What makes a model nonlinear

Topics: Regression Analysis. This confusion is understandable because both types can model curves. A model is linear when each term is either a constant or the product of a parameter and a predictor variable. A linear equation is constructed by adding the results for each term. This constrains the equation to just one basic form:. In statistics, a regression equation or function is linear when it is linear in the parameters. While the equation must be linear in the parameters, you can transform the predictor variables in ways that produce curvature.

For instance, you can include a squared variable to produce a U-shaped curve. This model is still linear in the parameters even though the predictor variable is squared. You can also use log and inverse functional forms that are linear in the parameters to produce different types of curves.

Lohninger on statistics, data analysis and chemometrics Home Basic Concepts Linear vs. Nonlinear Models. Linear vs. A logistic population growth model can provide estimates of the population for periods that were not measured, and predictions of future population growth. Independent and dependent variables used in nonlinear regression should be quantitative. Categorical variables, like region of residence or religion, should be coded as binary variables or other types of quantitative variables.

In order to obtain accurate results from the nonlinear regression model, you should make sure the function you specify describes the relationship between the independent and dependent variables accurately. Good starting values are also necessary.

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Popular Courses. Key Takeaways Both linear and nonlinear regression predict Y responses from an X variable or variables. Nonlinear regression is a curved function of an X variable or variables that is used to predict a Y variable Nonlinear regression can show a prediction of population growth over time.