Define the following term risk probability distrituion

Some distributions have an additional вЂњlocationвЂќ parameter added when they are specified using alternate parameters. The standard parameter names for each distribution can be found in the heading for each function in this documentation, or in the Excel Insert Function wizard. For example, the following formula specifies a normal distribution with mean 100 and a 95th percentile of 132.89:

This allows percentiles to be mixed with standard distribution arguments. If the type of parameter argument is a label in quotes (such as вЂњmuвЂќ), the parameter is the standard distribution argument with that name.

For example, the following function specifies a normal distribution with a 5th percentile of 67.10 and a 95th percentile of 132.89:ДЄlternate parameters can be either percentiles or standard distribution arguments (or a combination). The first specifies the type of the parameter, while the second is its value. Each parameter of an alternate parameter distribution function requires two arguments. The Alt (or AltD) versions of distribution functions provide this functionality. For example, it might be more natural to assess the 10th and 90th percentiles a normal distribution instead of the usual mean and standard deviation. refers to this as an вЂњalternate parameterвЂќ version of the distribution. Many distribution functions can be specified with percentile values instead of their more traditional parameters. =100+RiskUniform(10,20)+(1.5*RiskNormal(A1,A2)) Alternate Distribution Functions It is possible to construct complicated formulas that combine multiple distributions. This specifies a triangular distribution with a minimum value obtained from cell B1, a most likely value 1.5 times the value in cell B2, and a maximum value obtained from cell B3. Like most Excel functions, distribution functions can have arguments that reference cells or expressions, such as Specifies that during a simulation, the cell that contains it will generate random uniformly distributed samples between 10 and 20. For example, if the average number of people whoВ buy cheeseburgers from a fast-food chain on a Friday night at a single restaurant location is 200, a Poisson distribution can answer questions such as, 'What is the probability that more than 300 people will buy burgers?' The application of the Poisson distribution thereby enables managers to introduce optimal scheduling systems that would not work with, say, a normal distribution.Probability distribution functions are used for adding uncertainty to cells and equations in a spreadsheet model.

Many economic and financial data appear as count variables, such as how many times a person becomes unemployed in a given year, thus lending themselves to analysis with a Poisson distribution.ДЄ Poisson distribution can be used to estimate how likely it is that something will happen 'X' number of times.

Poisson distributions are used when the variable of interest is a discrete count variable.

A Poisson distribution, named after French mathematician SimГ©on DenisВ Poisson, can be used to estimate how many times an event is likely to occur within 'X' periods of time.

Define the following term risk probability distrituion