POISSON Excel Formula
Microsoft Excel has been part of our daily computation activities from simple to complex calculations. It is composed of different functions that are very helpful in terms of calculations in various aspects. These functions are still divided into groups like the Statistical Function has the subgroups of finding the largest and the smallest value, counting cells, distribution and tests of probability, averages, frequency and rank, deviation and variance, and trend line functions. One function in Excel is POISSON function which allows the user to predict how many events there are for a specific value in a given exact time.
The syntax or the series of arguments of POISSON function include:
=POISSON(x,mean,cumulative)
…whereas the argument X in the syntax refers to how many events there is. The argument Mean refers to the expected value of numbers of event. Lastly, the Cumulative argument refers to what kid of distributions to be computed. If the cumulative argument is TRUE you will have to use cumulative distributions function. The count of the random events that will occur is between the number 0 and X wherein the values are still included. If it is false probability mass functions is used wherein the the exact numbers of event that will occur is X.
In case #VALUE! errors will occur this means that supplied X arguments or mean argument are a non-numeric value. Moreover, #NUM! errors will occur if the given X argument is lesser than 0 and if Mean argument also is lesser than 0. Also, if the argument X isn’t a number it would be automatically truncated.
Try POISSON function by opening first an Excel Spreadsheet or worksheet and input cell A1 the number 2 and in cell A2 the number 5. The formula for the argument is as follow; =POISSON (A2, A3, TRUE) and the results will be 0.124652 which is Cumulative Poisson of the given value. For Poisson possibility smass functions with formula =POISSON (A2, A3, FALSE) and the result will be 0.084224. You can also insert the values in formula as follow; =POISSON (20, 25, FALSE) and the result will be 0.051917469.
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