Relationships in business and operational data do not always follow a straight line, and choosing too little or too much flexibility can weaken a regression analysis. In this course, Nonlinear Regression Models, you'll gain the ability to recognize nonlinear structure and select regression approaches that represent it clearly. First, you'll explore visual and statistical signals of nonlinearity through an analysis of the estimated cost of generating electricity at potential offshore wind power plant sites. Next, you'll discover how predictor expansion, interactions, and polynomial regression capture complex relationships, and how cross-validation supports degree selection. Finally, you'll learn how extrapolation affects interpretation and how splines and generalized additive models represent flexible relationships. When you're finished with this course, you'll have the skills and knowledge of nonlinear regression modeling needed to choose, evaluate, and interpret models for workplace data


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