# Normal Distribution

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## Normal Distribution

The well known bell shaped curve. According to the Central Limit Theorem, the probability density function of a large number of independent, identically distributed random numbers will approach the normal distribution. In the fractal family of distributions, the normal distribution only exists when alpha equals 2, or the Hurst exponent equals 0.50. Thus, the normal distribution is a special case which in time series analysis is quite rare. See: Alpha, Central Limit Theorem, Fractal Distribution.

## Bell Curve

A curve on a chart in which most data points cluster around the median and become less frequent the farther they fall to either side of the median. When plotted on a chart, a bell curve looks roughly like a bell.
References in periodicals archive ?
We could try more values for the skew normal distribution, for which we tried only two values in present paper.
The definition of grey degree based on normal distribution has an universal significance.
--The porosity distribution is not a large dispersion and has a normal distribution so there is no need to use the normalization methods.
To get estimates for the parameters of the normal distribution, we could use standard maximum likelihood estimators (MLEs).
(8) Under such distribution, Equations 6-10 can be expressed in the following way, using density ([f*.sub.t]) and cumulative standard normal distributions ([F*.sub.t]):
By modifying Pacey's speed normal distribution, the proposed TGMD is shown in the following equation:
Therefore we can make the assumption that this distribution is very close to normal distribution, because the average value is 382 nm.
Normal distribution with mean 25 and standard deviation 8 for sample sizes 10.
The normal distribution curve is used as a tool in measuring human capacities, pioneered by the leader Jack Welch, the former Chief Executive Officer of General Electric.
To make the overly erratic data fit into a normal distribution model, says Bar-Yam, economists and finance quants often add complicated parameters and conditions to their models, replete with confusing terminology like heteroscedasticity and kurtosis, but all that does is cover a much simpler flaw.
The central flaw lies in believing that price movements in financial products follow a normal distribution around a central mean.

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