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 ?
alpha]/2] is the upper [alpha]/2 quantile of the standard normal distribution.
The Y axis of cumulative standard normal distribution is divided into two parts by Moro algorithm, and then takes two corresponding algorithms for processing.
where t = ln L - [mu] / [sigma] and [PHI](t) is the cdf of the standard normal distribution.
n] = [mu]), the probability of the occurrence of a shortage in case of a demand with standard normal distribution will correspond to the probability of the non -occurrence of the shortage (figure 6).
Rather than treating the slope parameters in a linear fashion, the marginal effect of each explanatory variable can be calculated using the cumulative standard normal distribution in the case of the probit model or the cumulative exponential function for logit analysis.
1-[alpha]/2] is the (1-[alpha]/2)th quantile of the standard normal distribution.
The distribution based on the simulated data agreed quite well with the expected standard normal distribution.
State the test statistic to be used and its probability distribution The test statistic has a standard normal distribution
In what follows we will prove that the joint distribution of the (properly rescaled) height and area variables in this ensemble converge in distribution to the two-dimensional standard normal distribution.
Summing up the who, the distribution obtained with t = x - a/[sigma] changing variables is called standard normal distribution.
where r is the risk free interest rate, t is the time until expiration of the option, and [PHI] is the standard normal distribution function.
The critical z value is a percentile of the standard normal distribution and is obtained from a standard normal table.

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