From plotting you data (Wolfram|Alpha link), it does not look linear. So it better be fit by a polynomial. I assume you want to fit the data:
X Y
1 4
2 8
3 13
4 18
5 24
..
using a quadratic polynomial $y = ax^2 + bx + c.$ If so, then put your data in a matrix form (note that $x^0, x^1, x^2, y$ below are not actually in the matrix. They're just comments for your understanding):
$$
\begin{pmatrix}
\color{red}{x^0} & {\color{red} x} & \color{red}{x^2} \\
1 & 1 & 1 \\
1 & 2 & 4 \\
1 & 3 & 9 \\
& \ldots &
\end{pmatrix}
\begin{pmatrix}
c \\ b \\ a
\end{pmatrix} =
\begin{pmatrix}
{\color{red} y} \\ 4 \\ 8 \\ 13 \\ \ldots
\end{pmatrix} \tag{1}
$$
Now, equation $(1)$ is really of the following form:
$$ Xv = y \tag{2}$$
where each row encodes $cx^0 + bx+ax^2 = y$ for a particular pair of $(x,y)$ values. And we're looking for a solution vector $v^{T} = \begin{pmatrix}
c & b & a
\end{pmatrix}$ that gives the coefficients of that best fitting polynomial $ax^2 + bx+c$.
To solve for $v$, multiply $(2)$ both sides by $X^{T},$ we have
$X^{T}Xv= X^{T}y,$ or
$$ v = (X^{T}X)^{-1} X^{T}y.$$
This is a called linear least squares method because best means minimize the squared error. Several software packages can handle that for you. Luckily, Wolfram|Alpha can do.
To repeat for a polynomial of degree, say 4, construct
$$
\begin{pmatrix}
\color{red}{x^0} & {\color{red} x} & \color{red}{x^2} & \color{red}{x^3} & \color{red}{x^4} \\
1 & 1 & 1 & 1 & 1\\
1 & 2 & 4 & 8 & 16 \\
1 & 3 & 9 & 27 & 81 \\
& \ldots &
\end{pmatrix}
\begin{pmatrix}
e \\ d\\ c \\ b \\ a
\end{pmatrix} =
\begin{pmatrix}
{\color{red} y} \\ 4 \\ 8 \\ 13 \\ \ldots
\end{pmatrix} \tag{1}
$$
and solve for $v$, this should give you the parameters $a,b,c,d,e$ s.t. $$ax^4+bx^3+cx^2+dx+e$$ best fits your data.
fit int <numbers>
did not work. – Kim Mar 18 '12 at 15:35