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
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.
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.
Thank you
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
Hi! This is a great question for our experts. Please send it to them here: statease.com/about-us/contact/contact-support/
when entering the responses, should i enter them as average with +- SD or as pure data? & thank you
Just enter the raw data for that specific experiment.
Can 3D graphs be created for categorial data? Thank you
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.
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.
No, the prediction interval is the correct interval to use; that the observed mean falls within the prediction interval is the test.