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Hi, this example uses the Placket-Burman Design. Further information support.minitab.com/en-us/minitab/media/pdfs/translate/Assistant_DOE.pdf

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.

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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/

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/

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

@@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/

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/

Hi, since this webinar focuses on a simple forecasting model, we do not run the ADF to show stationary. It will be good to test it also. Now ADF is available in Minitab 21 support.minitab.com/en-us/minitab/21/help-and-how-to/statistical-modeling/time-series/how-to/augmented-dickey-fuller-test/perform-the-analysis/enter-your-data/?SID=151601

@@MinitabMalaysiaSingapore Thank you very much. Can you please explain the entire process of forecasting. I mean stating form ADF to check the stationary, If It is not Stationary what can we do, If it is Stationary then how can we forecast . Because audience needs to understand the entire process. Please help us.

Hi, Minitab offers several analyses to do forecasting, they include simple forecasting and smoothing methods, correlation analysis methods, and ARIMA modeling. Although correlation analysis can be done separately from ARIMA modeling, Minitab presents the correlation method as part of ARIMA modeling. Simple forecasting and smoothing methods model components (trend, seasonal, cyclic, irregular) in a series that is usually easy to observe in a time series plot of the data. This approach decomposes the data into its parts and then extends the estimates of the components into the future to provide forecasts. They will be trend analysis, decomposition, moving average, single exponential smoothing, double exponential smoothing, and winter’s method. ARIMA modeling also makes use of patterns in the data, but these patterns might not be easily visible in a plot of the data. Then, ARIMA modeling uses differencing and the autocorrelation and partial autocorrelation functions to help identify an acceptable model. You can use the model to generate forecasts. ARIMA analysis requires data to be stationary (constant mean and variance). To decide if data are stationary or not, you can examine them by time series plot (Stat > Time Series > Box-Cox Transformation) and Augmented Dickey-Fuller (ADF) (Stat > Time Series > Augmented Dickey-Fuller Test). Time series plot to determine whether a transformation is required to give constant variance and Augmented Dickey-Fuller (ADF) to determine whether differencing makes the mean of the data stationary. Based on ADF output, if data is non-stationary, it implies that differencing is a reasonable step to try to make the data stationary. To calculate the differences, you can use differences or lag tools (Stat > Time Series > Differences/Lag) and use them to create the plot to examine the stationary Once you have stationary data, identify a model. Use Forecast with Best ARIMA model (Stat > Time Series > Forecast with Best ARIMA Model) to significantly speed up the model identification process by automatically selecting the best model from a candidate set. You can verify the ACF and PACF of residuals indicate a random process, signified when there are no large spikes, and after you are satisfied with the fit, make the forecast. support.minitab.com/en-us/minitab/21/help-and-how-to/statistical-modeling/time-series/supporting-topics/basics/methods-for-analyzing-time-series/ support.minitab.com/en-us/minitab/21/help-and-how-to/statistical-modeling/time-series/supporting-topics/time-series-models/fit-an-arima-model/

thanks@@MinitabMalaysiaSingapore

Please see our reply below, thanks :)

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.

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.

Hi, please send email to minitab@bizits.com for further assistance. Thanks.

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Hi, can I get your email address? or you may drop us an email at minitab@bizits.com

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

Hi Juliano, using Predictive Analytics tools in Minitab helps you to identify the most important predictors (eg manpower, material) that impact your job scheduling. Given settings of significant predictors, the forecast model will also predict response of your interest (eg expected completion days for a project)

Thank you! We hope you gained more knowledge and understanding about Minitab's Predictive Analytics

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Hi Ibragim, We hope you enjoyed the session and gained a better understanding of Minitab's capabilities :)

Could you pls reply

Hi Mugi, to run the normality test: Choose Stat > Basic Statistics > Normality Test support.minitab.com/en-us/minitab/21/help-and-how-to/statistics/basic-statistics/how-to/normality-test/before-you-start/example/ Hope this helps! Should you need further assistance, please feel free to contact us at minitab@bizits.com

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

Thank you for your comment Jesper!

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Hi, you may drop email to minitab@bizits.com with your contact details. Thanks. :)

Hi, you may drop email to minitab@bizits.com with your contact details. Thanks. :)

Hi javier maqueda ortega, thanks for your comment, we are glad you found the presentation helpful. Could you provide us your email address so that we can send you the presentation that was used during the webinar?