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Normal distribution, also known as the Gaussian distribution, is a probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean. In graph form, normal distribution will appear as a bell curve.
It is also called Gaussian distribution because it was discovered by Carl Friedrich Gauss. The normal distribution is a continuous probability distribution. It is very important in many fields of science. ... It is often called the bell curve because the graph of its probability density looks like a bell.
The Gaussian distribution is a continuous function which approximates the exact binomial distribution of events. ... The standard deviation expression used is also that of the binomial distribution. The Gaussian distribution is also commonly called the "normal distribution" and is often described as a "bell-shaped curve".
In probability theory, the normal (or Gaussian or Gauss or LaplaceGauss) distribution is a very common continuous probability distribution. Normal distributions are important in statistics and are often used in the natural and social sciences to represent real-valued random variables whose distributions are not known.
Gaussian distribution (also known as normal distribution) is a bell-shaped curve, and it is assumed that during any measurement values will follow a normal distribution with an equal number of measurements above and below the mean value.
Gaussian units constitute a metric system of physical units. This system is the most common of the several electromagnetic unit systems based on cgs (centimetregramsecond) units. It is also called the Gaussian unit system, Gaussian-cgs units, or often just cgs units.
Initially the normal distribution was called as "Gaussian distribution" after the famous mathematician GAUSS. The name normal was attributed later due to great statistician Karl E.
Independently, the mathematicians Adrain in 1808 and Gauss in 1809 developed the formula for the normal distribution and showed that errors were fit well by this distribution.
Normal distribution, also known as the Gaussian distribution, is a probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean. In graph form, normal distribution will appear as a bell curve.
Central limit theorem states that when we add large number of independent random variables, irrespective of the original distribution of these variables, their normalized sum tends towards a Gaussian distribution. ... The theorem can also been seen as a explanation why many natural phenomena follow Gaussian distribution.
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