Adj SS Model The adjusted sum of squares for the model is the difference between the total sum of square and the error sum of squares in the model compared to a model that uses only the mean of the response. In the Analysis of Variance table, Minitab separates the sums of squares into different components that describe the variation due to different sources. The order of the predictors in the model does not affect the calculation of the adjusted sums of squares. The more DF for pure error, the greater the power of the lack-of-fit test.ДЄdjusted sums of squares are measures of variation for different components of the model. The lack of fit test uses the degrees of freedom for lack-of-fit. The DF for lack-of-fit allow a test of whether the model form is adequate. If the two conditions are met, then the two parts of the DF for error are lack-of-fit and pure error. If the center points are in different blocks and the term for blocks is in the model, the center points are not replicated points. For example, if the design has two center points in the same block and no covariate, the center points are replicated points. Replicates are observations where each predictor has the same value, including blocks and covariates if those terms are in the model or if a stepwise procedure excluded those terms. The second condition is that the data contain replicates. The center point term is always for curvature, so the center point term does not count as a term that you can fit with the data that is not in the current model.

For example, if you the design has blocks but the blocks are not in the model. The first condition is that there must be terms that you can fit with the data that are not in the current model. DF for error If two conditions are met, then Minitab partitions the DF for error that are not for curvature. If the term for center points is not in the model, the row for curvature is part of the error that is used to test terms that are in the model. If the term for center points is in the model, the row for curvature is part of the model. DF for curvature If a design has center points, then one DF is for the test for curvature.

Increasing the number of terms in your model uses more information, which decreases the DF available to estimate the variability of the parameter estimates. Increasing your sample size provides more information about the population, which increases the total DF. The DF for a term show how much information that term uses. The total DF is determined by the number of observations in your sample. The analysis uses that information to estimate the values of unknown population parameters. The total degrees of freedom (DF) are the amount of information in your data.

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