DOE using JMP® Software: Construction and Interpretation of a Response Surface

[@PegaAnalytics]

For 2024 I am planning some new video content relating to DOE. What specific topics would be of interest? Please let me know if the comments :)

[@gauravdhumal1287]

RSM CCD design and its analysis to predict the response.

[@gunjanjeswani3178]

Please tell me how to determine and analyse the prediction equation in factorialdesign

[@dhaferalbakre2665]

Does it work with canopy size as response variable and Altitudes gradient as explanatory variable?.. Thanks in advance

[@scribbleyourlife]

You have a wonderful voice. Other than that, helpful video!

[@khadijasossey9062]

How did you found Y values?

[@dianas8001]

how do you simulate the data ? Ive been trying to figure that out for training/demonstration purposes

[@davidburnham3062]

It was a column formula. Use the RandomNormal() function to add noise.

[@kirankumarkr4611]

Can jmp software used for multi response optimization

[@davidburnham3062]

Short answer: yes. Slightly longer answer: use the FIt Model platform to create models for each response; then use the prediction profiler to create a desirability function based on the goals for each response (maximise, minimise, achieve target). Then optimise the desirability function.

[@kirankumarkr4611]

Thank you sir, one more question, is the rsm tool present in both jmp and jmp Pro version

[@davidburnham3062]

@@kirankumarkr4611 Yes, the ability to generate a response surface design, and the ability to represent data with a response surface model. are native features of JMP; you don't need the PRO version for that.

[@kirankumarkr4611]

@@davidburnham3062 thank you

[@andy5558797]

It's very useful video, thanks!

[@kirankumarkr4611]

Does jmp and jmp Pro version have rsm tool?

[@davidburnham3062]

Yes, the ability to generate a response surface design, and the ability to represent data with a response surface model. are native features of JMP; you don't need the PRO version for that.

[@jigmetenzin7596]

I have read some paper on hydrology models which are calibrated using Response Surface method (RSM). After reading those papers, I have some question related to RSM. The model I am working takes input data as follows : Daily Precipitation data, daily temperature data and daily snow cover area. With this model, I have associated parameter/ factor ( 5 factors - Continuous) with the model. The accuracy of the model is determined by Nash-Sutcliffe coefficient (R square) using the computed and measured discharge from the model. I have to keep on changing those factors ( trail and error method ) in order to achieve high R sqaure value. (Dailymeasured discharge validates the accuracy by comparing with value from daily computed discharge generated by the model) Is it possible to find a optimum value for achieving target R square by this DOE technique? Please advice me.

[@Prof.Mayank]

Nice video, helped a lot..Thanx dear

[@muhammadusmanishfaq9880]

How you get simulation data when you make a table for y?

[@davidburnham3062]

Well in the real-world you would conduct the experiment to collect the data, but for demonstration purposes I just used a formula to generate some simulated data.

[@NhungPham-zn9lw]

How did you found Y values?