Design of Experiments - Science method

The coded equations enable the comparison between the factors in terms of directions and magnitudes of effects by simply comparing the coefficients. on the other hand, the equations in actual terms are not helpful in that comparison while they can be used for the prediction of the responses. So, you can use either the coded or actual equations for predicting a response but you have to substitute for the factors and other regression terms in coded values for the coded equations or in actual values in actual values equations.

To pick data for determining Validation Acceptance Criteria:

It took me a while to understand this. In my opinion, the data representation here is quite poor. Measurements taken during different periods add to the confusion, as they appear to be another factor. However, this might be a standard method of presenting such data in animal husbandry.

The data presented in the table do not represent results obtained from specific combinations of factors. Instead, they show the summary main effects for each factor (check picture). As a result, we have five "results." Since one level of the supplement is 0%, the authors could actually calculate the impact of MC on the diet from the main effects and interactions.

You can use path analysis. Lavaan package in R will allow this and compute all relevant direct and indirect effects for you.

Design Expert is a software designed to help with the design and interpretation of multi-factor experiments.

Yes, Rohith, that Godly power is within us! If we start practicing the truth of natural existence, the way Jesus professed and practiced, we can feel the power of God within us!

I agree with the idea of Wadie Abu Dahoud.

there are other normalization methods for the "nominal-is-better" case besides the normalization possible through grey relational analysis (GRA). Some commonly used normalization methods in MCDM for nominal scale data include:

Each normalization method has its advantages and disadvantages, and the choice of method will depend on the specific characteristics of the decision problem and the nature of the data.

I can send you a book chapter in PDF format on your email, if you want? Just PM me your email and I will send it :) Hemangi Narvekar

Eftychia Aslanidou Thank you! The book didn't come up when I clicked, but the other two did. I think that last one might be the most helpful, but I need to get the full text to be sure. I suspect (since I've read a newer comprehensive meta-analysis) it won't have the answers but it's worth checking out!

Good luck Adam, I cannot think of any other way, research is often a horrendous amount of work.

I am agree to what mentioned by Tarek Moussaoui. You may consider the following as well:

1- In simple presentations use graphs to show the effects of main factors and also the interactions.

2- In scientific papers and doctoral thesis, In addition to finding the optimum conditions of your experiments by using final model of RSM, you should perform a verifying experiment at that condition, and compare the experimental against the model predicted response.

desirability functions are used in the framework of Multi Objective optimization, they sort of try to attain a compromise among the different objectives ( for examples finding the factor settings that is simultaneously optimal for a set of objective functions, which may be the yield of product A1, A2 and so on)

I suggest you look into the theory and implementation of the desirability functions method, and other multi objective RSM methods, from the book on Response Surface Methodology by Myers, Montogomery and Cook, in this book there is a complete chapter dedicated to multi objective optimization methods and use and implementation of desirability functions approach in Minitab software.

There are few question necessery to ask.

Are you sure you have used Central Composite Design? CCD requires 5 levels for each factor: -axial, -1, 0, 1, axial. Perhaps you used Box-Behnken Design which requires -1, 0, 1?

Nextly, what do you mean you used different data, which gave you a good model? You already had responses for this exact design? If not, maybe previous data does not represent your current experiment?

Eventually, are you sure your factors significantly affect the response? They might not, therefore it can not find significant model.

You're missing the point here since you are doing ANOVA I assume explanation is your goal. So why do you need to consider two models at all.unless you are doing something like response surface methods. In any case.p-value selection doesn't work either. I suggest you see the two references below

Especially the one by Montgomery available for download from the z-library. Best wishes David Booth

It might have more runs, no?

There is a 80 minute presentation by Statease, describing how to sophisticatetly assess if "more runs", etc, are really necessary. It explains the concept of "Fraction of the Design Space", FDS, graphs.

How do you conclude that "There is no [...] interaction"? Because the respective p-values from your sample happend to be larger than your level of significance?

Note that "Absence of evidence is NOT evidence of absence" (Cox).

You can (you should) ignore an interaction when it makes no sense or when you want to study marginal effects.

يكون من الافضل والدقيق استخدام المتوسط الحسابي الى استجابات @

As per my knowledge, CRD is used for homogenous experimental units, say lab and greenhouse research where influencing factors can be made the same for all treatments. But in field experiments, there is the heterogeneity of soil and climatic components and in such cases we use RCBD. Based on your question, just to answer it, I would say, when there is homogeneity in experimental units RCBD and CRD could be same which is possible in greenhouse and lab research only not in field experiments.

