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How to analyze unbalanced data which three of independent factors are unbalanced levels?

Shad

Hello,

I have a data set which has four predictors: three of the predictors including time, distance, and power are unbalanced and one balance predictor. Do you have any idea what is the best way to analyze such data? And how I can analyze them? I do appreciate any help.

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Peter_Bartell

Every analysis 'how to' question should start with an articulation of the practical problem you are trying to answer. You haven't shared that with the Community. Please do.

Then a thorough evaluation of the means by which the data was collected. A designed experiment? Happenstance data? Historical data in time series? And what do you know about the measurement system and it's variability?

Are there are any issues associated with that process which make subsequent analysis problematic. Missingness, outliers, nonsense values. What about correlation of predictor variables? Then embark on analysis...at the highest level I have three thoughts for you:

1. Plot the data.

2. Plot The Data.

3. PLOT THE DATA...and this is where JMP shines.

Shad

Hi Peter,

Thanks for your prompt response. Here is the problem articulation:

The experiment is a designed experiment. The objectives of this study were to determine the suitable combinations of IR heating duration, Gap distance, and intensity, followed by tempering treatments to maximize inactivation mold spores. I did plot the data and since it is not a balanced design there are some missing values. When I did the full factorial analysis the effect summary is not showing any values.

I was reading the same issue stated from other people: I found that I need to analyze using mixed model but I am not sure which variables should be considered as fixed and which as random?

BTW, how I can share my questions with the community now?

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Chris_Kirchberg

Thanks for the screen shot @Shad,

Based on the image, you have 15 terms in the model. IF it is a DOE with 4 effects and a full factorial design, then that would mean you have 16 runs. Is that correct? If so, you would not have enough degrees of freedom to estimate any of the effects/terms in the model (not enough data). You might want to start with stepwise regression to add terms in a forward stepwise fashion. Otherwise, remove terms that are most likely not going to have an impact (4th degree polynomial for example).

Hope that helps.

Chris

Dan_Obermiller

Another possible approach rather than going forward is to specify a model with only main effects and two-way interactions. Remove the three-way and four-way interactions.

Shad

Thank you, Chris.

I have 4 main effects in total but each effect has different levels. For example, the time has 6 levels, Gap has 3 levels and so on. I attached a screen of my distribution analysis. Another thing is that when I did multiple regression, some variables are correlated. And also by doing full factorial, VIF showed high values. I am wondering are these VIF value true since I am not sure the full factorial is right? As I said some of the parameter estimates are missing.

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