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Not all models are linear. The truth is that there are more nonlinear models than linear models that exist in real-life applications. Although different from the standard linear regression that we have known, nonlinear regression can and should be used to model nonlinear relationships present in the real data. There are many different types of nonlinear models as discussed in Bates and Watts (1998). An example to model the nonlinear relationship between the proportion of available chlorine in chlorine solution and the length of time in weeks from a chlorine data using SAS/R/Stata/SPSS to illustrate the model fitting can be found in Chapter 10 of Wilson and Chen (2021).To illustrate nonlinear multi-level modeling, we use this chapter to introduce the nonlinear regression first as the introduction for the next chapter on nonlinear multilevel modeling. In this chapter, we focus on a population dynamics model (i.e., population growth model), which is a typical nonlinear model.

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