def covariance(x, y): """ Whereas variance measures how a single variable deviates from its mean, covariance measures how two variables vary in tandem from their means. A "large" positive covariance means that x tends to be large when y is large and small when y is small. A "large" negative covariance means the opposite - that x tends to be small when y is large and vice versa. A covariance close to zero means no such relationship exists. """ n = len(x) return dot(de_mean(x), de_mean(y)) / (n - 1)
def total_sum_of_squares(y): """the total squared variation of y_i's from their mean""" return sum(v ** 2 for v in de_mean(y))
def covariance(x, y): n = len(x) return dot(de_mean(x), de_mean(y)) / (n - 1)
def total_sum_of_squares(y): """the total squared variation of y_i's from their mean""" return sum(v**2 for v in de_mean(y))