1.The way to calculate the slope: the covariance of x and y divided by the variance of x
from numpy import cov
slope_density = cov(wine_quality["quality"],wine_quality["density"])[0,1]/wine_quality["density"].var() #cov(x,y) is the function from numpy, which returns a 2*2 metric,.var() is pandas function.
2.To get the intercept: b = y - ax( x and y are the mean value of each column)
intercept_density = wine_quality["quality"].mean() - wine_quality["density"].mean() * (calc_slope(wine_quality["density"],wine_quality["quality"]))
Statistics and Linear Algebra 4
原文：http://www.cnblogs.com/kingoscar/p/6124330.html