Minitab Statistical Software: Design of Experiment

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  • เผยแพร่เมื่อ 23 ธ.ค. 2024

ความคิดเห็น • 30

  • @salahlakhdar6714
    @salahlakhdar6714 ปีที่แล้ว +4

    First, i would like to thank you for making this great presentation on Minitab, especially DOE. It's helped me a lot.

    • @MinitabMalaysiaSingapore
      @MinitabMalaysiaSingapore  ปีที่แล้ว

      Thank you Salah! We hope you gained more knowledge and understanding towards Minitab

  • @manasmohapatra4228
    @manasmohapatra4228 3 หลายเดือนก่อน +1

    Just loved this one and your efforts

  • @adibcs97
    @adibcs97 ปีที่แล้ว

    I have run the experiment and have my own factorial design.. can i just analyze the data using my own factorial design.. How should I do?

    • @MinitabMalaysiaSingapore
      @MinitabMalaysiaSingapore  ปีที่แล้ว +1

      Yes, you can. But you must define your design by using Stat > DOE > Factorial > Define Custom Factorial Design before analyzing.
      Select the design that you want to define;
      Define Custom 2-Level Factorial Design: Use to define a 2-level factorial design from data in your worksheet.
      support.minitab.com/en-us/minitab/21/help-and-how-to/statistical-modeling/doe/how-to/factorial/define-custom-factorial-design/define-custom-2-level-factorial/perform-the-analysis/define-design/
      Define Custom 2-Level Split-Plot Design: Use to define a split-plot design from data in your worksheet.
      support.minitab.com/en-us/minitab/21/help-and-how-to/statistical-modeling/doe/how-to/factorial/define-custom-factorial-design/define-custom-2-level-split-plot/perform-the-analysis/define-design/
      Define Custom General Full Factorial Design: Use general full factorial design when the factors have any number of levels and you want to treat all factors as categorical
      support.minitab.com/en-us/minitab/21/help-and-how-to/statistical-modeling/doe/how-to/factorial/define-custom-factorial-design/define-custom-general-full-factorial/perform-the-analysis/define-design/

  • @jawjaemina
    @jawjaemina 7 หลายเดือนก่อน

    Thank you for the helpfull video! But i would like to ask a few things..
    1. Could we processing data twice first before we entering the optimization and verification stage? In my case, i have 19 runs of formulation that i got using simplex centroid design method. From that 19 samples, i want to get 4 best samples from 1 response result. After i get 4 best samples, i test the 4 samples again with different responses. And then the response results of 4 samples is used in optimization and verification stage. Is it possible to do that in one time (not in separated time when i processing data to get 4 best samples, and processing data to have optimization formula)?
    2. Could you elaborate again to me about what is the difference between simplex centroid design and simplex lattice design in a easier way?🙏🏻
    I'm sorry for my english.. i hope you would understand what i mean. Thank you in advance🙏

    • @MinitabMalaysiaSingapore
      @MinitabMalaysiaSingapore  6 หลายเดือนก่อน

      Thank you for your questions.
      Regarding your first question, it falls beyond our technical support scope. However, we offer personalized assistance through our Statistical Consulting service.
      For your second question, you can find more information here: support.minitab.com/en-us/minitab/help-and-how-to/statistical-modeling/doe/supporting-topics/mixture-designs/choose-a-mixture-design/
      If you need further assistance, please feel free to email us at minitab@bizits.com.

  • @fatmaelmaghraby3208
    @fatmaelmaghraby3208 ปีที่แล้ว

    thank you very much, but i want calculate adequate precision from minitab and lack of fit

    • @MinitabMalaysiaSingapore
      @MinitabMalaysiaSingapore  ปีที่แล้ว

      Thanks for your question. To know more about lack-of-fit and lack-of-fit tests in Minitab, you may find more details here:
      support.minitab.com/en-us/minitab/21/help-and-how-to/statistical-modeling/regression/supporting-topics/regression-models/lack-of-fit-and-lack-of-fit-tests/
      Should you need further assistance, feel free to email us at minitab@bizits.com.

  • @prachibhatia8219
    @prachibhatia8219 11 หลายเดือนก่อน

    hello. It was a wonderful presentation which has cleared many of my doubts. I have some query, it would be kind of you if you can help me.
    1) I want to study the effect of various factors on enzyme production. Factors i want to study are around 10. So I should use plackett burman design or full factorial design.??
    2) After which I want to know which factors effect the production and then optimize it. So which design is suitable.
    3) After we select any design we save this document run our experiment and come back n add our values in next block and proceed for the analysis right?
    4) lastly how can we get predicted values from the software and compare it my actual values. Where is this option available.
    Thank you