Dear Sir/Madam

Below, I presented my experimental data published in https://www.sciencedirect.com/science/article/abs/pii/S0960852419313148

Let's try to find the obtained quadratic equation using Design-Expert software and compare the results:

Q=+838.49+202.28A+382.17B+416.84C+94.04AB+109.54AC+278.44BC-220.71A²+125.44B²+16.19C²

A B C R

10 6 1500 347.5

10 3 2500 429.3

10 3 500 200.3

10 9 500 316.7

10 9 2500 1350.2

125 3 1500 478.6

125 6 500 483.2

125 6 2500 1250.3

125 6 1500 790.2

125 9 1500 1473.4

240 9 2500 2276.5

240 6 1500 912.2

240 3 500 312.3

240 3 2500 670.2

240 9 500 495.6

@Sakil Mahmud That just is not true. That is a rule-of-thumb that has been debunked over and over again. Did you even bother to look at any of the other answers to this question? Instead of finding articles to copy/paste from that you clearly don't understand, please keep these threads free of unhelpful and misleading answers.

following this link

I suggest you use 'doebioresearch package'. Is very good in factorial data analysis. Most especially agricultural experiments.

non-reproducible outcomes suggests that there are one or more fundamental flaws in the research. Your sample size might be too small given system variability. You might be missing some key variables. The analysis might not be appropriate. Some combination of all three. Some experiments are difficult, and maybe I can only have three replicates. I run the experiment again and get a different outcome. If you are certain that you have the right experiment, then try running the experiment several times and block each time, but analyze as one experiment.

I agree with the suggestions provided by @Andrew Paul McKenzie Pegman and @Medhat Elsahookie. Moreover, in field conditions, you may think of re-seeding/planting to maintain the same upto a certain period.

As I think, you must construct and build this device by yourself. It is a classic tradition of all famoust physicists.

Wrong. Factorial design will work here if I understand your description correctly. See Montgomery Design and Analysis of Experiments available in the z-library for download. Thinking in terms of regression rather than ANOVA should be of help to you. Best wishes David Booth PS follow up with Response surface methods to see if you can optimize your design.

Hi! You might want to check this free online randomization app. You can apply simple randomization, block randomization with random block sizes and also stratified randomization.

The simplest analysis would be a repeated measures ANOVA, with the repeated measures factor being defined by the emotional intelligence scores before and after a night shift. Yours additional variables can be added to the model as between-subject continuous variables (sometimes referred to as covariates in packages as SPSS), or as between-subject factors. The interactions of the repeated measures factor with the covariates or between-subject factor will let you know if the effect of occasion is different for different values of your additional variables.

One main problem with using this simple approach is that if there is attrition, then the cases analysed can only be those with complete data. If there is a substantial amount of missing data, then other more advanced procedures will need to be used to minimize bias (eg linear mixed models, multiple imputation etc).

I think you are asking a question about how the data would be analyzed, not a question about physically you would arrange the treatments. If I understand correctly, either option you present has an observation for each combination of reactant and the other factors. That is, in either case you would need at least 32 experimental units (4 x 2 x 2 x 2).

You will want to have omnibus model to analyses these data. (Not four separate models, if that was the question). A single model will make the best use of the information in the data.

However, you may not want to include all potential interactions in the model. So, for example, a model that might reflect your (4 x 2^3) thinking might be, Y ~ Reactant + A + B + C + AB + AC + BC + ABC .

Hey Diogo Kawano I use and recommend an online software called Mind the Graph. It has a big illustration library, and if you need a specific illustration they will make it for you. Check the website! Good luck!

https://mindthegraph.com/

Hi Arvin Hajipour

I suggest you the followng book by D. Montgomery:

Montgomery, Douglas (2013). Design and analysis of experiments (8th ed.). Hoboken, NJ: John Wiley & Sons, Inc. ISBN 9781118146927

Best regards,

Ebrahim

It's a matter of the units you use

Zebrafish don't really have a breeding season. They have asynchronous ovaries meaning that all stages of oocytes are present, and they can spawn daily (or multiple times per week) throughout the year. Oocytes are released when stimulated by males during the breeding process.

This depends upon seed available with you. If you do not want to go for augmented then conventional will work.

Hi Amin .. ,

You wrote "I fixed X2 and varied X1 (X1= 5%, 35% and 65%). I observed that Y increases with increasing X1 from 5% to 65%."

However, exploratory studies should be also based on DOE (https://www.itl.nist.gov/div898/handbook/pri/pri.htm ), otherwise you can get a partial and bias view of your experimental conditions.

Please refer to the following link, it answers your quession very well.

Lesson 11: Response Surface Methods and Designs

When there are 30+ reactants, I first would make a network, with the input of the experimenters, of the relations between the reactants: you really have to understand parts of the chemical reactions . Modeling, without understanding the basics of what you are trying to model, is never a good idea. And, given my knowledge of chemistry, I fail to see the use of stochastic optimization in this context. Maybe systems and control theory could give you insights as well. Maybe you can view the whole as a system, with inputs and outputs.

