Thank you for sharing this explanation! I also deeply appreciate the captions to help us understand what is being said. I’m in a MBA program and stats aren’t my strongest skill so thanks again.
Thank you for your video. However, there is a statement you made which mentioned the highest average response is 8.3 but I saw in one corner there is also a value of 9.1. Why is that?
Hello, is it possible to use response optimizer without considering any interaction effects? I ask you this since when I studied response surface, the only way to optimize was considering curvature (interactions, quadratic effects). I was wondering if even in the factorial response optimizer, interactions are mandatory for optimization.
@@instituteofqualityandrelia7902 thank you. Actually my question was about optimization. Can I still optimize without considering interactions (if they are not significant)? I ask you this because in response surface the prerogative for optimization was to have interaction and/or quadratic effects. I wonder why this would be different in pure factorial doe.
Hello friend, you may have defined the factors as text. In case these are numerical, you can create the design with numerical factors and analyse. If your factors are discrete, then contour surface plots are not possible.
Yes. True. But with the regression equation that includes interaction, you may find the optimum settings somewhere else. This in only illustration, and what you say may not be true in other cases. Thanks for your keen interest.
Dear sir, What is the red reference line in pareto effect diagram? How its calculated by minitab? Also, is it possible to optimize design by using the results of finite element analysis to minimise the stress or displacement in the structure we analysed?
The red line represents limit of randomness at the specified confidence level. It is calculated using t-distribution and error degrees of freedom. For example, at 8:43, the value above the line is shown as 2.14. The error degrees of freedom are 14. So this 2.14 is critical value of two-tailed t-distribution with 0.05 alpha risk and 14 degrees of freedom. Use function =T.INV.2T(0.05,14) to get this value on XL. Try in some other cases. Watch our video on t-test. Link to this video: th-cam.com/video/euzzQU3n0UU/w-d-xo.html.
Hello Eric, The threshold p-value is decided by the confidence level as you have mentioned. You can change confidence level in Minitab in Stat>DOE>Analyse Factorial Design and Options (Ref Minitab 17 version). It may vary for other versions. And yes, the R-sq (Adj) 86% is an error. Apologise for that. Will trty to correct it if possible in the video. I have added the correction in the description. TH-cam does not allow editing the video. Thanks for your keen interest.
if p-value of block is significant, does that mean there is unacceptable lot to lot variation? if yes, what action can be usually taken to resolve the issue?
It depends on what the blocking factor is. If p-value shows that it is significant, then we need to consider the block in the model and prediction. There is no question of accepting or not accepting!
Thanks Ravish. For blocking please watch our other videos. On this case, three blocks are for three batches. Three replicates also provide better power and lower beta risk.
Please Sir. Please...I'm in dire need of assistance. I have data that needs to be analysed using full factorial method but I don't know how to go about it. Please can you help me? I'm frustrated Please
Hello Ayanam! Watch my foundation videos on the subject of DOE. Here are links! 1.th-cam.com/video/pTAUa6qXV6E/w-d-xo.html 2. th-cam.com/video/cIXYKynq1-o/w-d-xo.html 3.th-cam.com/video/h6EaP3o4-sU/w-d-xo.html
A block is a discrete factor that is not under control of the experimenter. Examples are different batches, diffferent machines. It is not of primary interest to the experimenter but can result in additional variation. Please see our previous videos on DOE to understand the videos better. This is the seventh video on DOE.
Hello! Thank you for your question. I should have explained that in the first part of the video. The three blocks are for the three batches of material. Apologise for delay in my respones. Link to part 1 of the video: th-cam.com/video/d1lUNrOKc2g/w-d-xo.htmlsi=k67m9ztysCFZkCUe
Hello Shehzadkhan, Thanks for your question! This is most likely because of insufficient degrees of freedom (DF) for the error term. Calculation of p-values in ANOVA requires at least one DF for error. You will be seeing * instead of p-values. Am I correct?
@@shehzadkhanlucky7477 You can either omit terms that have very little contribution. You can identify these by looking at Sum of Squares (SeqSS) values. If this is not possible, then you can replicate experiment but this is additional time and budget.
Thanks for your ineterst! Please watch our video on Fractional Factorial Designs. Here is the link: th-cam.com/video/MQhf1KFhYCw/w-d-xo.html. The Resposne Surface Designs are used when response is nonlinear and requires second or higher order models.
