# Gaussian Distribution

The **Gaussian distribution**, also known as the **normal distribution**, is a continuous probability distribution widely used in statistics and probability theory.

## Gaussian distribution applications

We can use this distribution to describe the following values:

**Physical Measurements**: Many physical measurements, such as height, weight, blood pressure, and body temperature, can be reasonably approximated by a Gaussian distribution. For example, the height of adult men or women in a population often follows a Gaussian distribution;**Errors and Residuals**: In statistical analysis or regression modeling, errors or residuals (the difference between observed and predicted values) are usually assumed to be normally distributed;**Test Scores**: Standardized test scores such as the SAT or ACT are often modeled using a Gaussian distribution in educational testing;**Environmental measurements**: A Gaussian distribution can often describe variables such as air pollution, noise levels, and water quality measurements.

## Python implementation

We can also use `.cdf()`

method of `scipy.stats.norm`

class to work with Gaussian distribution in Python. It has two main parameters: `loc`

determines the mean value of the experiment's result, and `scale`

determines the average deviation from the mean.

### Example

Calculate the probability that the height of a randomly chosen man will be less than `160`

or more than `190`

. Assume that the mean value of men's height is `170,`

and the average deviation is `20`

.

The Gaussian distribution is one of the most popular and commonly used distributions. Its properties are discussed in more detail in the Probability Theory Mastering course.

Everything was clear?

Course Content

Probability Theory Basics

## Probability Theory Basics

5. Covariance and Correlation

# Gaussian Distribution

The **Gaussian distribution**, also known as the **normal distribution**, is a continuous probability distribution widely used in statistics and probability theory.

## Gaussian distribution applications

We can use this distribution to describe the following values:

**Physical Measurements**: Many physical measurements, such as height, weight, blood pressure, and body temperature, can be reasonably approximated by a Gaussian distribution. For example, the height of adult men or women in a population often follows a Gaussian distribution;**Errors and Residuals**: In statistical analysis or regression modeling, errors or residuals (the difference between observed and predicted values) are usually assumed to be normally distributed;**Test Scores**: Standardized test scores such as the SAT or ACT are often modeled using a Gaussian distribution in educational testing;**Environmental measurements**: A Gaussian distribution can often describe variables such as air pollution, noise levels, and water quality measurements.

## Python implementation

We can also use `.cdf()`

method of `scipy.stats.norm`

class to work with Gaussian distribution in Python. It has two main parameters: `loc`

determines the mean value of the experiment's result, and `scale`

determines the average deviation from the mean.

### Example

Calculate the probability that the height of a randomly chosen man will be less than `160`

or more than `190`

. Assume that the mean value of men's height is `170,`

and the average deviation is `20`

.

The Gaussian distribution is one of the most popular and commonly used distributions. Its properties are discussed in more detail in the Probability Theory Mastering course.

Everything was clear?