Standard Normal Distribution (Gaussian distribution) 1/2
What is it?
This is a continuous probability distribution for a real-valued random variable.
Key characteristics:
- The mean value or expectation is equal to 0.
- The standard deviation to 1.
- The shape is bell-curved.
- The distribution is symmetrical. Python realization:
We will generate standard normal distribution with the size 1000 and mean and standard deviation specific to the standard normal distribution. We use the function random.normal()
from the numpy
library with the parameters: loc
is the mean value and scale
is the standard deviation.
You can play with the distribution size and see how the distribution will be modified.
123456789import numpy as np import matplotlib.pyplot as plt import seaborn as sns # Generate standard normal distribution with the size 1000 data = np.random.normal(loc = 0, scale = 1, size = 1000) sns.histplot(data = data, kde = True) plt.show()
Дякуємо за ваш відгук!
single
Запитати АІ
Запитати АІ
Запитайте про що завгодно або спробуйте одне із запропонованих запитань, щоб почати наш чат
Awesome!
Completion rate improved to 3.7
Standard Normal Distribution (Gaussian distribution) 1/2
Свайпніть щоб показати меню
What is it?
This is a continuous probability distribution for a real-valued random variable.
Key characteristics:
- The mean value or expectation is equal to 0.
- The standard deviation to 1.
- The shape is bell-curved.
- The distribution is symmetrical. Python realization:
We will generate standard normal distribution with the size 1000 and mean and standard deviation specific to the standard normal distribution. We use the function random.normal()
from the numpy
library with the parameters: loc
is the mean value and scale
is the standard deviation.
You can play with the distribution size and see how the distribution will be modified.
123456789import numpy as np import matplotlib.pyplot as plt import seaborn as sns # Generate standard normal distribution with the size 1000 data = np.random.normal(loc = 0, scale = 1, size = 1000) sns.histplot(data = data, kde = True) plt.show()
Дякуємо за ваш відгук!
Awesome!
Completion rate improved to 3.7single