The equation for my line is \( y=a(x-x_0)+y_0 \) so that at \(x=x_0, y=y_0\) as required.

I can find the slope if the line equation is the standard \( y=ax+b \) using the formula given here (for \(\hat{\beta}\)) http://en.wikipedia.org/wiki/Simple_linear_regression but I am unsure how to go through the same steps for the form of \( y \) which I require.

From that wiki page, the slope of the standard \(y=\hat\beta x+\alpha \) is \(\hat\beta = \frac{ \sum_{i=1}^{n} (x_{i}-\bar{x})(y_{i}-\bar{y}) }{ \sum_{i=1}^{n} (x_{i}-\bar{x})^2 }\) where \(\bar{x} \mbox{ and } \bar{y}\) are the mean of all \(x_i \mbox{ and } y_i\)

I'm hoping to find a similar equation for my form of \(y\) (\(y=a(x-x_0)+y_0\)) where \((x_0,y_0)\) is the coordinate which the best fit line must pass through.

Thanks for any help, Tim