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Reexamining the Effect of Income

A Comparison between Linear and Nonlinear Models

Market researchers frequently use linear regression models to perform critical analyses. These models examine how a predictor, such as a sudden increase in income, may determine an outcome, such as the likelihood that someone will donate to charity. Some sophisticated models even include interactions that show how the relationship between a predictor and an outcome vary over some key feature such as a customer’s annual salary.

Linear Model

The problem with this approach is that the relationship between a predictor and an outcome is assumed to be linear. Customer behavior is not always that simple. By using nonlinear models[1],[2] it is possible to gain a more accurate picture of whether someone will donate to charity after a sudden increase in their salary.

Nonlinear Model

A comparison between the two models reveals how linear models may conceal valuable information. Nonlinear models help you hear the voice of the customer more clearly.

Model Comparison 2

Find out more about how nonlinear models can help your business. Who knows what valuable secrets lie hidden in the data?

 

 

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[1] Hastie T, Tibshirani R. Varying-coefficient models. Journal of the Royal Statistical Society, Series B. 1993; 55:757–779.

[2] Hoover DR, Rice JA, Wu CO, Yang LP. Nonparametric smoothing estimates of time-varying coefficient models with longitudinal data. Biometrika. 1998; 85:809–822.