Keys to Analyzing a Response Surface Design

Optimize your products and processes with accurate prediction models. In this webinar, learn how to get the most out of your response surface method (RSM) design by following a few key analysis steps. See how automated model-reduction tools build simpler models that predict more precisely. Then discover how diagnostics confirm your model’s validity. Finally, learn how key statistics like lack of fit and various R-squared measures characterize the polynomial model. All these tools are used together to guide researchers towards their goal of process optimization.
Download the slides: cdn.statease.c...
Keys to building the design: • Keys to Building the P...
Doing formulations? Use mixtures instead: • A Crash Course in Mixt...
See RSM in action: • RSM Saves the Circuit ...
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[@ahmedabdulrazzaq1159]

Thank you

[@saraadnan-o9q]

when entering the responses, should i enter them as average with +- SD or as pure data? & thank you

[@StatisticsMadeEasybyStatEase]

Just enter the raw data for that specific experiment.

[@kashifanwar1589]

Hi, I am working on an experiment using Response Surface Methodology (Design of Experiment). I use StatEase software for the same. During analysis the software shows that CUBIC model for my data is Aliased. However, the cubic model is significant (p 0.05). Moreover R2 value is also very good 0.985.
Can I use this model for prediction of optimized conditions, although the model is aliased, but statistically significant?
Please give me a quick response, Thanks

[@StatisticsMadeEasybyStatEase]

Hi! This is a great question for our experts. Please send it to them here: statease.com/about-us/contact/contact-support/

[@diyahprobowulan8038]

Can 3D graphs be created for categorial data? Thank you

[@StatisticsMadeEasybyStatEase]

The 3D graph for categorical factors is a set of bar graphs, with the height of each bar representing the predicted response value for that combination.

[@rafidahmohdariff]

hai, if the means of my experimental values are all already in the PI interval, should I do the two sample t-test also to see either there is significant diff. between the experimental and predicted values? Thank you.

[@StatisticsMadeEasybyStatEase]

No, the prediction interval is the correct interval to use; that the observed mean falls within the prediction interval is the test.