The best fit line (using least squares regression) for the n points (X1,Y1), (X2,Y2),...(Xn,Yn) has the form: $$y = mx+b$$
Where
Slope:
$$m = \frac{n\Bigg(\displaystyle\sum_{i=1}^{n}x_iy_i\Bigg)-\Bigg(\displaystyle\sum_{i=1}^{n}x_i\Bigg)\Bigg(\displaystyle\sum_{i=1}^{n}y_i\Bigg)}{n\Bigg(\displaystyle\sum_{i=1}^{n}{x_i}^2\Bigg)-\Bigg(\displaystyle\sum_{i=1}^{n}x_i\Bigg)^2}$$
Y-Intercept:
$$b = \frac{\Bigg(\displaystyle\sum_{i=1}^{n}y_i\Bigg)-m\Bigg(\displaystyle\sum_{i=1}^{n}x_i\Bigg)}{n}$$
\(x_i\) is the time of the \(i\)th data point
\(y_i\)is the value of the \(i\)th data point
The correlation coefficient quantifies the strength of the linear relationship and is calculated by: $$r = \frac{SSXY}{\sqrt{SSX*SSY}}$$
Where $$SSX = \frac{\displaystyle\sum_{i=1}^{n}{x_i}^2}{n}-\frac{\Bigg(\displaystyle\sum_{i=1}^{n}x_i\Bigg)^2}{n^2}$$
$$SSY = \frac{\displaystyle\sum_{i=1}^{n}{y_i}^2}{n}-\frac{\Bigg(\displaystyle\sum_{i=1}^{n}y_i\Bigg)^2}{n^2}$$
$$SSXY = \frac{\displaystyle\sum_{i=1}^{n}x_iy_i}{n}-\frac{\Bigg(\displaystyle\sum_{i=1}^{n}x_i\Bigg)\Bigg(\displaystyle\sum_{i=1}^{n}y_i\Bigg)}{n^2}$$