失拟的解释

细履平沙

Interpretation of Lack of Fit
Lack of fit (LOF) values often give experimenters concern. This page is meant to give experimenters a better understanding of this statistic and what could cause it to be significant.
Significant lack of fit means that the variation of the replicates about their mean values is less than the variation of the design points about their predicted values. Either the runs replicate well and their variance is small, the model doesn't predict well, or some combination of the two.
When there is significant lack of fit, one thing to check is how the replicates were run - were they independent experiments run from scratch and not just replicated measurements on a single experiment? If they were repeated measurements, it is likely that the error has been underestimated (the LOF numerator is artificially small). In this case, the lack of fit statistic is no longer a valid test and decisions about using the model will have to be made based on other statistical criteria.
If the replicates have been run correctly, then perhaps the model is not fitting all the design points well. Consider transformations (check the Box Cox diagnostic plot.) Check for outliers. It may be that a higher-order model would fit the data better. In that case, the design probably needs to be augmented to add more runs to estimate the additional terms. This may be the case when, for instance, the Fit Summary screen shows that the linear and quadratic models have significant lack of fit, but the aliased cubic model has insignificant lack of fit.
If nothing can be done to improve the fit of the model, it may be necessary to use the model as is and then rely on confirmation runs to validate the experimental results. In this case, be alert to the possibility that the model may not be a very good predictor of the process under certain conditions.

细履平沙

以上对失拟的计算，失拟显著的应对措施都进行了介绍。

细履平沙

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lilin439

我晕
这么高的威望让我们新手怎么看啊