Regression Dilution
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High Quality Content by WIKIPEDIA articles! Regression dilution is a statistical phenomenon also known as "attenuation". Consider fitting a straight line (linear regression) for the relationship of an outcome variable y to a predictor variable x, and estimating the gradient (slope) of the line. Statistical variability, measurement error or random noise in the y variable causes imprecision in the estimated gradient, but not bias: on average, the procedure calculates the right gradient. However, variability, measurement error or random noise in the x variable causes bias in the estimated gradien...
High Quality Content by WIKIPEDIA articles! Regression dilution is a statistical phenomenon also known as "attenuation". Consider fitting a straight line (linear regression) for the relationship of an outcome variable y to a predictor variable x, and estimating the gradient (slope) of the line. Statistical variability, measurement error or random noise in the y variable causes imprecision in the estimated gradient, but not bias: on average, the procedure calculates the right gradient. However, variability, measurement error or random noise in the x variable causes bias in the estimated gradient (as well as imprecision). In a nutshell: the more variability is in the x measurement, the closer the estimated gradient gets to 0 instead of the true gradient. This 'dilution' of the gradient towards 0 is referred to as "regression dilution," "attenuation," or "attenuation bias."
