Negative value of response in central composite design]]>

I am running an experiment with four categorical variables. They all have two levels.

My issues is that one of the variable will be active only when one of the another variable will have a specific level.

Example, I have A, B, C and D variables.

Each variable has two levels Yes and No.

Variable D levels Yes and No are valid only when variable B is Yes.

How can I model this DOE in Minitab.

Your help is highly appreciated.]]>

we have to run a DOE (using Minitab) but we know our distribution will be non parametric.

Do you know if this could be an issue while analyzing data or do you have any recommendation?

Thank you in advance,

Marta]]>

Here some example:

View attachment 946]]>

I have to run a doe and I need to estimate the number of repetitions.

How do you handle this estimation when you do not know the variance of the process and the effect size?

Could be ok to run a hypothetical number of reps (e.g 3) and after collecting data assess the power of the test? Eventually adding more reps if necessary.

Another doubt I have is: how can I assess for the effect size given that I have a multilevel multifactorial doe? The effect size is the difference between...

Sample size determination and effect size]]>

I have been asked to design a DoE experiment for a project involving food supplement formulations. However, I am not sure DoE can even be used for this study so am asking for some advice.

We have 20 active ingredients that can be mixed in different quantities and combinations to produce a formulation. A formulation is successful based upon non-quantitative measurements (looks, taste, smell etc).

Based on this information, I believe this means that the study would use 20...

DoE with a large number of categorical variables]]>

first time posting here. I have created an online tool for creating and analyzing design of experiments. It is called desice.io. Currently it is free of charge. Feedback is welcomed.

Kind regards,

Rupert]]>

I am using Minitab to generate some experimental designs which generated a Pareto Chart. It contains a red line which indicates the significant factors - does anyone know how this red line is computed? I can't seem to find the equations used for this in the documentations.

Thank you.]]>

I was wondering if anybody wants to share His or her experiences in using DOE in low tec warehouse environments?

Specifically in finding the right setting for the optimal warestorage in the high level rack, the best setting for truck unloading etc.

Processes which don't generate lot's of useable data due to very little machine use

Inputs are much appreciated, thank you very much]]>

I am trying to assess the effect of several scheduling policies (treatments) on a the waiting times for patients (response). These are computer experiments.

For each factor-level combination, I draw n patients at random, schedule them, and record their waiting times. There is no reason to assume that the n patients for each factor-level combination constitute n replications as the patients are independent and require different operations, due dates, resources, etc....

Experiments without replications]]>

"Subset" DOE]]>

I am currently trying to use Minitab DOE to analyse some factors in terms of their significance and main effects. When I tried using the analyse factorial design function, I can choose the order of the terms I want and whether I want to include any interactions into the analysis.

By having different order terms included in the analysis - the result in terms of the identified significant factors are different. Factor B was deemed insignificant when using only the single-order...

DOE (Minitab) - Analyse Factorial Design with Higher Order Terms]]>

ANOVA output of RSM]]>

wish to run each treatment once in the morning, between 9:00 and 12:00. and once in the afternoon, between 1:00 and 4:00. all four treatments in the morning followed by all four treatments in the afternoon. and perform the test for 4 consecutive days.

Now, days, order of run in mornings or evenings , as well as morning or evening tests seem nuisance factors. so I came to graeco.

but each cell of graeco table shows the run...

Design experiment with multiple nuisance factors including time]]>

Details of the method used are at...

Optimizing Factor Levels when Results are Qualitative]]>

I conducted a full factorial experiment with three factors à 2 levels each. I did not conduct replicates of those but added 3 center points.

Factors:

o Incubation time (0h, 2h, 4h)

o Process temperature (30°C, 40°C, 50°C)

o Process duration (Time) (4h, 7h, 10h).

Ouput variables: texture coefficient, yield.

Here’s my data:

Nr. Text. Yield Incub Temp Time

1 h1 2.63 54 0 30 4

2 h2 1.71 51 4 30 4

3 h3 2.37 56 0 50 4

4 h4...

Analysis 2^3 with 3 center points]]>

I was running an unreplicated two-level factorial design with three center points for screening of my factors.

The ANOVA-output of my data gave me a significant regression (which is good), but also a significant curvature together with an insignificant lack of fit.

My interpretation of this is that i do have a significant curvature, but my regression model is nonetheless able to model my datapoints (the residual plots also look as if the regression does adequatly model...

Significant curvature with insignificant LoF]]>

What would be the best approach for an only experiment with 4 factors?

Acid

Temperature

Concentration

Time

The thing is that there are two important considerations in this experiment and its necesary some ideas to apporach the experiment correctly.

1. There are three different acids and they wouldnt combine ever.

2. One of them (Acid C) allows a higher temperature due to its boiling temperature.

It looks something like this:

Acid A

Temperature: 10-20 ºC

Concentration: 5-10 %...

Designing an experiment with some consideretions]]>

So my question is, numerically speaking, are the edge of limits of the DoE the tolerance values of the QTPP and CQA parameters?

Thanks in advance.]]>