Back in the days I had to pay a full fee just to attend a DOE classes however, I didn't get to understand a simple thing. The reason is that, they couldn't address the DOE principle as simple as this. Words can't express my sincere gratitude for you at DataLab. Keep it up guys, sharing knowledge is caring for everyone. ❤❤❤❤❤❤❤❤
10.04 16 effects for lubrication and 16 runs for temperature.... this makes it a total of 32 runs, but explained as 24 runs... could you please help to clarify..??
Hi there! incredible content here.. but i do have a question regarding case example at 19:55, i noted that there is a third factor, C, which was not discussed when introducing response analysis to determine if there are any interaction between A and B. How can we then interpret if there is an interaction of C with A and C with B to the response variable?
Hi man ythanks for you comment! Oh, I'm sorry if we have explained this in a misleading way! Of course the response must be measured taking C into account!
I think the point was that 3 different variations will be brought, i.e., first keeping oil constant, 8 runs will be tested at low temperature, 8 runs at high temperature. This way the temperature effect is monitored. Then to evaluate the lubrication effect, temperature was kept at low and only lubricant was changed i.e., from oil to grease.
Nice video! I was wondering: At 9:26, N= 2,4 so you would do 2 +2 runs, but later N = 16 so you do 8 + 8 runs? What is the difference between both instances?
Hello, thank you very much for your feedback! This is because you have to round up, so 2.4 would be 3 and you can't divide 3 by 2, so you need 4 attempts in total! Regards Hannah
Hello, thank you very much for this wonderful video. I have a question, for the equation that is used to estimate the number of runs needed that depends on standard deviation and the effect that is relevant to us). Where do I get the standard deviation? Do I need to make a random number of runs first and then determine the standard deviation then use it in the equation?
Back in the days I had to pay a full fee just to attend a DOE classes however, I didn't get to understand a simple thing. The reason is that, they couldn't address the DOE principle as simple as this.
Words can't express my sincere gratitude for you at DataLab.
Keep it up guys, sharing knowledge is caring for everyone.
❤❤❤❤❤❤❤❤
Hi, many many thanks for your nice feedback!!!! Yes, of course we will continue : ) Again thanks and Regards, Hannah and Mathias
Please is there a way you can put me through sir, I need it for my research but I don't understand it at all.
Yeh I agree. I sat in a DOE masters class for three days - this short video did it in 20 minutes!!
@@timwatson9413 Thanks : )
Highly appreciated, how in easy steps DOE explained.
Many thanks : )
Mam,am from India (Tamilnadu -chennai) super explanation
Many many thanks : )
Incredible video with such amazing clarity! Could you please also make some videos about screening and optimization, please?
Many thanks for your nice feedback! I will put it on my To Do List!
This is so helpful and useful for my research
Thanks you : )
HIghlights:
12:00 Screening
20:00 Example
Man I got lost half way in the video. Try re-watching with not luck.
In German we say: "aller guten Dinge sind drei" : ) Regards Hannah
@@datatab i like that! in english we say "third time's the charm"
@@cvspvr : )
Amazing clarity ❤
10.04 16 effects for lubrication and 16 runs for temperature.... this makes it a total of 32 runs, but explained as 24 runs... could you please help to clarify..??
same question!
Thank you!
You're welcome! Thanks for your feedback!
Hi there! incredible content here.. but i do have a question regarding case example at 19:55, i noted that there is a third factor, C, which was not discussed when introducing response analysis to determine if there are any interaction between A and B. How can we then interpret if there is an interaction of C with A and C with B to the response variable?
Hi man ythanks for you comment! Oh, I'm sorry if we have explained this in a misleading way! Of course the response must be measured taking C into account!
Nice graphic
Many thanks : )
👍
Thanks : )
Mistake at 10:00, a total of 32 runs would be required, not 24 runs.
I think the point was that 3 different variations will be brought, i.e., first keeping oil constant, 8 runs will be tested at low temperature, 8 runs at high temperature. This way the temperature effect is monitored. Then to evaluate the lubrication effect, temperature was kept at low and only lubricant was changed i.e., from oil to grease.
What about lubricant & high temperature?
@@nda9921 that is only for Full Factorial Design case where all possible interactions are evaluated.
Excellent explanation with practical example
Many thanks : )
Nice video! I was wondering: At 9:26, N= 2,4 so you would do 2 +2 runs, but later N = 16 so you do 8 + 8 runs? What is the difference between both instances?
Hello, thank you very much for your feedback! This is because you have to round up, so 2.4 would be 3 and you can't divide 3 by 2, so you need 4 attempts in total! Regards Hannah
@@datatab Ah I understand now! Thank you!
@@lianne199 You are welcome : )
very good job you are great teacher
Thank you! 😃
Oh my God! Miss you are absloutely amazing teacher with perfect explaination
Hello, thank you very much for this wonderful video.
I have a question, for the equation that is used to estimate the number of runs needed that depends on standard deviation and the effect that is relevant to us).
Where do I get the standard deviation? Do I need to make a random number of runs first and then determine the standard deviation then use it in the equation?