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I'm running multiple linear regression with 6 variables. For one of the variables D, the correlation coefficient between D and the response Y is - 0.34. But in the regression output, the coefficient for D is +8.9.
What is the best way to interpret D's influence on Y? Is it safe to assume that there's some confounding going on so I can ignore the fact that the correlation coefficient is negative and thus say increasing D will result in increasing Y?