Logistic Regression and Discriminant Analysis

Tillmanns Sebastian, Krafft Manfred


Abstract
Questions like whether a customer is going to buy a product (purchase vs. non-purchase) or whether a borrower is creditworthy (pay off debt vs. credit default) are typical in business practice and research. From a statistical perspective, these questions are characterized by a dichotomous dependent variable. Traditional regression analyses are not suitable for analyzing these types of problems, because the results that such models produce are generally not dichotomous. Logistic regression and discriminant analysis are approaches using a number of factors to investigate the function of a nominally (e.g., dichotomous) scaled variable. This chapter covers the basic objectives, theoretical model considerations, and assumptions of discriminant analysis and logistic regression. Further, both approaches are applied in an example examining the drivers of sales contests in companies. The chapter ends with a brief comparison of discriminant analysis and logistic regression.



Publication type
Chapter in Book

Peer reviewed
Yes

Publication status
Published

Year
2017

Book title
Handbook of Market Research

Editor
Homburg Christian, Klarmann Martin, Vomberg Arnd

Start page
1

End page
39

Pages range
1-39

Publisher
Springer

DOI