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Statistical Distributions with NumPy
Normal Distribution, Part I

The most common distribution in statistics is known as the normal distribution, which is a symmetric, unimodal distribution.

Lots of things follow a normal distribution:

  • The heights of a large group of people
  • Blood pressure measurements for a group of healthy people
  • Errors in measurements

Normal distributions are defined by their mean and standard deviation. The mean sets the “middle” of the distribution, and the standard deviation sets the “width” of the distribution. A larger standard deviation leads to a wider distribution. A smaller standard deviation leads to a skinnier distribution.

Here are a few examples of normal distributions with different means and standard deviations:

normal_distribution

As we can see, each set of data has the same “shape”, but with slight differences depending on their mean and standard deviation.

Instructions

Examine the normal distribution displayed to your right.

Try changing the mean. What happens to the distribution?

Try increasing the standard deviation. What happens to the distribution?

When you’re done, move on to the next exercise.

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