The purpose of this study is to better characterize factors associated with the likelihood of macronutrient excess or inadequacy among U.S adults by modeling parts of the conditional distribution of dietary intakes other than the conditional mean. The risk of dietary inadequacy or excess faced by an individual tends to increase as his or her intake moves from the mean of a nutrient intake distribution toward its tails. Therefore, marginal effects of explanatory variables estimated at the conditional mean using ordinary least squares may be of limited value in characterizing these distributions. Quantile regression is effective in this situation since it can estimate conditional functions at any part of the distribution. Quantile regressions based on data from USDA's 1994-96 Continuing Survey of Food Intakes by Individuals indicate that differences in mean macronutrient intakes based on sociodemographic characteristics can be quite different from intake differences at other parts of the distributions. Therefore, judging dietary disparities between subpopulations by comparing mean intakes only, and not by comparing intakes at other parts of the distributions, may lead to misleading or incomplete conclusions.