Given the pattern that self-reported BMI values underestimate "true" BMI values, except at very low levels of BMI, and given that the discrepancies between self-reported and measured BMI vary systematically with height, weight, age, gender, pregnancy status, marital status, income, and, to a lesser extent, with race and ethnicity, we used these variables to predict measured BMI scores (Table 3). The results from this regression model show that self-reported height and weight, in conjunction with a few demographic characteristics, account for more than 92% of the variation in measured BMI scores. (Note: We used a polynomial regression approach for height and weight and age, eliminating higher power terms, if they showed no significant effect on the dependent variable [24].) The predicted BMI scores from this model represent "adjusted" BMI scores, which take account of all the predictor variables in the equation. While the mean discrepancy between the adjusted and measured BMI is close to zero, since it is a residual score, these discrepancies continue to show a systematic, though smaller, bias in relation to the measured BMI. The simple linear
