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:
12345import 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:
1234567891011import 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.
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Completion rate improved to 3.7Poisson Distribution 3/3
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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:
12345import 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:
1234567891011import 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.
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Completion rate improved to 3.7single
