In statistical inference based on regression coefficients, yes; in predictive modelling applications, correction is neither necessary nor appropriate. To understand this, consider the measurement error as follows. Let "y" be the outcome variable, "x" be the true predictor variable, and "w" be an approximate observation of "x". Frost and Thompson suggest, for example, that "x" may be the true, long-term blood pressure of a patient, and "w" may be the blood pressure observed on one particular clinic visit. Regression dilution arises if we are interested in the relationship between "y" and "x", but estimate the relationship between "y" and "w". Because "w" is measured with variability, the slope of a regression line of "y" on "w" is less than the regression line of "y" on "x".