Poisson Distribution 2/3
As you remember, with the .pmf() function, we can calculate the probability over a range using the addition rule. Look at the example:
Example 1/2: We know that per day the expected value of users is 100. Calculate the probability that 110 users will visit the app.
This distribution is discrete, so to calculate the probability of getting the exact number of customers, we can use the .pmf() function with two parameters: the first is our desored number of events, and the second is lambda.
Python realization:
We will use .pmf() function for the Poisson distribution using stats.poisson.pmf().
123import scipy.stats as stats probability = stats.poisson.pmf(110, 100) print("The probability is", probability * 100, "%")
Example 2/2:
The expected value of sunny days per month is 15. Calculate the probability that the number of sunny days will equal 16, 17, 18, or 19.
Python realization:
12345678910import scipy.stats as stats prob_1 = stats.poisson.pmf(16, 15) prob_2 = stats.poisson.pmf(17, 15) prob_3 = stats.poisson.pmf(18, 15) prob_4 = stats.poisson.pmf(19, 15) probability = prob_1 + prob_2 + prob_3 + prob_4 print("The probability is", probability * 100, "%")
Thanks for your feedback!
single
Ask AI
Ask AI
Ask anything or try one of the suggested questions to begin our chat
Summarize this chapter
Explain the code in file
Explain why file doesn't solve the task
Awesome!
Completion rate improved to 3.7Poisson Distribution 2/3
Swipe to show menu
As you remember, with the .pmf() function, we can calculate the probability over a range using the addition rule. Look at the example:
Example 1/2: We know that per day the expected value of users is 100. Calculate the probability that 110 users will visit the app.
This distribution is discrete, so to calculate the probability of getting the exact number of customers, we can use the .pmf() function with two parameters: the first is our desored number of events, and the second is lambda.
Python realization:
We will use .pmf() function for the Poisson distribution using stats.poisson.pmf().
123import scipy.stats as stats probability = stats.poisson.pmf(110, 100) print("The probability is", probability * 100, "%")
Example 2/2:
The expected value of sunny days per month is 15. Calculate the probability that the number of sunny days will equal 16, 17, 18, or 19.
Python realization:
12345678910import scipy.stats as stats prob_1 = stats.poisson.pmf(16, 15) prob_2 = stats.poisson.pmf(17, 15) prob_3 = stats.poisson.pmf(18, 15) prob_4 = stats.poisson.pmf(19, 15) probability = prob_1 + prob_2 + prob_3 + prob_4 print("The probability is", probability * 100, "%")
Switch to desktop for real-world practiceContinue from where you are using one of the options belowThanks for your feedback!
Awesome!
Completion rate improved to 3.7single
