This paper reviews methods for regression analysis when independent variables (X's) are missing. Six classes of procedures are distinguished: complete case analysis, available case methods, least squares on imputed data, maximum likelihood, Bayesian methods, and multiple imputation. The author compares and illustrates these methods when missing data are confined to one independent variable, and extensions to more general patterns are indicated. The performance of methods is discussed when the missing data are not missing completely at random. Least squares methods that fill in missing X's using only data on the X's are contrasted with likelihood-based methods that use data on the X's and Y. The latter approach is preferred and provides methods for elaborating the basic normal linear regression model. The author suggests that more widely distributed software is needed that advances beyond complete-case analysis, available-case analysis, and naive imputation methods. Bayesian simulation methods and multiple imputation are reviewed as fruitful avenues for future research.This paper reviews methods for regression analysis when independent variables (X's) are missing. Six classes of procedures are distinguished: complete case analysis, available case methods, least squares on imputed data, maximum likelihood, Bayesian methods, and multiple imputation. The author compares and illustrates these methods when missing data are confined to one independent variable, and extensions to more general patterns are indicated. The performance of methods is discussed when the missing data are not missing completely at random. Least squares methods that fill in missing X's using only data on the X's are contrasted with likelihood-based methods that use data on the X's and Y. The latter approach is preferred and provides methods for elaborating the basic normal linear regression model. The author suggests that more widely distributed software is needed that advances beyond complete-case analysis, available-case analysis, and naive imputation methods. Bayesian simulation methods and multiple imputation are reviewed as fruitful avenues for future research.