This link maybe useful for you for downloading design expert(DX8.0.6) and miniTab 18.1 with crack for longer use

Dear Muhammad Amir Maqbool

You can use augmentedRCBD package in R software.

You need to describe your outcome more. It is common in some areas to use methods like ANOVA with a Likert scale, lets say 1-5. But that is generally a bad idea. You might get away with it if your scale is from 1-7 or greater, but still generally a bad idea. I attached research showing empirically why this is so and more appropriate models to apply depending on your outcome variable. Essentially, the distance between categories of 1 and 2 might not be the same as 2 and 3 or 3 and 4, etc. Even if there are numbers on the scale. The psychological distance could be different and different for some people. At best you miss relationships and at worst you have a model that makes no sense. For classic ordinal Likert scales I would go with generalized linear modeling using a cumulative distribution with a probit link function. But the article by Bürkner which is attached goes into more depth.

Thank you, Andrea

Dear Subash S. ,

I suggest you to see links and attached file on subject.

Thank you for your answers. Actually, I had a lot of samples. There were 8 variations and two Central points, so 4 replicates per variation and 8 replicates per central point (due to a categoric factor). I produced 48 samples and cutted them into 50 stripes per sample. For the response I described, I only used 5 of each.

The design was a fractional factorial, so I had to deal with many aliases.

Looking at the problem some weeks later, I think the range of factors being too large caused the amount of significant effects.

When your surface is non-linear (interactions between variables exist, as Prof. David Eugene Booth mentioned), you can easily miss the optimal point using the OFAT. Yet, you might want to use it to sense your system and assess the importance of variables to better judge the ranges you might want to consider for a systematic DOE. A good practice is to check the literature for approximate values of the variables you are considering in your study. That will bring you closer to the optima and saves your time and resources.

Check the "Experimentation for Improvement" MOOC in Coursera (https://www.coursera.org/learn/experimentation). Montgomery books are excellent resources as well.

Thank you very much to all of you

Collaborative design is thought to have some general advantages and disadvantages. Depending on your experimental setting, the collaborative aspect could improve the outcome for each group. Importantly, the individual designers may be encouraged to express their ideas/understanding (sketches, notes, conversations) when working together. This might help the group keep track of the design process, as opposed to individual designers who tend to make fewer snapshots of their process. So if the experiment is comparing the process or the outcomes, grouping participants can be helpful. A benefit of having individual participants is the number of data points you collect.

Modern engineering design education today relying primarily on the project-based coursed, performed by the group of students. For a reason. It is almost impossible to imagine a relevant industrial task implemented by one (even skilled) individual. It's simply not a real-life scenario.

A simple by hand solution is to start with a fractional factorial of the 2^10 in 16 then replicate with the 3 level varying each time. That's 48 treatments. This guarantees you'll be able to measure all main effects of the 3 as well as all interactions between the 3 with each 2 level factor but only get the main effects of the 2 level factors. Unfortunately the main effect of the three cost you 2df, each main effect of the 2 will require 10df and each interaction will require 10x2df=20df. That's a minimum of 32df plus one for error is min 33df. So a 48 is not bad but someone may have a better solution in 36 which I suspects may exist with some careful confounding. This is bc 36 is a 6^2 which caters nicely to 2 and 3 level attributes.

You have a within x wihtin x within x within design. I could run suvh a design if you xwant. Just send me the data

If you would like to double the repeated measures, e.g T1, T2, T3, T4, T5, T6, you could run the Cochran Q test followed by post hoc McNemar tests (thus, adjusting for multiple p-values). Instead of a (2x3) Between x Within subjects design, you then could test a (3x3) Within x Within design.

Dear Jabir Ismaeili,

I have attached the excel file which I try to display the design of experiment. I hope it would be useful.

Our lab at the University of Birmingham have just started using Gorilla.sc to build online experiments. So far, I have found Gorilla.sc really intuitive and easy to use and so I would definitely recommend it to other researchers. I'm not 100% sure whether you will be able to have the file playing in the background for the whole experiment, however, there is an option to have audio files playing in on each trial on Gorilla.sc so you might be able to?

If you do choose to use Gorilla.sc, some of the following webpages could be helpful to you! I referred to some of the following webpages to get an idea of how to build a task successfully:

Video walkthroughs that give a speedy introduction to using Gorilla.sc, some of which I found quite useful: https://gorilla.sc/support

Sample Code: https://gorilla.sc/support/script-samples#altercontent. I found the sample code really useful as there were examples for altering or adding content to the task via script (i.e. forcing participants to be in fullscreen when taking part, changing background text and colour, implementing live scoring etc.).

