Linear regression in NumPy

[nikie]

I'm a little bit stuck with NumPy here, and neither the docs nor
trial&error seems to lead me anywhere:
I've got a set of data points (x/y-coordinates) and want to fit a
straight line through them, using LMSE linear regression. Simple
enough. I thought instead of looking up the formulas I'd just see if
there isn't a NumPy function that does exactly this. What I found was
"linear_least_squares", but I can't figure out what kind of parameters
it expects: I tried passing it my array of X-coordinates and the array
of Y-coordinates, but it complains that the first parameter should be
two-dimensional. But well, my data is 1d. I guess I could pack the X/Y
coordinates into one 2d-array, but then, what do I do with the second
parameter?

Mor generally: Is there any kind of documentation that tells me what
the functions in NumPy do, and what parameters they expect, how to call
them, etc. All I found was:
"This function returns the least-squares solution of an overdetermined
system of linear equations. An optional third argument indicates the
cutoff for the range of singular values (defaults to 10-10). There are
four return values: the least-squares solution itself, the sum of the
squared residuals (i.e. the quantity minimized by the solution), the
rank of the matrix a, and the singular values of a in descending
order."
It doesn't even mention what the parameters "a" and "b" are for...