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Jul 30, 2019 10:27 AM
(2126 views)

I calculated predicted x from linear fit using fit model > inverse prediction. Is it possible to do the same using the quadratic model.

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I believe you are using the Bivariate platform. Use the Fit Least Squares platform instead. The Inverse Prediction command in this platform still does not work with quadratic models but there is another option. Follow these steps:

- Select
**Analyze**>**Fit Model**. - Select
**response**column and click**Y**. - Select
**factor**column. - Click
**Macros**and select**Polynomial to Degree**. - Change Emphasis to
**Minimal Report**. - Click
**Run**. - Click the red triangle at the top and select
**Factor Profiling**>**Profiler**. - Click the red triangle next to
**Prediction Profiler**and select**Optimization and Desirability**>**Desirability Functions**. - Control-click on the Desirability plot at the right end of the top row of plots.
- Change the goal to
**Match Target**. Do NOT change the desirability values otherwise. - Enter the
**target**response value as the**Middle**value. - Enter
**Low**and**High**values that surround the target. These values are not critical. - Click
**OK**. - Click the red triangle next to
**Prediction Profiler**and select**Optimization and Desirability**>**Maximize Desirability**.

The inverse prediction appears as the red number below the profiler for the factor.

Learn it once, use it forever!

5 REPLIES 5

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Re: inverse prediction using quadratic model

Generally, no.

What is the inverse prediction for Y = 10?

Learn it once, use it forever!

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@markbailey I am trying to calculate 'x' from 'y' using quadratic standard curves so I can compare with 'x' calculated using same standard data fir to a linear model using jmp. I know I can do this graphically by using crosshairs tool with overlaid linear and quadratic functions. Is there any other way to calculate 'x' from the quadratic response function using JMP to show the difference between this and corresponding values calculated using the linear model?

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Re: inverse prediction using quadratic model

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I believe you are using the Bivariate platform. Use the Fit Least Squares platform instead. The Inverse Prediction command in this platform still does not work with quadratic models but there is another option. Follow these steps:

- Select
**Analyze**>**Fit Model**. - Select
**response**column and click**Y**. - Select
**factor**column. - Click
**Macros**and select**Polynomial to Degree**. - Change Emphasis to
**Minimal Report**. - Click
**Run**. - Click the red triangle at the top and select
**Factor Profiling**>**Profiler**. - Click the red triangle next to
**Prediction Profiler**and select**Optimization and Desirability**>**Desirability Functions**. - Control-click on the Desirability plot at the right end of the top row of plots.
- Change the goal to
**Match Target**. Do NOT change the desirability values otherwise. - Enter the
**target**response value as the**Middle**value. - Enter
**Low**and**High**values that surround the target. These values are not critical. - Click
**OK**. - Click the red triangle next to
**Prediction Profiler**and select**Optimization and Desirability**>**Maximize Desirability**.

The inverse prediction appears as the red number below the profiler for the factor.

Learn it once, use it forever!

Highlighted
##

@markbailey Thank you for your response. It worked and I got results for the inverse prediction for linear model but I'll not be able to get inverse prediction for quadratic model in JMP. Correct?

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Re: inverse prediction using quadratic model

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Re: inverse prediction using quadratic model

Is what correct?

I followed the steps above using the Big Class data table. The weight is in the Y role and I added height and height^2 terms to the model. I defined the desirability function to match a target of Y = 120. Here is the result:

The inverse prediction is 66.3 inches.

Learn it once, use it forever!

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