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Multiple Regression

Hello,

I have an scenario where my response is a continuous variable and predictors are mixed(continuous and categorical with more than 2 levels)

In the response i see a trend and my goal is investigate which predictor are trending with it.(Other words which predictor is mostly contributing more in response trend because trend is a bad sign for given proccess.)

I am trying  different ways by using multiple regression but i have some concerns:

Would be better to run different regression for each type of variables(one time with only numeric and next time with only categorical) to detect the causing trend predictor?

When i run the model with all variables i am getting some NA for some estimates and this is a sign of correlation. In this point i am not sure how to decide which variable should i have to remove to avoid multicollinearity.

I hope for some ideas.

Thanks. 

AnalyticsRe: Multiple Regression

When you say, 'trend'...I'm presuming you mean a time based systematic change in the response. For the continuous predictor variables I suggest a simple overlay plot in Graph Builder leveraging the time sequence of collection of the responses (maybe with a local data filter to highlight specific x's efficiently). For the categorical variables is there still a lurking time element? Say, like batch 1, 2, 3, 4, etc. where each subsequent batch is bounded in a time sequence? If you've got a time sequence to the categorical variables, use the Value Ordering column property to get them to show up in the correct time sequence in Graph Builder. If you all you are really interested in is detecting this time based trend...you should be able to 'see it' in Graph Builder and not even need to go to any modeling.

Re: Multiple RegressionActually , i am interesting more at variable that is causing the trend than when it happened.
Thanks.Re: Multiple RegressionHello,
I am trying to use this method but it does not work because it gives error related multicollinearity.
Warning message:
In leaps.setup(x, y, wt = wt, nbest = nbest, nvmax = nvmax, force.in = force.in, :
2 linear dependencies found
Thanks for your help.