The following shows how to interpret significant main effects and interaction effects.
 

If the pvalue in the ANOVA table indicates a statistically significant main effect or interaction effect, use the means table to understand the group differences.
For main effects, the table displays the groups within each factor and their fitted means. For interaction effects, the table displays all possible combinations of groups across both factors.
 

Examine the means table to understand the differences between the groups in your data. Look for differences in group means. If the interaction term is statistically significant, do not interpret the main effects without considering the interaction effects.
In these results, the interaction effect is statistically significant. The interaction effect indicates that the relationship between MetalType and Strength depends on the value of SinterTime. For example, if you use MetalType 2, SinterTime150 is associated with the highest mean strength. However, if you use MetalType 1, SinterTime 100 is associated with the highest mean strength.
S is measured in the units of the response variable and represents the how far the data values fall from the fitted values. The lower the value of S, the better the model describes the response. However, a low S value by itself does not indicate that the model meets the model assumptions. You should check the residual plots to verify the assumptions.
R^{2} is the percentage of variation in the response that is explained by the model. The higher the R^{2} value, the better the model fits your data. R^{2} is always between 0% and 100%.
R^{2} always increases when you add additional predictors to a model. For example, the best fivepredictor model will always have an R^{2} that is at least as high the best fourpredictor model. Therefore, R^{2} is most useful when you compare models of the same size.
A high R^{2} value does not indicate that the model meets the model assumptions. You should check the residual plots to verify the assumptions.
Use adjusted R^{2} when you want to compare models that have different numbers of predictors. R^{2} always increases when you add a predictor to the model, even when there is no real improvement to the model. The adjusted R^{2} value incorporates the number of predictors in the model to help you choose the correct model.
Use predicted R^{2} to determine how well your model predicts the response for new observations. Models that have larger predicted R^{2} values have better predictive ability.
A predicted R^{2} that is substantially less than R^{2} may indicate that the model is overfit. An overfit model occurs when you add terms for effects that are not important in the population, although they may appear important in the sample data. The model becomes tailored to the sample data and therefore, may not be useful for making predictions about the population.
Predicted R^{2} can also be more useful than adjusted R^{2} for comparing models because it is calculated with observations that are not included in the model calculation.
 

In these results, the predictors explain 63.28% of the variation in the response. The adjusted R^{2} is 46.96%, which is a decrease of 17%. The low predicted R^{2} value (17.37%) indicates that the model does not predict new observations as well as it fits the sample data. Thus, you should not use the model to make generalizations beyond the sample data.
Use the residual plots to help you determine whether the model is adequate and meets the assumptions of the analysis. If the assumptions are not met, the model may not fit the data well and you should use caution when you interpret the results.
Use the residuals versus fits plot to verify the assumption that the residuals are randomly distributed and have constant variance. Ideally, the points should fall randomly on both sides of 0, with no recognizable patterns in the points.
Pattern  What the pattern may indicate 

Fanning or uneven spreading of residuals across fitted values  Nonconstant variance 
A point that is far away from zero  An outlier 
Use the normal probability plot of residuals to verify the assumption that the residuals are normally distributed. The normal probability plot of the residuals should approximately follow a straight line.
Pattern  What the pattern may indicate 

Not a straight line  Nonnormality 
A point that is far away from the line  An outlier 
Changing slope  An unidentified variable 