Hi all JMPers,
Does anyone know what saturated model is used in the LACK OF FIT test? More specifically, when running Fit Model, say by Standard Least Squares, I get the table of Lack Of Fit as following:
My understanding is that:
1) p-value gives me that the model can be improved by adding interaction terms,
2) Max RSq is obtained by the saturated model.
Is my understanding correct? If so, is there a way I can get the saturated model in JMP?
Thank you all!
Accepted Solutions
Yes, your understating is correct.
If so, is there a way I can get the saturated model in JMP?
Yes, you can run such model with some data prep.
(Caution-a saturated model is over-parameterized to the point that it is essentially interpolating the data. It is not a sound modeling practice.)
(1) The screenshot shows a linear regression fit to predict HEIGHT with SEX and WEIGHT as predictors. Although this main effect model doesn't appears to be under-fit, Max R Sq indicates a saturated model would achieve R Sq at 0.8872.
(2) To do this Combine predictor variables to form a grouping variable, SEX_WEIGHT, so that I can assigns a parameter to each unique combination of the predictors. Use the Combine Columns to get it
(3) Refit the model using SEX_WEIGHT as the predictor . As shown, R Square is indeed 0.8872. There are 32 parameter estimates (plus intercept) in the model.
Yes, your understating is correct.
If so, is there a way I can get the saturated model in JMP?
Yes, you can run such model with some data prep.
(Caution-a saturated model is over-parameterized to the point that it is essentially interpolating the data. It is not a sound modeling practice.)
(1) The screenshot shows a linear regression fit to predict HEIGHT with SEX and WEIGHT as predictors. Although this main effect model doesn't appears to be under-fit, Max R Sq indicates a saturated model would achieve R Sq at 0.8872.
(2) To do this Combine predictor variables to form a grouping variable, SEX_WEIGHT, so that I can assigns a parameter to each unique combination of the predictors. Use the Combine Columns to get it
(3) Refit the model using SEX_WEIGHT as the predictor . As shown, R Square is indeed 0.8872. There are 32 parameter estimates (plus intercept) in the model.