POWER BI: Modeling

Statistical Functions

Poission function

Returns the poisson distrubtion used for predicting the number of events that will occur within a specified time interval. For example, the number of car accidents at a large intersection during rush hour or the number of calls recieved by a call center during lunch hour.

Syntax:

Example 1:

The average number of accidents at a large intersection per month follows a poisson distribution with a mean of 5. Find the probability that next month there will be exactly 2 accidents.

Answer:

In the Power BI formula bar define the following measures:

                      Poisson2 = POISSON.DIST(2,5,False)
	             

Returns:

• ${\dfrac{e^{-\lambda}{\lambda^{n}}}{n!}} = {\dfrac{e^{-5}{5^{2}}}{2!}} = 0.0842 $

Example 2:

The average number of accidents at a large intersection per month follows a poisson distribution with a mean of 5. Find the probability that next month there will be 2 accidents or less.

Answer:

In the Power BI formula bar define the following measures:

                        Poisson2 = POISSON.DIST(2,5,True)
	             

Returns:

• ${\sum_{k=0}^{n}\dfrac{e^{-\lambda}{\lambda^{n}}}{n!}}$


• $ = {\sum_{k=0}^{2}\dfrac{e^{-5}{5^{2}}}{2!}}$
• $ = {\dfrac{e^{-5}{5^{0}}}{0!}} + {\dfrac{e^{-5}{5^{1}}}{1!}} + {\dfrac{e^{-5}{5^{2}}}{2!}}$


• $ = {0.125}$

Power BI Demo: