The interpretation of the coefficients for a curvilinear relationship is less intuitive than linear relationships.Īs a refresher, in linear regression, you can use polynomial terms model curves in your data. Now, let’s move on to interpreting the coefficients for a curvilinear relationship, where the effect depends on your location on the curve. A linear relationship indicates that the change remains the same throughout the regression line. The previous linear relationship is relatively straightforward to understand. Use Polynomial Terms to Model Curvature in Linear Models For more information, read my post Contour Plots: Using, Examples, and Interpreting. For multiple linear regression, the interpretation remains the same.Ĭontour plots can graph two independent variables and the dependent variable. However, plots can display only results from simple regression-one predictor and the response. That’s why a near zero coefficient suggests there is no effect-and you’d see a high (insignificant) p-value to go along with it. For this scenario, the mean weight wouldn’t change no matter how far along the line you move. Let’s suppose that the regression line was flat, which corresponds to a coefficient of zero. Consequently, we can’t shift along the line by a full meter for these data. In this case, the height and weight data were collected from middle-school girls and range from 1.3 m to 1.7 m. Keep in mind that it is only safe to interpret regression results within the observation space of your data. If you move to the right along the x-axis by one meter, the line increases by 106.5 kilograms. The regression line on the graph visually displays the same information. If your height increases by 1 meter, the average weight increases by 106.5 kilograms. This coefficient represents the mean increase of weight in kilograms for every additional one meter in height. The height coefficient in the regression equation is 106.5.
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