SumSq - Sum of squared error for each term except for the constant.ĭF - Degrees of freedom. The corresponding F-statistic is for testing the lack-of-fit by comparing the model residuals with the model-free variance estimate computed on the replicates.įirst column - Terms included in the model. If the data includes replicates, or multiple measurements at the same predictor values, then the anova partitions the error SumSq into the part for the replicates and the rest. The corresponding F-statistics are for testing the significance of the linear terms and higher-order terms as separate groups. If there are higher-order terms in the regression model, anova partitions the model SumSq into the part explained by the higher-order terms and the rest. PValue - p-value for the F-test on the model. In this example, it is 89.987, and in the linear regression display this F-statistic value is rounded up to 90. constant model in the linear regression display. The square root of this value is the root mean squared error in the linear regression display, or 4.09.į - F-statistic value, which is the same as F-statistic vs. For example, the mean squared error for the error term is 1488.8/89 = 16.728. MeanSq - Mean squared error for each term. There are four coefficients in the model, so the model DF is 4 – 1 = 3, and the DF for error term is 93 – 4 = 89. For example, MPG data vector has six NaN values and one of the data vectors, Horsepower, has one NaN value for a different observation, so the total degrees of freedom is 93 – 1 = 92. Degrees of freedom is n - 1 for the total, p - 1 for the model, and n - p for the error term, where n is the number of observations, and p is the number of coefficients in the model, including the intercept. SumSq - Sum of squares for the regression model, Model, the error term, Residual, and the total, Total.ĭF - Degrees of freedom for each term. Perform analysis of variance (ANOVA) for the model. For example, the model is significant with a p-value of 7.3816e-27. P-value - p-value for the F-test on the model. constant model - Test statistic for the F-test on the regression model, which tests whether the model fits significantly better than a degenerate model consisting of only a constant term. For example, the R-squared value suggests that the model explains approximately 75% of the variability in the response variable MPG.į-statistic vs. R-squared and Adjusted R-squared - Coefficient of determination and adjusted coefficient of determination, respectively. Root mean squared error - Square root of the mean squared error, which estimates the standard deviation of the error distribution. For example, the model has four predictors, so the Error degrees of freedom is 93 – 4 = 89. For example, Number of observations is 93 because the MPG data vector has six NaN values and the Horsepower data vector has one NaN value for a different observation, where the number of rows in X and MPG is 100.Įrror degrees of freedom - n – p, where n is the number of observations, and p is the number of coefficients in the model, including the intercept. Number of observations - Number of rows without any NaN values.
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