Unfortunately the SAS documentation doesn't give any ODS table names for proc means. You can use the output statement to create the table.

proc means data=;

var;

output out=table1(drop=_type_ _freq_) mean=avg;

run;

The results will be stored in work.table1 & the means will be stored in variable 'avg'. You can change the variable name. It creates 2 additional variables _type_ & _freq_ which if you don't require you can drop as I did in above code. If you want additional statistics then you'll have to create additional variables. Say I want to add # of obs, sum & stddev then,

output out=table1(drop=_type_ _freq_) mean=avg sum=s n=num_of_obs std=s;

Things change if you use a class variable. Output statement creates an observation for each level where level is the combination of classes. Say I used a class variable 'Gender' & 'grade'. Gender has 2 types: M & F; Grade has 4 types: A,B,C,D. I used following class statement:

class grade gender;

Therefore, there are 11 levels:

1) _type_=0: mean for all dataset.

2) _type_=1: mean for all M.

3) _type_=1: mean for all F.

4) _type_=2: mean for all M & A.

5) _type_=2: mean for all M & B.

6) _type_=2: mean for all M & C.

7) _type_=2: mean for all M & D.

8) _type_=2: mean for all F & A.

9) _type_=2: mean for all F & B.

10) _type_=2: mean for all F & C.

11) _type_=2: mean for all F & D.

Note that _type_=1 is the rightmost variable in the class statement, _type_=2 is for rightmost 2 variables & so on.

So your output dataset will have 11 observations. If you want just the 8 observations for MA,MB,MC,MD,FA,FB,FC&FD then use the nway option or specify the _types_ number. See both the options in the following codes:

NWAY:

proc means data= nway;

var;

output out=table1(drop=_type_ _freq_) mean=avg;

run;

NWAY automatically calculates the stats for the highest _type_. So you'll have means for ma,mb,...,fd.

_types_= :

proc means data=;

var;

output out=table1(drop=_type_ _freq_ where=(_types_=2)) mean=avg;

run;

This will also give the same result. With this you can customize the types. Say you want the mean of entire dataset then use _type_=0.

There is a third option also, the Types statement:

proc means data=;

var;

types grade*gender;

output out=table1(drop=_type_ _freq_) mean=avg;

run;

This will also give the same result but it has an advantage that it is resource efficient relative to where & nway options for large datasets.