Can anyone help?.in analysing factorial design my residuals are always coming 0 and the normality plot have all points at 0.please tell the mistake i made
Hi Alka! This happens in saturated designs. Ths measn that the number of treatments in the experiment equals number of effects that you are analysing, and there is no degree of freedom left for Mean-square error in ANOVA and thus F-ratio cannot be calculated. You can see my other videos on Degrees of freedom, fractional factoril design and other videos on DOE. Good luck!
![เปิดบ้าน โคตรสวย ป๋องกพล รู้จักตั้งนาน เพิ่งรู้ว่ารวย!!! l [Nickynachat]](http://i.ytimg.com/vi/tmgCF_2HfYI/mqdefault.jpg)
Thank you for sharing this explanation! I also deeply appreciate the captions to help us understand what is being said. I’m in a MBA program and stats aren’t my strongest skill so thanks again.
Welcome! I am glad to know!
Just watched the whole DOE series. One of the few videos that actually looks under the hood of what minitab offers. great job
Thank you! Glad to know you watched the full DOE series! Do you work for Minitab? Regards..Hemant
@@instituteofqualityandrelia7902 No, I am a Mech. Engineer that needed a refresher.
Thank you for your tutorial! I watch the whole series from the very first one and I understand DoE a lot better!
Happy to hear that!
Thank You for sharing. These are very simple videos and good explanations to understand.
Thank you Pravin!
I passed my course thanks to you. Thank you very much!
I am glad it helped! Congratulations!
Great explanation; the best i have seen in the web. Thank you Sir!!
Wow, thanks a lot for your feedback and appreciation! I get motivated to do more with such feedback!
Thank you so much for your very clear explanation.
Welcome! I am glad you liked it!
Very well explained sir. Great job....
Thank you!😊
Thank you for your video. However, there is a statement you made which mentioned the highest average response is 8.3 but I saw in one corner there is also a value of 9.1. Why is that?
Thank you for your comment. You are correct. It should be 9.1 instead. Apologise for my oversight!
This helped me pass my course. Many thanks sir!
I am glad it helped! Congratulations!
Thank you sir, the explanation is easy and understandable
Appreciate your feedback! Thank you.
Excellent tutorial Sir, Tank you!
Thanks. Appreciate your feedback.
Thank you so much, Sir. Really helpful, congratulations!
Glad it was helpful!
Hello, is it possible to use response optimizer without considering any interaction effects? I ask you this since when I studied response surface, the only way to optimize was considering curvature (interactions, quadratic effects). I was wondering if even in the factorial response optimizer, interactions are mandatory for optimization.
I would say consider interactions if these are statistically significant. Else, you can omit it from the model.
@@instituteofqualityandrelia7902 thank you. Actually my question was about optimization. Can I still optimize without considering interactions (if they are not significant)?
I ask you this because in response surface the prerogative for optimization was to have interaction and/or quadratic effects. I wonder why this would be different in pure factorial doe.
@@luigi88953 yes. If you omit from the model, these will not be used for optimization.
@@instituteofqualityandrelia7902 thank you!
hello, thanks for the explanation, but in my factorial design i don't have to option to do contour or surface plots, can i use graph option instead?
Hello friend, you may have defined the factors as text. In case these are numerical, you can create the design with numerical factors and analyse. If your factors are discrete, then contour surface plots are not possible.
It's worth watching sir
Thank you!
At 1:29 the highest average is 9.13 is in the corner with high case depth and radius and low roughness
Yes. True. But with the regression equation that includes interaction, you may find the optimum settings somewhere else. This in only illustration, and what you say may not be true in other cases. Thanks for your keen interest.
Dear sir,
What is the red reference line in pareto effect diagram? How its calculated by minitab?
Also, is it possible to optimize design by using the results of finite element analysis to minimise the stress or displacement in the structure we analysed?
The red line represents limit of randomness at the specified confidence level. It is calculated using t-distribution and error degrees of freedom. For example, at 8:43, the value above the line is shown as 2.14. The error degrees of freedom are 14. So this 2.14 is critical value of two-tailed t-distribution with 0.05 alpha risk and 14 degrees of freedom. Use function =T.INV.2T(0.05,14) to get this value on XL. Try in some other cases. Watch our video on t-test. Link to this video: th-cam.com/video/euzzQU3n0UU/w-d-xo.html.
