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Lære Standard Normal Distribution (Gaussian distribution) 1/2 | Distributions
Probability Theory Update
course content

Kursusindhold

Probability Theory Update

Probability Theory Update

1. Probability Basics
2. Statistical Dependence
3. Learn Crucial Terms
4. Probability Functions
5. Distributions

book
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.

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import 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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Sektion 5. Kapitel 4
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book
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.

123456789
import 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()
copy

Switch to desktopSkift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder
Var alt klart?

Hvordan kan vi forbedre det?

Tak for dine kommentarer!

Sektion 5. Kapitel 4
Switch to desktopSkift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder
Vi beklager, at noget gik galt. Hvad skete der?
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