![]() ![]() In particular, it is emphasized in Kutner et al. Alternatively, model validation refers to the plausibility and generalizability of the regression function in terms of the stability and suitability of the regression coefficients. In the process of model selection, residual analysis and diagnostic checking are employed to identify influential observations, leverage, outliers, multicollinearity, and other lack of fit problems. Among the methodological issues and statistical implications of regression analysis, model adequacy and validity represent two vital aspects for justifying the usefulness of the underlying regression model. , and Montgomery, Peck, and Vining, among others. ![]() General guidelines and fundamental principles on regression analysis have been well documented in the standard texts of Cohen et al. The extensive utility incurs continuous investigations to give various interpretations, extensions, and computing algorithms for the development and formulation of empirical models. Regression analysis is the most commonly applied statistical method of all scientific fields. ![]()
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