The parameters of many nonlinear models are often better defined and correspond to biological processes that can be interpreted with respect to them. We often refer to such relationships as “black box” or empirical because there is no clear or obvious relationship between the model parameters and the biology of the response. However, while the numerical relationship can be modeled with a polynomial, it may be devoid of any practical meaning or significance. Distributions of these data often follow other more complex but definable equations.Īlmost any relationship can be fit using higher-order polynomials, as you learned in Chapter 13 on Multiple Regression. This is often caused by the nonlinear reaction of many physical and biological processes to time, temperature, and other conditions. Much of the experimental data that is gathered, however, is inherently nonlinear. The growing degree day formula for corn and other warm-season crops, for instance, assumes that a plant sustains linear growth between 50° F (10° C) and 86° F (30° C) (Fig. Many statistics are based on this assumption of linearity between variables because calculations are simpler. Relationships among variables in agronomic data are often assumed to be linear. Many relationships are curvilinear rather than linear.
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