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 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))
