## Gustav

was active active 1 week, 1 day ago## Activity

@rbutler thanks again!

But why can’t I use Dunnett’s test if my purpose is to find a factor or combination of factors to optimize for max success rates?I have done Tukey test, and it shows the same, that A factor is significant compared to Control. However, A1B0 vs A1B1 is not.

Does it mean that in this case of logistic regression results of…[Read more]

@rbutler thank you for your reply!

Number of trials is actually number of time each treatment ran, for example for A0 B1 there was 7539 runs and in 141 cases there was a success for count2 response and 93 successes for count response. I am considering now only count2 response for the analysis though.

I rewrote it in R to be a bit more clear:

A…

Actually it is better to transform it to 1-factor 4-level design.

The results in this case show that A factor is significant.

Hello!

Just curious why we see different results in such cases.

Here we have a classic 2-factor 2-level factorial design which we can analyze using Nominal regression/logistic regression.

Analysis shows that only interaction is significant :

However, if we transform this data as if there were 4 factors…[Read more]

Please help me understand how to interpret the results of full-factorial experiment with 4 factors when 1 of 2 main effects is not significant, but their interaction is significant.

My dataset:

A B C D Trials Succeses

1 0 0 0 0 1852 11

2 0 0 0 1 1878 3

3 0 0 1 0 1869 9

4 0 0 1 1 1881 14

5 0 1 0 0 1926 4

6 0 1 0 1 1920 6

7 0 1 1 0 1891…[Read more]@rbutler , thank you very much for clarification and suggestion on learning DoE.

I agree with the approach regarding software that you suggested, so I will go that way.

Also by the way I found that it is possible to simulate responses using JMP’s Simulate Responses feature and it works well.

The only difficulty that I encountered is how to…[Read more]@rbutler, thank you very much for your help once again!

I am sorry, I think I was not clear enough. I used the data in 1st post just for example.

The actual case is that I do not have response values.My input is:

1. Fractional factorial design with 15 factors resolution IV

2. Only effects are known, 8 effectsEffect sizes:

Factor % of…

Hi @GChollar,

Thank you!My purpose was to derive responses for each row from effects and (1).

Do you mean that I can calculate responses for each row (treatment combination) from predefined effects (A, B, C, (1)) using linear regression? Unfortunately, I could not understand how.

In my case (1), A, B, C, AB, BC, AC, ABC are known.

a ab ac abc…[Read more]Hi, @rbutler

Thank you for your reference, I found it on Amazon, I wish there were kindle version.

Currently I am using a book by Barker “Quality by Experimental Design” for learning.Thank you for sharing your experience. I understand your point regarding sample size and I agree with importance of time/money/effort balance. I understand what…[Read more]

Hi Sergei @ssobolev,

As far as I know only JMP has this functionality. I could simulate data in JMP for normal distribution but for binomial data it was difficult for me to translate the estimated effect sizes to coefficients that are need to be entered in Response Simulation dialog box.

2 years, 11 months agoThere are 3 points to reverse engineer DoE for me now.

1. With a help of simulation it is easier to learn DoE

2. Simulation is useful for estimating power and sample sizes, choosing different designs

3. Currently I want to create some software that will check whether online-analytics setup works correctly. I want to simulate human behavior -…[Read more]I found some simultaneous equation calculators, so I hope it will solve the problem.

Thank you very much for a direction! 2 years, 11 months ago@rbutler, again tahk you,

At this point I can accept perfection or I can try adding a noise in simulation later.

I have used this code to simulate full factorial with binomial response (0 or 1):

`New Table( "Untitled",`

[Read more]

Add Rows( 10 ),

New Column( "A", Numeric, "Nominal", Format( "Best", 12 ),

Formula( Random Integer( 0, 1 ) ) ),…Hi!

Sorry, I thought there were no new replies in the topic because I haven’t received any notifications.

Here is the JMP script that generates trials for full factorial:

`New Table( "Untitled",`

[Read more]

Add Rows( 10 ),

New Column( "A", Numeric, "Nominal", Format( "Best", 12 ),

Formula( Random Integer( 0, 1 ) ) ),

New Column( "B",…@rbutler, thank you!

As I understand 1 in this case is a value when all factors at -1. If so, I can specify this value.

Is it possible to solve the equation in such case? 2 years, 11 months agoHello,

I am learning DoE and want to simulate responses for fractional factorial design.

As an input for simulation I want to use estimated effect sizes of main factors and some interactions.

I know there is a formula for calculating effects from obtained responses:

“For each of the main effects, the estimated effect consists of the mean…

Could you please help me with this problem?

I want to create a computer emulator for a learning purpose. It will emulate a binary outcome and use several Yes/No factors as an input (lets say A B C).

The output will be the % of successes.The output when all factors are at “-” is 5% of successes.

When

A at “+” 5.5% (0.5% gain)

B at…[Read more]I have a data:

`A B C D Successes Trials Rate`

[Read more]

0 0 0 0 19 19000 0,100000

0 0 1 1 21 19000 0,110526

0 1 0 1 17 19000 0,089474

0 1 1 0 21 19000 0,110526

1 0 0 1 15 19000 0,078947

1 0 1 0 22 19000 0,115789

1 1 0…How many variables are we talking about?

4 variables.

What kind of a fraction? Half rep, quarter rep, saturated?

Resolution 4

How do you know that the response will be at its absolute minimum for the design space when the setting are all at their low levels?

Variables are categorical. Type: Yes/No

The standard deviation of the response is…

Hi

Could you please help me with sample size calculation in Minitab?

I want to conduct 2-level fractional factorial design.

-I know the SD of a response.

-I want to achieve 80% of power (P).

-Number of variables is known – k.

And I want to detect the smallest effect of d=20%.

So that if we perform a response optimization, then calculate…[Read more]- Load More