    • @MinitabMalaysiaSingapore
      @MinitabMalaysiaSingapore  11 หลายเดือนก่อน

      Hi, your questions on choosing an appropriate statistical tool for your project and interpreting the output relative to your process are beyond our technical support scope. We offer personalized support through our Statistical Consulting service which carries fee at U$2K for 4-hour block.
      Having said that, here are some guidances for your reference:
      Phases of design experiment
      support.minitab.com/en-us/minitab/21/help-and-how-to/statistical-modeling/doe/supporting-topics/basics/phases-of-a-designed-experiment/
      Available designs in Minitab Statistical Software
      support.minitab.com/en-us/minitab/21/help-and-how-to/statistical-modeling/doe/supporting-topics/basics/which-standard-designs-can-minitab-create/
      To get the predicted values, you must store the model by Stat > DOE > Factorial Design > Analyze Factorial
      support.minitab.com/en-us/minitab/21/help-and-how-to/statistical-modeling/using-fitted-models/supporting-topics/basics/stored-model-overview/
      Once you store the model, you may proceed with Stat > DOE > Factorial Design > Predict
      support.minitab.com/en-us/minitab/21/help-and-how-to/statistical-modeling/using-fitted-models/how-to/predict/before-you-start/overview/

  • @nileshtank5909
    @nileshtank5909 4 หลายเดือนก่อน

    Thank you for this informative session. It would be more impactful if the medium of communication was clear. It may be because of Persons's physical limitation.

  • @Research_Page
    @Research_Page 11 หลายเดือนก่อน

    Hi Mam. This was a very nice and clear presentation. Can I use factorial design for 3 factors and 3 levels?

    • @MinitabMalaysiaSingapore
      @MinitabMalaysiaSingapore  11 หลายเดือนก่อน

      Hi, for factors with more than 2 levels, we will use General Full Factorial Design
      Here the links how to create the general full factorial: support.minitab.com/en-us/minitab/21/help-and-how-to/statistical-modeling/doe/how-to/factorial/create-factorial-design/create-general-full-factorial/before-you-start/example/

    • @Research_Page
      @Research_Page 11 หลายเดือนก่อน

      Thank you for the response. Will it be good if I use Taguchi instead of RSM?@@MinitabMalaysiaSingapore

    • @MinitabMalaysiaSingapore
      @MinitabMalaysiaSingapore  11 หลายเดือนก่อน

      @@Research_Page This will depend on your response, the number of factors, and the level of your study.
      Response surface design methodology is often used to refine models after you have determined important factors using screening designs or factorial design; especially if you suspect curvature in the response surface. support.minitab.com/en-us/minitab/21/help-and-how-to/statistical-modeling/doe/supporting-topics/response-surface-designs/response-surface-central-composite-and-box-behnken-designs/
      Taguchi design is to create a robust parameter design to identify controllable factors in your process that can minimize response variation and make your product insensitive to changes in noise factor. Taguchi's design includes a control factor and a noise factor. support.minitab.com/en-us/minitab/21/help-and-how-to/statistical-modeling/doe/supporting-topics/taguchi-designs/taguchi-designs/

  • @joeljoel3949
    @joeljoel3949 2 ปีที่แล้ว

    Very informative maam. Easy to understand.

    • @MinitabMalaysiaSingapore
      @MinitabMalaysiaSingapore  2 ปีที่แล้ว

      Thank you for your comment! Do follow us also on social media to learn tips and tricks on performing better quality analysis with Minitab.

  • @sergiom.3860
    @sergiom.3860 4 หลายเดือนก่อน

    Thank you very much for your contributions, it is honestly amazing the stuff out there that is free to access, why do we pay for education?

  • @arfasafaraz7985
    @arfasafaraz7985 9 หลายเดือนก่อน

    Sorry, how to download the software? Any link?

    • @MinitabMalaysiaSingapore
      @MinitabMalaysiaSingapore  9 หลายเดือนก่อน

      Hi, thank you for watching our webinar. You can download the free trial here: www.minitab.com/en-us/products/minitab/free-trial/

  • @jesperlauridsen2683
    @jesperlauridsen2683 2 ปีที่แล้ว

    Thank you very much for this presentation. This is very good.

  • @taameberhanu2535
    @taameberhanu2535 8 หลายเดือนก่อน

    Nice presentation.

  • @arynasyarinie3151
    @arynasyarinie3151 2 ปีที่แล้ว +1

    Very good

  • @foodtechnologist838
    @foodtechnologist838 ปีที่แล้ว

    I wish my teacher also explain like you😢

    • @MinitabMalaysiaSingapore
      @MinitabMalaysiaSingapore  ปีที่แล้ว

      Hi, we hope you gained more knowledge and understanding about Minitab in this webinar :)

  • @user-fj5xj8fj6f
    @user-fj5xj8fj6f ปีที่แล้ว +5

    Appreciate you r video, but you seriously need to work on your English, its really difficult to understand what you are talking about, this issue especially gets more pronounced when you explain on subjects that there is no presentation tool available in the session

    • @MinitabMalaysiaSingapore
      @MinitabMalaysiaSingapore  ปีที่แล้ว

      Hi, sorry for the inconvenience. We understand that our English might not be perfect, but we're continuously working on improving it. Thanks for pointing it out.