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Jun 23, 2016 7:04 AM
(5278 views)

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!

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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.

3 REPLIES

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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.

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Re: Lack of fit test

Thanks a million!

This is a great answer, although I didn't find the **Combine Column, **I am using JMP Pro 11.0, but I got the idea.

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Re: Lack of fit test

Yes,** Combine Columns** along with many other column utilities were** **added in JMP 12**. **