`CMAX(M)`
`CSUM(M)`
`CSSQ(M)`
+ `MEAN(M)`
Returns a row vector with the same number of columns as `M`, in
- which each element is the minimum, maximum, sum, or sum of
- squares, respectively, of the elements in the same column of `M`.
+ which each element is the minimum, maximum, sum, sum of squares,
+ or mean, respectively, of the elements in the same column of `M`.
```
- CMIN({1, 2, 3; 4, 5, 6; 7, 8, 9} ⇒ {1, 2, 3}
- CMAX({1, 2, 3; 4, 5, 6; 7, 8, 9} ⇒ {7, 8, 9}
- CSUM({1, 2, 3; 4, 5, 6; 7, 8, 9} ⇒ {12, 15, 18}
- CSSQ({1, 2, 3; 4, 5, 6; 7, 8, 9} ⇒ {66, 93, 126}
+ CMIN({1, 2, 3; 4, 5, 6; 7, 8, 9}) ⇒ {1, 2, 3}
+ CMAX({1, 2, 3; 4, 5, 6; 7, 8, 9}) ⇒ {7, 8, 9}
+ CSUM({1, 2, 3; 4, 5, 6; 7, 8, 9}) ⇒ {12, 15, 18}
+ CSSQ({1, 2, 3; 4, 5, 6; 7, 8, 9}) ⇒ {66, 93, 126}
+ MEAN({1, 2, 3; 4, 5, 6; 7, 8, 9}) ⇒ {4, 5, 6}
```
+ `MEAN` is a PSPP extension.
+
* `MMIN(M)`
`MMAX(M)`
`MSUM(M)`
of the elements of `M`.
```
- MMIN({1, 2, 3; 4, 5, 6; 7, 8, 9} ⇒ 1
- MMAX({1, 2, 3; 4, 5, 6; 7, 8, 9} ⇒ 9
- MSUM({1, 2, 3; 4, 5, 6; 7, 8, 9} ⇒ 45
- MSSQ({1, 2, 3; 4, 5, 6; 7, 8, 9} ⇒ 285
+ MMIN({1, 2, 3; 4, 5, 6; 7, 8, 9}) ⇒ 1
+ MMAX({1, 2, 3; 4, 5, 6; 7, 8, 9}) ⇒ 9
+ MSUM({1, 2, 3; 4, 5, 6; 7, 8, 9}) ⇒ 45
+ MSSQ({1, 2, 3; 4, 5, 6; 7, 8, 9}) ⇒ 285
```
* `RMIN(M)`
squares, respectively, of the elements in the same row of `M`.
```
- RMIN({1, 2, 3; 4, 5, 6; 7, 8, 9} ⇒ {1; 4; 7}
- RMAX({1, 2, 3; 4, 5, 6; 7, 8, 9} ⇒ {3; 6; 9}
- RSUM({1, 2, 3; 4, 5, 6; 7, 8, 9} ⇒ {6; 15; 24}
- RSSQ({1, 2, 3; 4, 5, 6; 7, 8, 9} ⇒ {14; 77; 194}
+ RMIN({1, 2, 3; 4, 5, 6; 7, 8, 9}) ⇒ {1; 4; 7}
+ RMAX({1, 2, 3; 4, 5, 6; 7, 8, 9}) ⇒ {3; 6; 9}
+ RSUM({1, 2, 3; 4, 5, 6; 7, 8, 9}) ⇒ {6; 15; 24}
+ RSSQ({1, 2, 3; 4, 5, 6; 7, 8, 9}) ⇒ {14; 77; 194}
```
* `SSCP(M)`
TRACE(MDIAG(1:5)) ⇒ 15
```
+* `COV(M)`
+ Returns a symmetric matrix whose diagonal elements are the variance
+ of the each column in `M` and whose off-diagonal elements are the
+ covariance of the two columns of the element's row and column. For
+ example, if `M` is a `N`×2 matrix, the result is a 2×2 matrix `{a,
+ b; b, c}`, where `a` is the variance of `M`'s first column, `b` is
+ the covariance between `M`'s two columns, and `c` is the variance
+ of `M`'s second column.
+
+ ```
+ COV({80, 70; 63, 20; 100, 50})} ⇒ {343, 260; 260, 633.33}
+ ```
+
+ `COV` is a PSPP extension.
+
### Matrix Property Functions
* `NROW(M)`