Example Projects: https://gorilla.sc/support/samples

Open Materials: https://gorilla.sc/openmaterials

Hope this helps! Wishing you all the best with your research!

Dear Nils Wagner,

I suggest you to see links and attached files on topic.

Best regards

Hi,

Please check this

If you take data from something not in the experimental design it is not part of the experiment but might in some way be treated as a covariate or covariates. The classical paper on such things (in this case rainfall) is by R A Fisher referenced here: and can be downnloaded from the Fisher archives at this link: https://scholar.google.com/scholar_lookup?title=The%20influence%20of%20rainfall%20on%20the%20yield%20of%20wheat%20at%20Rothamsted&author=RA.%20Fisher&journal=Phil.%20Trans.%20Roy.%20Soc.%2C%20London&volume=213&pages=89-142&publication_year=1924

I am sure some clever searching from Google or Google scholar will yield many more such references. You might also check this link which may be easier reading: Montgomery, Design and Analysis of Experiments: https://www.google.com/search?q=montgomery+d.c.+design+and+analysis+of+experiments&rlz=1C1CHBF_enUS915US915&oq=Montgomery+D+C+&aqs=chrome.1.69i57j0l6j69i60.19204j1j15&sourceid=chrome&ie=UTF-8 Start with ANCOVA. Best wishes, David Booth

Dear Nate Tran, hope the following documents will be usefull in your work. My Regards

I suggest you follow :

https://support.minitab.com/en-us/minitab/18/help-and-how-to/modeling-statistics/doe/supporting-topics/mixture-designs/what-is-a-mixture-design/

https://www.hindawi.com/journals/tswj/2015/684319/

No.

Yes,@Saurabh, you need to first KD the gene and then also overexpress it. If you don't do it, the referee or your examiner would ask for that.

I used this strategy and did my KD and over expression in duplicate each time, one for the RNA purification and other for the protein.

On the topic of "control groups":

Indeed, in a factorial design, each factor has a distinct control group. That's why I mentioned that you need at least one control group in an RCT. The RCT-logic still holds.

If you have a full factorial design, you could say that the group which represents the combination of the baselines of all factors, is semantically the "most authentic" control group. However, statistically, as you mention, the comparison is within the factors. At the same time, the analysis considers the interaction effects between the factors. That is the beauty of factorial designs.

For a very thorough, very well written, and highly applicable introduction into RCTs (and, as an extension, also factorial designs) I can highly recommend Gerber's and Green's book https://wwnorton.com/books/9780393979954

Yes, if the p-value is greater than 0.05, you may remove the component from the model.

Dear Luis F. Gouveia

if you want, i can send free trial design expert(DX7) softwer for you.

it is suitable for DOE.

best rigards

reza asadi

Dear Denish,

why placket burman?

It is used for screening the factors and selecting the most effective factors.

best rigards

Hi, interesting question. Could you provide more information for the search of the mentioned articles (ciftni et al 2008 and kamran et al 2015 , kamran et al 2016 )?,

Thanks

Ahmed Y. Kira

you can use CCD or BBD in RSM (DX7).

10-12 test.

Milon Selvam Dennison

DX7 is better.

best rigards.

reza asadi

Maryam Moazami Goodarzi

Your question is very vague. Please make it clearer.

best rigards

some permissible ranges in works of literature, in addition other molding and curing conditions can be determining factors. watch this work: Potential use of ceramic waste as precursor in the geopolymerization reaction for the production of ceramic roof tiles

I recommend to you that plot Y vs B and D (3D plots) at 3 or 4 different values of A. My recommendation is supported in the fact that B has the quadratic effect and D has higher coefficients values than A.

Quite often, QC data can be significantly unbalanced. I had a data set with 20+ variables and 100,000+ subjects from a line of a particular product, and 100-120 of the subjects came back for warranty work. The goal was to find the important variables to help predict which products would come back for warranty work. All the standard statistical methods failed. I didn't.

Maybe you can find a data set that has highly imbalanced data (Like cancer) and find something interesting.

If you designed an experiment in Minitab and you analyze it through the 'Analyze' option in the DOE sub-menu, the software assumes that the highest level for each factor is something like '1'. The lowest level is '-1'. If you analyze the data through the Regression sub-menu, then it uses the actual levels for the analysis and creates all sorts of issues for analysis.

Under the DOE submenu, after analysis, there will be 2 sets of coefficients. One for the scaled factors levels (-1, 1) and another for the unscaled levels. When you go to optimize, use the coefficients for the coded model.

In general, ANN is useful when you have lots of data. DOE tries to minimize the amount of data you use.

The p-value is not at all informative here. You need to find out what the characteristic of the lack-of-fit is. Examine diagnostic plots. Simulate fake data based on your model and see if this is in relevant discrepancy with your observations.