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Linear regression has gained widespread popularity in literature. However, many applications of linear regression have been in situations in which the model data are collinear or ‘ill-conditioned.’ Collinearity makes it difficult to ascertain the effect of any single variable, produce biased estimates of coefficients for regressors. Although conducting a multicollinearity diagnosis does not solve nor lead to any specific solution of the problem, realizing its potential impact on findings from regression analysis allows a more careful interpretation of data. In this paper, we examine the performance of ratio of standardized regression slope to semi-partial correlation in an ill-conditioned model with some existing methods of collinearity diagnostics. The result shows that ratio of standardized regression slope to semi-partial correlation seem to be more sensitive to the multicollinearity among predictors in the model than the variance inflation factor and condition number methods.

Performance of Ratio of Standardized Regression Slope to Semi-Partial Correlation in Identifying Ill-Conditioned Model