Thank you. it was very clear
Good to know!
hello sir ! Hemant sir as great as always :)
So nice of you
Thanks Ankit!
Why is the threshold p value for significant factor is p
Hello Eric, The threshold p-value is decided by the confidence level as you have mentioned. You can change confidence level in Minitab in Stat>DOE>Analyse Factorial Design and Options (Ref Minitab 17 version). It may vary for other versions. And yes, the R-sq (Adj) 86% is an error. Apologise for that. Will trty to correct it if possible in the video. I have added the correction in the description. TH-cam does not allow editing the video. Thanks for your keen interest.
if p-value of block is significant, does that mean there is unacceptable lot to lot variation? if yes, what action can be usually taken to resolve the issue?
It depends on what the blocking factor is. If p-value shows that it is significant, then we need to consider the block in the model and prediction. There is no question of accepting or not accepting!
Please explain block and replicate in detail sir...why no. of run increased from 8 to 24 after adding 3 block and 3 replicate
Thanks Ravish. For blocking please watch our other videos. On this case, three blocks are for three batches. Three replicates also provide better power and lower beta risk.
SIr, when we need to use factorial design and RSM response surface mode design in minitab , both methods are answering for data with results.
I have not understood your comment. Kindly clarify.
Thank you so much
Welcome! Appreciate your feedback!
Please Sir. Please...I'm in dire need of assistance. I have data that needs to be analysed using full factorial method but I don't know how to go about it. Please can you help me? I'm frustrated Please
Hello Ayanam! Watch my foundation videos on the subject of DOE. Here are links!
1.th-cam.com/video/pTAUa6qXV6E/w-d-xo.html
2. th-cam.com/video/cIXYKynq1-o/w-d-xo.html
3.th-cam.com/video/h6EaP3o4-sU/w-d-xo.html
this is so good thanks for it. but i dont understant block? what is it? is it same value with our factor?
A block is a discrete factor that is not under control of the experimenter. Examples are different batches, diffferent machines. It is not of primary interest to the experimenter but can result in additional variation. Please see our previous videos on DOE to understand the videos better. This is the seventh video on DOE.
Why three blocks are chosen? What effect will we observe if we choose only one block?
Hello! Thank you for your question. I should have explained that in the first part of the video. The three blocks are for the three batches of material. Apologise for delay in my respones. Link to part 1 of the video:
th-cam.com/video/d1lUNrOKc2g/w-d-xo.htmlsi=k67m9ztysCFZkCUe
Hi.. Could you please help me? The contour plot option is unactive and I can't plot it
Hi Natalia! It is possible that you defined the factors as text. Please check. You must define the factors as numeric to get contour plots.
Hello sir ,
I follow your video for the analysis of DOE on minitab 18 and it doesn't give p-values and t-values. What can be the possible reason?
Hello Shehzadkhan, Thanks for your question! This is most likely because of insufficient degrees of freedom (DF) for the error term. Calculation of p-values in ANOVA requires at least one DF for error. You will be seeing * instead of p-values. Am I correct?
@@instituteofqualityandrelia7902 Absolutely right sir * sign appears
@@instituteofqualityandrelia7902 Sir what am i supposed to do now?
@@shehzadkhanlucky7477 You can either omit terms that have very little contribution. You can identify these by looking at Sum of Squares (SeqSS) values. If this is not possible, then you can replicate experiment but this is additional time and budget.
@@instituteofqualityandrelia7902 Ok, thank you sir.
sir what is the difference between full factorial design, half factorial design and Response surface methodology
Thanks for your ineterst! Please watch our video on Fractional Factorial Designs. Here is the link: th-cam.com/video/MQhf1KFhYCw/w-d-xo.html. The Resposne Surface Designs are used when response is nonlinear and requires second or higher order models.
Can anyone help?.in analysing factorial design my residuals are always coming 0 and the normality plot have all points at 0.please tell the mistake i made
Hi Alka! This happens in saturated designs. Ths measn that the number of treatments in the experiment equals number of effects that you are analysing, and there is no degree of freedom left for Mean-square error in ANOVA and thus F-ratio cannot be calculated. You can see my other videos on Degrees of freedom, fractional factoril design and other videos on DOE. Good luck!