Identify the Most Important Predictor Variables in Regression Models

Hi,
i have a problem to identify the most important predictor in regression.
There are three inputs that are statisticaly significant (p value = 0,000) and there is no multicolinearity.

What is difference between;
- F-Value
- Contribution
- Coef or coeded Coef

Which values should be considered when assessing the most significant predictor? I mean which one should be adjusted "as a first" in order to optimize the process.
Could be please give me the explanation to the attached files.

 

Hi Essegn, I could cut and paste the info from, my head (early Sunday AM here), my notes and the Minitab support site... but its easier and m ore accurate is you go to the MT link below, it s real good....hope this helps.

Cheers G650EG

https://support.minitab.com/en-us/m...cs/regression-models/regression-coefficients/

 

Hi G650EG,

thank you for your reply.
I was aware, that i get such an answer, but i am still unable to interpretate the most significant predictor variable.
I have made a summary statistic of Output - please look at the attached file.

From my point of view, the Input Y is the most important (because correlation & F-Value & PLS Regression, which is not included here).
But here seems to the most important Input X.

Coded coefficients are different - it depends which coding of continuous predictors are being to used - see the attached file.
I have read all the explanation for Coeded & Uncoded Coeffcients, Contribution, F-Test and F-Value.
It seems to me, that the coeded coefficients are the key, but there are still some obscurities.

Could someone help by interpretation of the results?

 

Thank you for the great explanation! It makes sense now.
I am still confused about standartized vs. non standdartized coefficients.

When are continuous variables being standardized?
Is there another reason as to avoid the multicolinearity?

In Minitab there are thee ways:
- Specify low and high levels to code as -1 and +1
- Subtract the mean, then divide by the standard deviation
- Subtract the mean

As can be on the attached picture seen (my last post), they give different results.
What is to be considered?

When a promissing model is being built (with non coeded coefficients, VIF is low), should be run an another run with standardisation of continuous variables, in order to have 1:1 comparison?