gaussian distribution Sentence Examples

  1. In statistics, the Gaussian distribution, also known as the normal distribution, is a bell-shaped curve that represents the distribution of data.
  2. Many natural phenomena, such as human height and IQ scores, follow a Gaussian distribution.
  3. The central limit theorem states that the sum or average of a large number of independent random variables tends to follow a Gaussian distribution.
  4. Gaussian distribution is characterized by its mean and standard deviation, which determine its shape and spread.
  5. The probability density function of the Gaussian distribution is given by the famous bell-shaped curve formula.
  6. Gaussian distribution is widely used in various fields such as finance, engineering, and physics due to its mathematical properties and real-world applicability.
  7. When plotting data on a histogram, if it forms a symmetrical bell curve, it indicates that the data follows a Gaussian distribution.
  8. Gaussian distribution is essential in hypothesis testing and inferential statistics to make predictions and draw conclusions about populations.
  9. One of the assumptions in linear regression analysis is that the error terms follow a Gaussian distribution.
  10. Gaussian distribution plays a crucial role in machine learning algorithms like Gaussian Naive Bayes and Gaussian process regression for classification and regression tasks.

gaussian distribution Meaning

Wordnet

gaussian distribution (n)

a theoretical distribution with finite mean and variance

Synonyms & Antonyms of gaussian distribution

No Synonyms and anytonyms found

FAQs About the word gaussian distribution

a theoretical distribution with finite mean and variance

No synonyms found.

No antonyms found.

In statistics, the Gaussian distribution, also known as the normal distribution, is a bell-shaped curve that represents the distribution of data.

Many natural phenomena, such as human height and IQ scores, follow a Gaussian distribution.

The central limit theorem states that the sum or average of a large number of independent random variables tends to follow a Gaussian distribution.

Gaussian distribution is characterized by its mean and standard deviation, which determine its shape and spread.