The ANOVA table tests the acceptability of the model from a statistical perspective. The Regression row displays information about the variation accounted for by your model. The Residual row displays information about the variation that is not accounted for by your model. The regression and residual sums of squares are approximately equal, which indicates that about half of the variation in polishing time is explained by the model. The significance value of the F statistic is less than 0.05, which means that the variation explained by the model is not due to chance. While the ANOVA table is a useful test of the model's ability to explain any variation in the dependent variable, it does not directly address the strength of that relationship. The model summary table reports the strength of the relationship between the model and the dependent variable. R, the multiple correlation coefficient, is the linear correlation between the observed and model-predicted values of the dependent variable. Its large value indicates a strong relationship. R Square, the coefficient of determination, is the squared value of the multiple correlation coefficient. It shows that about half the variation in time is explained by the model. As a further measure of the strength of the model fit, compare the standard error of the estimate in the model summary table to the standard deviation of time reported in the descriptive statistics table. Without prior knowledge of the diameter of a new product, your best guess for the polishing time would be about 35.8 minutes, with a standard deviation of 19.0. With the linear regression model, the error of your estimate is considerably lower, about 13.7.