# POISSON

### Definition of POISSON

Returns the value of the Poisson distribution function (or Poisson cumulative distribution function) for a specified value and mean.

### Sample Usage

`POISSON(2.4,1,FALSE)`

`POISSON(A2,A3,TRUE)`

### Syntax

`POISSON(x, mean, cumulative)`

• `x` - The input to the Poisson distribution function.

• `mean` - The mean (mu) of the Poisson distribution function.

• `cumulative` - Whether to use the Poisson cumulative distribution function rather than the distribution function..

### Notes

• The Poisson distribution function is typically used to calculate the number of 'arrivals' or 'events' over a period of time, such as the number of network packets or login attempts given some mean.

• If `cumulative` is `TRUE` then `POISSON` returns the probability of `x` or fewer events, otherwise the probability of exactly `x` events.

`WEIBULL`: Returns the value of the Weibull distribution function (or Weibull cumulative distribution function) for a specified shape and scale.

`NORMSINV`: Returns the value of the inverse standard normal distribution function for a specified value.

`NORMSDIST`: Returns the value of the standard normal cumulative distribution function for a specified value.

`NORMINV`: Returns the value of the inverse normal distribution function for a specified value, mean, and standard deviation.

`NORMDIST`: Returns the value of the normal distribution function (or normal cumulative distribution function) for a specified value, mean, and standard deviation.

`NEGBINOMDIST`: Calculates the probability of drawing a certain number of failures before a certain number of successes given a probability of success in independent trials.

`LOGNORMDIST`: Returns the value of the log-normal cumulative distribution with given mean and standard deviation at a specified value.

`LOGINV`: Returns the value of the inverse log-normal cumulative distribution with given mean and standard deviation at a specified value.

`EXPONDIST`: Returns the value of the exponential distribution function with a specified lambda at a specified value.

`BINOMDIST`: Calculates the probability of drawing a certain number of successes (or a maximum number of successes) in a certain number of tries given a population of a certain size containing a certain number of successes, with replacement of draws.

### Excellentable will generate the outcome when hitting enter.

A
B
C
1
State
No of Events
Expected Mean
2
Alabama
13
4
3
12
3
4
Arizona
2
5
5
Arkansas
8
10
6
California
4
2
7
3
9
8
Connecticut
2
1
9
Delaware
4
7
10
Florida
1
8
11
Georgia
1
40
12
Hawaii
1
10
13
14
0.124652019
D
1
Data
2
1
3
2
4
3
5
6
6
6
7
6
8
134
9
200
10
9
11
1.5
12
0.25
13
14

arrivals, x

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