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Great seminars very illuminating.

Danke für Video! Im Beispiel wurden ja jetzt nur die kontrollierbaren Faktoren eingegeben. Ganz am Anfang wurden ja noch die unkontrollierbaren Faktoren genannt. Wie können bei der Versuchsplanung bzw. Bei der Auswertung die unkontrollierbaren Faktoren berücksichtigt werden?

I somehow missed the part where the result or goal of highest flare was set?

Thank you for a great tutorial.

For optimal response surface design (one-factor), if my independent factor has several levels with repeated measures (assuming outliers are removed), is it correct to input all these individual result points, or do I take the average result of each level and input them?

Hello, I have a question regarding mixture designs. Lets say I have 7 different manufacturers of flour and I want to make a mixture using a maximum of two flours for simplicity reasons, and I want to evaluate every possible combination of the 7 flours. How would one go about this problem? I tried doing a mixture design with 7 ingredients, but I dont know if I can add a constraint that only 2 of the ingredients can be used at a time. I also thought about doing this with the different manufacturers as categoric factors, but since all 7 flours can be used either as ingredient one or two, the resulting design generates nonsensical experiments, where same flours are used as ingredient one and two. The designed experiments also contain replicates which are not specified by the design as replicates, but if I look closely at the data, they are replicates.

Very good explanation

Will be nice to have the transfer equation for variation (POE) ? There are plans to have on the future versions ? Because if we have this equation we can predicted the variation in several regions of the process and predict quality levels

Does the software works in arm based laptops, because I’m going to buy a Surface laptop 7 with arm chip.

Thanks, very helpful!

Very good

Can you share the ppt ?

hai.. The **Predicted R²** of 0.3685 is not as close to the **Adjusted R²** of 0.7063 as one might normally expect; i.e. the difference is more than 0.2. This may indicate a large block effect or a possible problem with your model and/or data. Things to consider are model reduction, response transformation, outliers, etc. All empirical models should be tested by doing confirmation runs. The example above was from the software. the software stated if the difference is greater than 0.2, there is problem with data or etc. However, as u said, greater than 0.2 is better. I also found in one journal stated that greater is better. Can you explain regarding this matter? Thank you so much.

Thank you a lot for your illustration, really, outliers are a big problem! But also, I think there are problems that can come from multicollinearity and need to be solved.

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.

Thanks very much

Is there any way to know if rsm is done right by looking at 3D graph

God Bless you, sir

Such an insightful presentation

How do I download design expert? I have sought for it for months, can’t find how to download it! 🥺

Based on your experience do you think mixture designs can be effective in the development of concrete mixes. Just a random question? 58:00:01

Nice presentation on optimal designs. I especially like your concluding statement, "Always choose a design that fits the problem."

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) and has insignificant lack of fit (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

We did not see the presets in the simulation part of the video since pre-simulated files were used. Would you care to add that in your next video?

O problema todo é que o dual do dual é o primal.

Could you kindly add the sample files you use for the examples to the description in the video for easy access

What does it mean when a coefficient of a component in optimal design model equation is negative while the response only takes positive value? My understanding is that the coefficient is the response value of the component when it is pure (100%), so it can't be negative.

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

Please, can you explain how we can get the value of Response 1

Absolutely great content. Thank you for uploading this!

This is an excellent lecture on a very important concept in DOE. I have 2 questions. 1. Adding additional model points can increase the desired FDS. What if there are some practical constraints in not adding additional model points like time, or budget? What trade-offs we need to make to ensure we get the same precision? 2. What if I don't have an historical data of the std deviation? Let's say the process is a new one. Which std dev should I use? Should a separate ANOVA study be conducted and then use this std dev?

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

Thank you

I'm going to have mixture component of A, B and C. A+B+C = 100% and the ratio of A/B must maintain at 0.5. How do I set the constrain since I can't enter both lower and upper limit with the same value.

Do you have version to have Weibull Regression and DOE?

Great video!

Hi there, Can I use old data from somebody else and increase responses, and the design expert redesign another experiment for me so i do less experiment?

Thnak you for this amazing video! How can I choose the number of samples I have to run for each trial?

28:30 I would suggest using 'Strength' as the metric, not Strength Reduction. Because the end customer is interested in strength. 34:10 Seems that component B is not adding value to the mixture from the response Strength perspective 37:04 Seems that component B is also not adding value with respect to the response Zeta Potential 57:00 Did you decide to leave the AB term in the model (despite p value > 0,10) because it felt not comfortable for you to tell the organization that component B can be removed (and that the company has been using it for no good reason)?

Thanks for sharing