CODEWITHSUNDEEP
statisticsregressioncorrelation
user3087937
user3087937

Reputation: 45

multiple regression correlation effect

I would like to investigate the effects of two independent variables on a dependent variable. Suppose we have X1, X2 independent variables, and Y dependent variable.

I use two different approaches. In the first approach, to eliminate the effect of X1 on Y, I generate the conditional distribution of Y|X1 and perform regression using the second variable X2. When I check the correlations between X2 and Y|X1, I obtain relatively high correlations (R2>0.50). However, when I perform multiple regression over a wide range of data (X1 and X2), the effect of X2 on Y is decreased and becomes insignificant. How do these approaches give conflicting results? What is the most appropriate approach to determine the effect of X2 on Y for a given X1 value? Thanks.

Upvotes: 0

Views: 122

Answers (1)

Rafael Valero
Rafael Valero

Reputation: 2816

It could be good to see the code or the above in mathematical notation. For instance: did you include the constant terms?

What do you see when:

Y = B0 + B1X1 + B2X2

That will be the easiest to check, and B2 will give you probably what you want.

That model is still simple, you could explore something like:

Y = B0 + B1X1 + B2X2 + B3X1X2

or

Y = B0 + B1X1 + B2X2 + B3X1X2 + B4X1^2 + B5X2^2

And see if there are changes in the coefficients and if there are new significant coefficients.

You could go further and explore Structural Equation Models

Upvotes: 1

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