The fastest and the most efficient way would be to use a native scipy function from linregress which calculates everything:
slope : slope of the regression line
intercept : intercept of the regression line
r-value : correlation coefficient
p-value : two-sided p-value for a hypothesis test whose null hypothesis is that the slope is zero
stderr : Standard error of the estimate
And here is an example:
a = [15, 12, 8, 8, 7, 7, 7, 6, 5, 3]
b = [10, 25, 17, 11, 13, 17, 20, 13, 9, 15]
from scipy.stats import linregress
linregress(a, b)
will return you:
LinregressResult(slope=0.20833333333333337, intercept=13.375, rvalue=0.14499815458068521, pvalue=0.68940144811669501, stderr=0.50261704627083648)
P.S. Just a mathematical formula for slope:
