The corresponding F-statistics in the F column assess the statistical significance of each term. This display decomposes the ANOVA table into the model terms. But, in this example, there are no replicated observations.ĭisplay the ANOVA table for the model terms. In that case, the F-statistic is for testing the lack of fit, that is, whether the fit is adequate or not. When there are replicated observations, the residual term is also separated into two parts first is the error due to the lack of fit, and second is the pure error independent from the model, obtained from the replicated observations. The corresponding F-statistics in the F column are for testing the significance of the linear and nonlinear terms as separate groups. There are three linear terms in the model (one Smoker indicator variable, Weight, and Age). Since there are two non-linear terms ( Weight^2 and the interaction between Weight and Age), the nonlinear degrees of freedom in the DF column is 2. This display separates the variability in the model into linear and nonlinear terms. You can also programmatically access the F-statistic of the model. There might be other predictor (explanatory) variables that are not included in the current model. The R-squared value of 0.528 means the model explains about 53% of the variability in the response. The model is significant at the 5% significance level. The F-statistic of the linear fit versus the constant model is 21, with a p-value of 4.81e-14. R-squared: 0.528, Adjusted R-Squared: 0.503į-statistic vs. Number of observations: 100, Error degrees of freedom: 94 BloodPressure ~ 1 + Smoker + Age*Weight + Weight^2
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