Regression Analysis

  • Bernd Skiera
  • Jochen Reiner
  • Sönke Albers
Living reference work entry


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.


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


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

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

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