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, "%")
¡Gracias por tus comentarios!
single
Pregunte a AI
Pregunte a AI
Pregunte lo que quiera o pruebe una de las preguntas sugeridas para comenzar nuestra charla
Resumir este capítulo
Explicar el código en file
Explicar por qué file no resuelve la tarea
Awesome!
Completion rate improved to 3.7
Poisson Distribution 2/3
Desliza para mostrar el menú
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, "%")
¡Gracias por tus comentarios!
Awesome!
Completion rate improved to 3.7single