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()
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Completion rate improved to 3.7Standard Normal Distribution (Gaussian distribution) 1/2
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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()
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Completion rate improved to 3.7single
