Given x and y vectors in quick variables 1 and 2 as before, the first job is to form the matrix that describes the problem.
Thus we want a @c{$19\times2$} 19x2 matrix with our x vector as one column and ones as the other column. So, first we build the column of ones, then we combine the two columns to form our A matrix.
2: [1.34, 1.41, 1.49, ... ] 1: [ [ 1.34, 1 ]
1: [1, 1, 1, ...] [ 1.41, 1 ]
. [ 1.49, 1 ]
...
r 1 1 v b 19 RET M-2 v p v t s 3
Now we compute @c{$A^T y$} trn(A) * y and @c{$A^T A$} trn(A) * A and divide.
1: [33.36554, 13.613] 2: [33.36554, 13.613]
. 1: [ [ 98.0003, 41.63 ]
[ 41.63, 19 ] ]
.
v t r 2 * r 3 v t r 3 *
(Hey, those numbers look familiar!)
1: [0.52141679, -0.425978]
.
/
Since we were solving equations of the form @c{$m \times x + b \times 1 = y$} m*x + b*1 = y, these numbers should be m and b, respectively. Sure enough, they agree exactly with the result computed using V M and V R!
The moral of this story: V M and V R will probably solve your problem, but there is often an easier way using the higher-level arithmetic functions!
In fact, there is a built-in a F command that does least-squares fits. See section Curve Fitting.