Contenido del Curso
Probability Theory Update
Probability Theory Update
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().
import 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:
import 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 escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones¡Gracias por tus comentarios!
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().
import 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:
import 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 escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones¡Gracias por tus comentarios!
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().
import 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:
import 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 escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones¡Gracias por tus comentarios!
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().
import 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:
import 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 escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opcionesCambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones