def normal_lower_bound(probability, mu=0, sigma=1):
"""returns the z for which P(Z >= z) = probability"""
return inverse_normal_cdf(1 - probability, mu, sigma)
def normal_lower_bound(probability, mu=0, sigma=1):
"""returns the z for which P(Z >= z) = probability"""
return inverse_normal_cdf(1 - probability, mu, sigma)
from buckets import *
import random
import sys
sys.path.insert(0, '../probability')
from distributions import inverse_normal_cdf
"""
Knowing the max, min, mean and standard deviation is often not enough to show
distinct characteristics of data.
These two sets of data have means close to 0 and standard deviations close to
58. However, they have very different distributions
"""
random.seed(0)
# uniform between -100 and 100
uniform = [200 * random.random() - 100 for _ in range(1000)]
# normal distribution with mean 0, standard deviation 57
normal = [57 * inverse_normal_cdf(random.random())
for _ in range(10000)]
plot_histogram(uniform, 10, "Uniform Histogram")
plot_histogram(normal, 10, "Normal histogram")
from buckets import * import random import sys sys.path.insert(0, '../probability') from distributions import inverse_normal_cdf """ Knowing the max, min, mean and standard deviation is often not enough to show distinct characteristics of data. These two sets of data have means close to 0 and standard deviations close to 58. However, they have very different distributions """ random.seed(0) # uniform between -100 and 100 uniform = [200 * random.random() - 100 for _ in range(1000)] # normal distribution with mean 0, standard deviation 57 normal = [57 * inverse_normal_cdf(random.random()) for _ in range(10000)] plot_histogram(uniform, 10, "Uniform Histogram") plot_histogram(normal, 10, "Normal histogram")