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The use of modern robust regression analysis with graphics: an example from marketing

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Listed:
  • Riani, Marco
  • Atkinson, Anthony C.
  • Morelli, Gianluca
  • Corbellini, Aldo

Abstract

Routine least squares regression analyses may sometimes miss important aspects of data. To exemplify this point we analyse a set of 1171 observations from a questionnaire intended to illuminate the relationship between customer loyalty and perceptions of such factors as price and community outreach. Our analysis makes much use of graphics and data monitoring to provide a paradigmatic example of the use of modern robust statistical tools based on graphical interaction with data. We start with regression. We perform such an analysis and find significant regression on all factors. However, a variety of plots show that there are some unexplained features, which are not eliminated by response transformation. Accordingly, we turn to robust analyses, intended to give answers unaffected by the presence of data contamination. A robust analysis using a non-parametric model leads to the increased significance of transformations of the explanatory variables. These transformations provide improved insight into consumer behaviour. We provide suggestions for a structured approach to modern robust regression and give links to the software used for our data analyses.

Suggested Citation

  • Riani, Marco & Atkinson, Anthony C. & Morelli, Gianluca & Corbellini, Aldo, 2025. "The use of modern robust regression analysis with graphics: an example from marketing," LSE Research Online Documents on Economics 126922, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:126922
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    References listed on IDEAS

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    More about this item

    Keywords

    AVAS; Box-Cox transformation; brushing; forward search; generalized additive model (GAM); linked plots;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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