mytest > help > Part 2. MM4XL Tools > 1. Strategic Tools > Risk Analyst > 4. Functions > 3. Distribution Functions > mmNORMAL(Mean, StDeviation)

Risk Analyst

mmNORMAL(Mean, StDeviation)

Example

=mmNORMAL(0, 1) can equal -1.277808189.

Application

Sometimes called the Gaussian distribution, this function is used to draw normally distributed values around the mean according to the standard deviation. It has a very wide range of applications, because there are many variables that fall into a Normal distribution. These include such things as human height, the weight of a pack of biscuits, intelligence scores, or the world temperature.

It is also frequently used to represent phenomena whose distribution is not known, but is thought to be symmetrical to a given mean value.

How to use

This function returns a normally distributed value around the mean. This helps, for instance, to model the growth of a given market for successive years. Say that we are looking into the profitability of a new product launch and we need to estimate the market size for 5 consecutive years. The value of the market at year zero is estimated in 1 million and will grow at a rate of around 5% a year. The formula below helps to model this case:

=mmNORMAL(1.05, 0.01) * 1000000

Copy the mmNORMAL formula above in 100 cells. You will find that it produces values in the range 1.01-1.08, which roughly correspond to 1.05 plus or minus 3 standard deviations. The mean of a Normal distribution plus or minus 3 standard deviations is the range supposed to host 99.7% of the values. The value obtained with mmNORMAL times the market value results in an estimate of next years market value.

Technical profile

Type Continuous distribution.
Syntax =mmNORMAL(Mean, StDeviation)
Domain  Monte Carlo Simulation Software: Management Process Risk Analysis
Mode Mean.
Parameters Mean = mu
StDeviation > 0
Remarks If any argument is nonnumeric mmNORMAL returns the #VALUE! error value. If StDeviation < 0 mmNORMAL returns the #NUM! error value.
Relationships It is related to the Beta, Binomial, Chi2, Gamma, Inverse Gauss, LogNormal, Poisson and Students t variate.
Graphs
mmNORMAL(0, 1) mmNORMAL(10, 5)
 Monte Carlo Simulation Software: Management Process Risk Analysis  Monte Carlo Simulation Software: Management Process Risk Analysis
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