Contenido del Curso
Probability Theory Update
Probability Theory Update
Poisson Distribution 3/3
As you remember, with the .cdf() function, we can calculate the probability that the random variable will take a value less then or equal a defined number. Look at the example:
Example 1/2:
The expected value of sunny days per month is 15. Calculate the probability that the number of sunny days will be less or equal 12.
Python realization:
import scipy.stats as stats probability = stats.poisson.cdf(12, 15) print("The probability is", probability * 100, "%")
Example 1/2:
The expected value of sunny days per month is 15. Calculate the probability that the number of sunny days will be less equal the number within the range from 5 to 11 (5; 11].
Python realization:
import scipy.stats as stats prob_1 = stats.poisson.cdf(11, 15) prob_2 = stats.poisson.cdf(5, 15) probability = prob_1 - prob_2 print("The probability is", probability * 100, "%")
When we subtract the second expression from the first, we leave the interval from 11 to 5 exclusive. Thus, using this calculation stats.poisson.cdf(11, 15), we will find the probability that our variable will take a value less than 11. And using this calculation stats.poisson.cdf(5, 15), we will find the probability that our variable will take a value less than or equal to 5.
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 3/3
As you remember, with the .cdf() function, we can calculate the probability that the random variable will take a value less then or equal a defined number. Look at the example:
Example 1/2:
The expected value of sunny days per month is 15. Calculate the probability that the number of sunny days will be less or equal 12.
Python realization:
import scipy.stats as stats probability = stats.poisson.cdf(12, 15) print("The probability is", probability * 100, "%")
Example 1/2:
The expected value of sunny days per month is 15. Calculate the probability that the number of sunny days will be less equal the number within the range from 5 to 11 (5; 11].
Python realization:
import scipy.stats as stats prob_1 = stats.poisson.cdf(11, 15) prob_2 = stats.poisson.cdf(5, 15) probability = prob_1 - prob_2 print("The probability is", probability * 100, "%")
When we subtract the second expression from the first, we leave the interval from 11 to 5 exclusive. Thus, using this calculation stats.poisson.cdf(11, 15), we will find the probability that our variable will take a value less than 11. And using this calculation stats.poisson.cdf(5, 15), we will find the probability that our variable will take a value less than or equal to 5.
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 3/3
As you remember, with the .cdf() function, we can calculate the probability that the random variable will take a value less then or equal a defined number. Look at the example:
Example 1/2:
The expected value of sunny days per month is 15. Calculate the probability that the number of sunny days will be less or equal 12.
Python realization:
import scipy.stats as stats probability = stats.poisson.cdf(12, 15) print("The probability is", probability * 100, "%")
Example 1/2:
The expected value of sunny days per month is 15. Calculate the probability that the number of sunny days will be less equal the number within the range from 5 to 11 (5; 11].
Python realization:
import scipy.stats as stats prob_1 = stats.poisson.cdf(11, 15) prob_2 = stats.poisson.cdf(5, 15) probability = prob_1 - prob_2 print("The probability is", probability * 100, "%")
When we subtract the second expression from the first, we leave the interval from 11 to 5 exclusive. Thus, using this calculation stats.poisson.cdf(11, 15), we will find the probability that our variable will take a value less than 11. And using this calculation stats.poisson.cdf(5, 15), we will find the probability that our variable will take a value less than or equal to 5.
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 .cdf() function, we can calculate the probability that the random variable will take a value less then or equal a defined number. Look at the example:
Example 1/2:
The expected value of sunny days per month is 15. Calculate the probability that the number of sunny days will be less or equal 12.
Python realization:
import scipy.stats as stats probability = stats.poisson.cdf(12, 15) print("The probability is", probability * 100, "%")
Example 1/2:
The expected value of sunny days per month is 15. Calculate the probability that the number of sunny days will be less equal the number within the range from 5 to 11 (5; 11].
Python realization:
import scipy.stats as stats prob_1 = stats.poisson.cdf(11, 15) prob_2 = stats.poisson.cdf(5, 15) probability = prob_1 - prob_2 print("The probability is", probability * 100, "%")
When we subtract the second expression from the first, we leave the interval from 11 to 5 exclusive. Thus, using this calculation stats.poisson.cdf(11, 15), we will find the probability that our variable will take a value less than 11. And using this calculation stats.poisson.cdf(5, 15), we will find the probability that our variable will take a value less than or equal to 5.
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