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Probability Theory

bookBinomial probability 2/2

Look at the code example of the binomial probability


              
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# Import relevant library
from scipy.stats import binom
# Here, we simulate an experiment of tossing 5 coins three times
experiment = binom.rvs(p = 0.5, size = 5, n = 3)
print(experiment)
copy

Explanation of the code above:

  1. We need to import binom object from scipy.stats.
  2. binom.rvs(p = 0.5, size = 5, n = 3) means that the probability of getting head is 50 %, p = 0.5; the size of sample in experiment is 5, size = 5; the number of trial is 3, n = 3.
  3. In the output we can see an array with five results for each coin with the number of successful trials for each coin.
Taak

Swipe to start coding

Your task here is almost the same as in the previous chapter, play with one coin!

Imagine that here you have a coin with a general probability of 50%. Follow this algorithm:

  1. Import the binom object from scipy.stats.

  2. Conduct the experiment with binom object using rvs() function:

    • Set p parameter equal to 0.5.
    • Set size parameter equal to 1.
    • Set n parameter equal to 5.

Please note, you can comment on the line where np.random.seed() was defined and "play with the coin" to receive various outputs.

Oplossing

Note

Explanation of the output : We were tossing one coin five times, and it only led to success in three cases.

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Look at the code example of the binomial probability


              
12345

            
# Import relevant library
from scipy.stats import binom
# Here, we simulate an experiment of tossing 5 coins three times
experiment = binom.rvs(p = 0.5, size = 5, n = 3)
print(experiment)
copy

Explanation of the code above:

  1. We need to import binom object from scipy.stats.
  2. binom.rvs(p = 0.5, size = 5, n = 3) means that the probability of getting head is 50 %, p = 0.5; the size of sample in experiment is 5, size = 5; the number of trial is 3, n = 3.
  3. In the output we can see an array with five results for each coin with the number of successful trials for each coin.
Taak

Swipe to start coding

Your task here is almost the same as in the previous chapter, play with one coin!

Imagine that here you have a coin with a general probability of 50%. Follow this algorithm:

  1. Import the binom object from scipy.stats.

  2. Conduct the experiment with binom object using rvs() function:

    • Set p parameter equal to 0.5.
    • Set size parameter equal to 1.
    • Set n parameter equal to 5.

Please note, you can comment on the line where np.random.seed() was defined and "play with the coin" to receive various outputs.

Oplossing

Note

Explanation of the output : We were tossing one coin five times, and it only led to success in three cases.

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Was alles duidelijk?

Hoe kunnen we het verbeteren?

Bedankt voor je feedback!

close

Awesome!

Completion rate improved to 3.7
Sectie 1. Hoofdstuk 5
single

single

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