It is called the

standard normal distribution if [mu] = 0 and [sigma] = 1.

It can be achieved by two steps: the first step is to generate the

standard normal distribution series by exchanging the random variables of sobol sequence which obeys the U (0, 1) distribution; the second step is to exchange the

standard normal distribution into the corresponding normal distribution based on the mean and variance of necessary

standard normal distribution series.

Nowadays, available statistical software products are capable of producing many important characteristics of the

standard normal distribution with higher accuracy than ever before, eliminating the need for the use of tabulated values.

where [phi] is the cumulative

standard normal distribution,

p] is the upper-side factor of

standard normal distribution, and G is the standard deviation.

We then can take a critical value from the

standard normal distribution at a 10 % significance level to determine if a particular model performs significantly better in explaining the data than another.

Figure 1 illustrates a

standard normal distribution with a mean of zero and a standard deviation of one.

it cannot be exactly defined in advance, thus the development of the actual utilization demand and of stocks can be determined only with random variables that are represented on the figure by the probability density functions of the

standard normal distribution.

For example, SAS can use the

standard normal distribution with a seed.

The mathematician Gauss defined the

standard normal distribution as

The probit model utilizes the

standard normal distribution in developing probabilities and is the additional method utilized in this analysis.

PHI](*) and [phi](*) denote the cumulative distribution function (CDF) and probability distribution function (PDF), respectively, of a

standard normal distribution.