Diagnosing and revising logistic regression models: Effect on internal solitary wave propagation

Abstract

Purpose

This study aims to apply a systematic statistical approach, including several plot indexes, to diagnose the goodness of fit of a logistic regression model, and then to detect the outliers and influential observations of the data from experimental data.

Design/methodology/approach

The proposed statistical approach is applied to analyze some experimental data on internal solitary wave propagation.

Findings

A suitable logistic regression model in which the relationship between the response variable and the explanatory variables is found. The problem of multicollinearity is tested. It was found that certain observations would not have the problem of multicollinearity. The P‐values for both the Pearson and deviance χ² tests are greater than 0.05. However, the Pearson χ² value is larger than the degrees of freedom. This finding indicates that although this model fits the data, it has a slight overdispersion. After three outliers and influential observations (cases 11, 27, and 49) are removed from the data, and the remaining observations are refitted the goodness‐of‐fit of the revised model to the data is improved.

Practical implications

A comparison of the four predictive powers: R², max‐rescaled R², the Somers' D, and the concordance index c, shows that the revised model has better predictive abilities than the original model.

Originality/value

The goodness‐of‐fit and prediction ability of the revised logistic regression model are more appropriate than those of the original model.

Citation

Chen, C., Yang, H.P., Chen, C. and Chen, T. (2008), "Diagnosing and revising logistic regression models: Effect on internal solitary wave propagation", Engineering Computations, Vol. 25 No. 2, pp. 121-139. https://doi.org/10.1108/02644400810855940