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Nonlinear Explanatory Multiple Regression Models

In: Business Statistics for Competitive Advantage with Excel 2016

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  • Cynthia Fraser

    (University of Virginia, McIntire School of Commerce)

Abstract

In this chapter, the insights offered by nonlinear regression models built with multiple drivers are examined. In many cases, a dependent variable and its drivers are approximately Normal, with skewness between −1 and +1. Linear regression models often provide good fit for either cross sectional or time series, and are often valid for forecasting in time series. However, the choice of a nonlinear model enables acknowledgement of the interactions inherent in many cases. With nonlinear models, the impact of each driver depends on the values of other drivers. In a nonlinear model, driver influences are multiplicative, which adds an element of realism relative to linear regression models with constant response, and provides richer insights from sensitivity analysis.

Suggested Citation

  • Cynthia Fraser, 2016. "Nonlinear Explanatory Multiple Regression Models," Springer Books, in: Business Statistics for Competitive Advantage with Excel 2016, chapter 0, pages 447-472, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-32185-1_14
    DOI: 10.1007/978-3-319-32185-1_14
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