Logistic Regression and Discriminant Analysis

Sebastian Tillmanns () and Manfred Krafft ()
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Sebastian Tillmanns: Westfälische Wilhelms-Universität Münster
Manfred Krafft: Westfälische Wilhelms-Universität Münster

A chapter in Handbook of Market Research, 2022, pp 329-367 from Springer

Abstract: 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.

Keywords: Dichotomous Dependent Variables; Discriminant Analysis; Logistic Regression (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-57413-4_20

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DOI: 10.1007/978-3-319-57413-4_20

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