Adaptive Regression for Modeling Nonlinear Relationships

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Focusing on the exploration of relationships in data, the book provides methods for determining linear versus nonlinear relationships and adapting models accordingly. It emphasizes the importance of incorporating nonlinearity in predictor variables within regression models to improve analysis of various outcome types. By addressing nonlinear dependencies, the book highlights how traditional linear analyses can lead to misleading conclusions, while nonlinear approaches can unveil valuable insights that enhance data understanding in applied research.