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  @user-re5hv5dg8e29 күн бұрын

  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.

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  @StatisticsMadeEasybyStatEase26 күн бұрын

  Hi! It looks like there's some confusion here. What the software is talking about is the *difference* between the Predicted and Adjusted R^2 values: you want them to agree with each other, so the difference of the values is less than 0.2. In other words, the Predicted R^2 in this example should be closer to the Adjusted R^2 value of 0.7063. This is not the same as looking at a single R^2 value, where you do want the value to simply be as high as possible.

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  @gomaakhalaf9667Ай бұрын

  Can I have the PPT for this lesson ?

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  @StatisticsMadeEasybyStatEaseАй бұрын

  Sure! Here's the link: statease.com/documents/379/Planning_DOEs_for_Basic_Research.pdf

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  @gomaakhalaf9667Ай бұрын

  @@StatisticsMadeEasybyStatEase I am too much interested in your valuable lectures, Thank you

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  @gomaakhalaf9667Ай бұрын

  @@StatisticsMadeEasybyStatEase I am too much interested in your valuable lectures, Thank you

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  @ahmedabouelezz807Ай бұрын

  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.

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  @user-re5hv5dg8eАй бұрын

  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.

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  @StatisticsMadeEasybyStatEaseАй бұрын

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

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  @hnaaalahmad2 ай бұрын

  Thanks very much

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  @sreedeviasokan60802 ай бұрын

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

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  @StatisticsMadeEasybyStatEase2 ай бұрын

  Yes, by seeing if the surface bisects the points and fits them within normal variation (does not exhibit a significant lack of fit). To see what I mean, open the tutorial data Chemical Conversion (Analyzed), go to the Model Graphs, 3D Surface and click through the Jump to run points. This is an example of RSM done right. : ) You can access the Stat-Ease tutorials at www.statease.com/docs/latest/tutorials

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  @oosconzult.86393 ай бұрын

  God Bless you, sir

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  @abiolaadebanjo47593 ай бұрын

  Such an insightful presentation

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  @emmanuelodunlami66913 ай бұрын

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

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  @StatisticsMadeEasybyStatEase3 ай бұрын

  You can request a trial from www.statease.com/trial, and buy it if you think it will suit your needs.

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  @bidec63 ай бұрын

  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

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  @StatisticsMadeEasybyStatEase3 ай бұрын

  Absolutely. Check out the Crash Course for Mixture Designs video: kzread.info/dash/bejne/q4t4pqWegZOwhqQ.html

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  @bidec63 ай бұрын

  @@StatisticsMadeEasybyStatEase I will definitely check it out.

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  @Kevin.S.McCarter_PhD3 ай бұрын

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

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  @StatisticsMadeEasybyStatEase3 ай бұрын

  Thank you very much!

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  @kashifanwar15893 ай бұрын

  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

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  @StatisticsMadeEasybyStatEase3 ай бұрын

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

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  @jecohuerta4 ай бұрын

  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?

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  @StatisticsMadeEasybyStatEase4 ай бұрын

  Going any deeper into this is beyond the scope of an introductory webinar, but you're welcome to explore the other videos here on KZread or check out our eLearning on the Stat-Ease Academy: www.statease.com/training/academy

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  @albertoramos45244 ай бұрын

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

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  @kpallwell5 ай бұрын

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  @alamutukehinde5 ай бұрын

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

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  @StatisticsMadeEasybyStatEase5 ай бұрын

  We don't offer those files publicly, but you're welcome to download the slides to make it easier to do the data entry yourself.

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  @gunawansolo65136 ай бұрын

  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.

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  @StatisticsMadeEasybyStatEase5 ай бұрын

  Sorry it took so long to see this! Please send this inquiry to our StatHelp group: www.statease.com/about-us/contact/contact-support/

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  @user-gs8xp5iq4l6 ай бұрын

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

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  @StatisticsMadeEasybyStatEase6 ай бұрын

  Just enter the raw data for that specific experiment.

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  @aswinharapan27606 ай бұрын

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

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  @StatisticsMadeEasybyStatEase6 ай бұрын

  We used some previously saved data for this tutorial. When you design your own experiments, you'll need to run the experiment and enter your observed responses.

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  @sergiofitch43786 ай бұрын

  Absolutely great content. Thank you for uploading this!

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  @vivekmahadevan98387 ай бұрын

  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?

