How do I find the formula when I only know some data points ?
Usually I just use the Trendline option for diagrams in Excel, but this one eludes me.
I expect it to be something like : Ax^2 + or - Bx + or - C.
Sample data:
X Y
1 4
2 8
3 13
4 18
5 24
6 30
7 37
8 44
9 51
10 60
11 68
12 78
13 88
14 99
15 110
16 122
17 136
18 150
19 166
20 180
21 197
22 216
23 235
24 255
25 277
26 300
27 325
28 351
29 378
30 408
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:
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.