In my Physics intro/data analysis lecture, theres some mention of "Linear least squares fits":
Gradient
$m = \frac{1}{\Delta} (N \sum x_i y_i - \sum x_i \sum y_i)$
$\sigma_m = \sqrt{N \frac{\sigma^2 y}{\Delta}}$
y-intercept
$c = \frac{1}{\Delta} (\sum x_{i}^2 \sum y_i - \sum x_i \sum x_i > y_i$
$\sigma \sqrt{\frac{\sigma^2 y}{\Delta} \sum x_{i}^{2}}$
Uncertainty for measured y
$\sigma_y = \sqrt{\frac{1}{N-2} \sum (y_i - mx_i -c)^2}$
$\Delta = N \sum x_{i}^{2} - (\sum x_i)^2$
Coefficient of determination
$r^2 = \frac{(N \sum x_i y_i - \sum x_i \sum y_i)^2}{(N \sum > x_{i}^{2} - (\sum x_i)^2) (N \sum y_i^2 - (\sum y_i)^2)}$
- $r^2 = 0$: no corelation
- $r^2 = 1$: perfect correlation
Main question is whats $\Delta$.
And if its simple, it'll be good to have a little understanding about how the formulas are defined. Just something very rough or a simple explaination