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  @StatisticsMadeEasybyStatEase7 ай бұрын

  If you do not want to add more runs, then finding ways to decrease the noise in the system, or increasing the acceptable "d" will both change the signal to noise ratio. A larger S/N ratio will increase the FDS calculation.

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  @diyahprobowulan80387 ай бұрын

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

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  @StatisticsMadeEasybyStatEase7 ай бұрын

  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.

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  @ahmedabdulrazzaq11597 ай бұрын

  Thank you

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  @iMistroom8 ай бұрын

  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.

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  @StatisticsMadeEasybyStatEase8 ай бұрын

  This imposes an extra equality constraint. The level of A is dependent on the level of B. Now there are only 2 components that vary, but all 3 are involved in the total 100%. Short answer - there is not an easy way to get this done.

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  @gggm19219 ай бұрын

  Do you have version to have Weibull Regression and DOE?

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  @StatisticsMadeEasybyStatEase9 ай бұрын

  There will be a Python interface that allows Weibull regression in the 2023 release of Stat-Ease 360.

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  @Pedritox09539 ай бұрын

  Great video!

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  @mobinvakili9 ай бұрын

  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?

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  @StatisticsMadeEasybyStatEase9 ай бұрын

  In theory yes, you can import the old data in the software and use Design Augmentation to add more runs to fit a higher order model. In practice, there are many questions starting with - is the process still running the same as it did previously? Are you measuring the same way? Was the previous data from a designed experiment or was it historical data from the system? This is really a practical experimentation question. You can try and it may be successful, or it may not work well and you would get better information from a new experiment.

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  @DM79810 ай бұрын

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

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  @StatisticsMadeEasybyStatEase10 ай бұрын

  Great question - this is a power/sample size calculation, based on the change in the response that you want to detect and the current standard deviation of the response. If you have a current license of Design-Expert or Stat-Ease 360, you are welcome to email more details to [email protected]. If you don't want to do the calculations, then often using a sample size of 4-5 per run, and then entering the average of those runs as the response, will give sufficient information.

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  @paolo720611 ай бұрын

  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)?

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  @StatisticsMadeEasybyStatEase11 ай бұрын

  For strength reduction the BD interaction was highly significant. As mentioned, there were a number of other responses. B may have been involved in other interactions in those cases. Subject matter knowledge would always drive these decisions.

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  @paolo720611 ай бұрын

  Thanks for sharing

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  @tafadzwankhoma1533 Жыл бұрын

  Thank you for the great video. What is the significance, if any, of the standard mean error in design evaluation?

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  @StatisticsMadeEasybyStatEase Жыл бұрын

  I'm not sure exactly which statistic you are referring to, but any standard error is an estimate of variation for that statistic.

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  @tafadzwankhoma1533 Жыл бұрын

  @@StatisticsMadeEasybyStatEase I meant the one below FDS value at 51:34

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  @StatisticsMadeEasybyStatEase Жыл бұрын

  @@tafadzwankhoma1533 That is the calculated standard error after the experimenter inputs their "d" and "s". Continuing forward to 52:21, Martin is demonstrating changing these values, thus changing the signal/noise ratio. The blue horizontal line is the calculated value (the standard error). The values should be your desired precision, relative to the normal process variation. Refer to this video for more discussion: kzread.info/dash/bejne/p41rsdOGacTdqKg.html

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  @tafadzwankhoma1533 Жыл бұрын

  the triangle at 11:34, is it generated in DX or you plotted it in another App/Software separately.

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  @StatisticsMadeEasybyStatEase Жыл бұрын

  The graphic originated in DX and was modified for the presentation.

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  @joseantonioochoafaure2599 Жыл бұрын

  👏

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  @tafadzwankhoma1533 Жыл бұрын

  Thank you for the recording. What is the relationship between Design Expert and StatEase 360. Can one use Stat-Ease 360 to completely design and analyse experimental data without Design Expert or StatEase is a build up on Design Expert

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  @StatisticsMadeEasybyStatEase Жыл бұрын

  Stat-Ease 360 includes Design-Expert software, and also has higher-level features such as linkage to Python and additional regression options.

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  @ahmedabdulrazzaq1159 Жыл бұрын

  Hello sir... Thank you for your great video.

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  @johnngala3248 Жыл бұрын

  Thank you very much. Am John NGALA (MSc student) of Gulu University of Uganda.

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  @johnngala3248 Жыл бұрын

  STATEASE is the BEST. Everything made ease. From Kenya studying in Uganda.

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  @irwin-hirsh Жыл бұрын

  thanks for this very topical for me at least! Thanks Joann for sharing.