parameter interactions

[#1]

Hello everyone,

I have a few questions about interactions in JMP.

I'm trying to analyze the impact of different parameters on a Y variable.

From the linear model and the least squares regression I create interactions between the parameters.

For example JMP creates interactions such as: A*B, A*C...

First question : Are there other tools to calculate the interactions between the parameters?

Second question : When JMP indicates the A*B interaction, how does it concretely characterize this interaction? It does not equal A multiplied by B.

Thank you for your answers.

Cordially

[#2]

Here are my thoughts:

1. I'm not sure what you mean by other tools?  There are a number of methods to build/analyze models.  Typically models are analyzed using the fit model platform:

https://www.jmp.com/support/help/en/17.1/?os=mac&source=application#page/jmp/model-specification.sht...

I personally like to visualize interactions, so interaction plots are quite useful.

2.  How JMP handles the estimation of interaction effects is dependent on the type of data (e.g., nominal, ordinal, continuous) and how the data was acquired. Interactions between variables are commonly coded as the product of two independent variables in statistical analysis. This practice is based on the concept of interaction effects, which refers to the combined effect of two or more variables on an outcome.  When the effect of a factor depends on another factor, this is an interaction. For continuous variables in an experiment, the levels are coded equidistant centered on zero (e.g., -1, 1 for 2-level factors).  The coding "normalizes" the coefficients to make analysis easier.  If you code your variables, then, indeed the interaction of A*B is the product of the main effects A and B:

A  B  A*B

-1 -1   1

1  -1  -1

-1  1  -1

1   1   1

[#3]

Thank you for your reply.

1. By tool I mean the word method. I tried to fit a linear model taking into account the parameters and their interactions. My model has an R squared of 0.55 so not enough to explain my variable. That's why I can't rely on my model to consider my interactions... That's when I look for other methods of analysis

2. I understand how interactions work with your explanations, but on a concrete example what would that look like?

The table below:

How I could create a C=height*weight parameter (in terms of interactions)

Cordially

[#4]

Sorry, not enough context to provide specific advice and your data is not from an experiment.  R-square is just one of several statistics used to assist in building a model (delta R-square-R-square adjusted, RMSE, residuals, p-values, etc.).  I looked at your data table and I'm a bit confused...what is the response variable you are trying to model?  Is it weight (this is what your fit model is modeling in the saved script)?  If the response is weight, you would not put the interaction of height*weight into the model (height is the predictor and weight is the response variable) Also confused because the R-Square adjusted for that fit model is 0.649...don't know where the .55 comes from?  Height and weight are correlated (r=.71) and there are some outliers in your data (both multivariate and residuals).

[#5]

Thank you for your reply. 

The remarks in my first two points have nothing to do with the comic I sent. It was just a test comic. I learned about the subject, it is now much clearer to me.

[#6]

I agree with @statman wrt to the usefulness of interaction plots. And if you really want to impress your friends at cocktail parties, use the JMP Prediction Profiler slider bars for one of the terms and watch the slope of the second term change as you slide the one term from left to right and back.