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Erschienen in:
2019 | OriginalPaper | Buchkapitel
9. Additional Linear Modeling Topics
verfasst von : Chris Chapman, Elea McDonnell Feit
Erschienen in: R For Marketing Research and Analytics
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Abstract
As we noted in Chap. 7, the range of applications and methods in linear modeling and regression is vast. In this chapter, we discuss four additional topics in linear modeling that often arise in marketing: Handling highly correlated observations, which pose a problem known as collinearity, as mentioned in Sect. 7.2.1. In Sect. 9.1 we examine the problem in detail, along with ways to detect and remediate collinearity in a data set. Fitting models for yes/no, or binary outcomes, such as purchasing a product. In Sect. 9.2 we introduce logistic regression models to model binary outcomes and their influences. Fitting models for yes/no, or binary outcomes, such as purchasing a product. In Sect. 9.2 we introduce logistic regression models to model binary outcomes and their influences. Finding a model for the preferences and responses of individuals, not only for the sample as a whole. In marketing, we often wish to understand individual consumers and the diversity of behavior and product interest among people. In Sect. 9.3 we consider hierarchical linear models (HLM) for consumer preference in ratings-based conjoint analysis data.