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, "%")
Grazie per i tuoi commenti!
single
Chieda ad AI
Chieda ad AI
Chieda pure quello che desidera o provi una delle domande suggerite per iniziare la nostra conversazione
Riassuma questo capitolo
Explain code
Explain why doesn't solve task
Awesome!
Completion rate improved to 3.7Poisson Distribution 2/3
Scorri per mostrare il 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, "%")
Cambia al desktop per esercitarti nel mondo realeContinua da dove ti trovi utilizzando una delle opzioni seguentiGrazie per i tuoi commenti!
Awesome!
Completion rate improved to 3.7single
