Some datasets can require substantial effort to find acceptable starting values. Unlike linear regression, you also need to supply starting values for the nonlinear algorithm. The downside is that it can take considerable effort to choose the nonlinear function that creates the best fit for the particular shape of the curve. The main positive is that nonlinear regression provides the most flexible curve-fitting functionality. The fact that you can fit nonlinear models with virtually an infinite number of functional forms is both its strength and downside. As I mentioned earlier, nonlinear regression can be harder to perform. Now, let’s fit the same data but using nonlinear regression. Related posts: Linear Regression and Seven Classical Assumptions of OLS Linear Regression Example of a nonlinear regression model Consequently, it’s time to try nonlinear regression. There’s nothing more we can do with linear regression. Our linear regression model can’t adequately fit the curve in the data.
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