric-scaled dependent variable (also called endogenous, explained, response, or predicted variable) and one or more metric-scaled independent variables (also called exogenous, explanatory, control, or predictor variable). We illustrate how regression analysis work and how it supports marketing decisions, e.g., the derivation of an optimal marketing mix. We also outline how to use linear regression analysis to estimate nonlinear functions such as a multiplicative sales response function. Furthermore, we show how to use the results of a regression to calculate elasticities and to identify outliers and discuss in details the problems that occur in case of autocorrelation, multicollinearity and heteroscedasticity. We use a numerical example to illustrate in detail all calculations and use this numerical example to outline the problems that occur in case of endogeneity.

Keywords

Regression analysis Marketing mix modeling Elasticities Multicollinearity Autocorrelation Outlier detection Endogeneity Sales response function 

References

  1. Albers, S. (2012). Optimizable and implementable aggregate response modeling for marketing decision support. International Journal of Research in Marketing, 29(2), 111–122.CrossRefGoogle Scholar
  2. Albers, S., Mantrala, M. K., & Sridhar, S. (2010). Personal selling elasticities: A meta-analysis. Journal of Marketing Research, 47(5), 840–853.CrossRefGoogle Scholar
  3. Assmus, G., Farley, J. W., & Lehmann, D. R. (1984). How advertising affects sales: A meta-analysis of econometric results. Journal of Marketing Research, 21(1), 65–74.CrossRefGoogle Scholar
  4. Bijmolt, T. H. A., van Heerde, H., & Pieters, R. G. M. (2005). New empirical generalizations on the determinants of price elasticity. Journal of Marketing Research, 42(2), 141–156.CrossRefGoogle Scholar
  5. Chatterjee, S., & Hadi, A. S. (1986). Influential observations, high leverage points, and outliers in linear regressions. Statistical Science, 1(3), 379–416.CrossRefGoogle Scholar
  6. Ebbes, P., Papies, D., & van Heerde, H. J. (2017). Dealing with endogeneity: A non-technical guide for marketing researchers. In C. Homburg, M. Klarmann, & A. Vomberg (Eds.), Handbook of market research. Berlin: Springer.Google Scholar
  7. Greene, W. H. (2008). Econometric analysis (6th ed.). Upper Saddle River: Pearson.Google Scholar
  8. Gujarati, D. N. (2003). Basic econometrics (4th ed.). New York: McGraw Hill.Google Scholar
  9. Hair, J. F., Black, W. C., Babin, J. B., & Anderson, R. E. (2014). Multivariate data analysis (7th ed.). Upper Saddle River: Pearson.Google Scholar
  10. Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling (PLS-SEM) (2nd ed.). Thousand Oaks: Sage.Google Scholar
  11. Hanssens, D. M., Parsons, L. J., & Schultz, R. L. (1990). Market response models: Econometric and time series analysis. Boston: Springer.Google Scholar
  12. Hsiao, C. (2014). Analysis of panel data (3rd ed.). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  13. Irwin, J. R., & McClelland, G. H. (2001). Misleading heuristics and moderated multiple regression models. Journal of Marketing Research, 38(1), 100–109.CrossRefGoogle Scholar
  14. Koutsoyiannis, A. (1977). Theory of econometrics (2nd ed.). Houndmills: MacMillan.CrossRefGoogle Scholar
  15. Laurent, G. (2013). EMAC distinguished marketing scholar 2012: Respect the data! International Journal of Research in Marketing, 30(4), 323–334.CrossRefGoogle Scholar
  16. Leeflang, P. S. H., Wittink, D. R., Wedel, M., & Neart, P. A. (2000). Building models for marketing decisions. Berlin: Kluwer.CrossRefGoogle Scholar
  17. Lodish, L. L., Abraham, M. M., Kalmenson, S., Livelsberger, J., Lubetkin, B., Richardson, B., & Stevens, M. E. (1995). How TV advertising works: A meta-analysis of 389 real world split cable T. V. advertising experiments. Journal of Marketing Research, 32(2), 125–139.CrossRefGoogle Scholar
  18. Pindyck, R. S., & Rubenfeld, D. (1998). Econometric models and econometric forecasts (4th ed.). New York: McGraw-Hill.Google Scholar
  19. Sethuraman, R., Tellis, G. J., & Briesch, R. A. (2011). How well does advertising work? Generalizations from meta-analysis of brand advertising elasticities. Journal of Marketing Research, 48(3), 457–471.CrossRefGoogle Scholar
  20. Snijders, T. A. B., & Bosker, R. J. (2012). Multilevel analysis: An introduction to basic and advanced multilevel modeling (2nd ed.). London: Sage.Google Scholar
  21. Stock, J., & Watson, M. (2015). Introduction to econometrics (3rd ed.). Upper Saddle River: Pearson.Google Scholar
  22. Tellis, G. J. (1988). The price sensitivity of selective demand: A meta-analysis of econometric models of sales. Journal of Marketing Research, 25(4), 391–404.CrossRefGoogle Scholar
  23. White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838.CrossRefGoogle Scholar
  24. Wooldridge, J. M. (2009). Introductory econometrics: A modern approach (4th ed.). Mason: South-Western Cengage.Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Goethe University FrankfurtFrankfurtGermany
  2. 2.Kuehne Logistics UniversityHamburgGermany

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