UCLA的一篇7页纸的关于MISSING VALUE的总结，比较全面，应用性强，欢迎参考。

1. Introduction

2. How SAS handles missing data in SAS procedures

3. Summary of how missing values are handled in SAS procedures

 - proc means

For each variable, the number of non-missing values are used

 proc freq

By default, missing values are excluded and percentages are based on the number of non-missing

values. If you use themissing option on the tables statement, the percentages are based on the total

number of observations (non-missing and missing) and the percentage of missing values are reported

in the table.

 proc corr

By default, correlations are computed based on the number of pairs with non-missing data (pairwise

deletion of missing data). The nomiss option can be used on the proc corr statement to request

that correlations be computed only for observations that have non-missing data for all variables on

the var statement (listwise deletion of missing data).

 proc reg

If any of the variables on the model or var statement are missing, they are excluded from the analysis

(i.e., listwise deletion of missing data)

 proc factor

Missing values are deleted listwise, i.e., observations with missing values on any of the variables in

the analysis are omitted from the analysis.

 proc glm

The handling of missing values in proc glm can be complex to explain. If you have an analysis with

just one variable on the left side of the model statement (just one outcome or dependent variable),

observations are eliminated if any of the variables on the model statement are missing. Likewise, if

you are performing a repeated measures ANOVA or aMANOVA, then observations are eliminated if

any of the variables in the model statement are missing. For other situations, see the SAS/STAT

manual about proc glm.

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4. Missing values in assignment statements