binom.pmf() method from the
scipy.stats library can be used to calculate the PMF of the binomial distribution at any value. This method takes 3 values:
x: the value of interest
n: the number of trials
p: the probability of success
For example, suppose we flip a fair coin 10 times and count the number of heads. We can use the
binom.pmf() function to calculate the probability of observing 6 heads as follows:
# import necessary library import scipy.stats as stats # st.binom.pmf(x, n, p) print(stats.binom.pmf(6, 10, 0.5))
Notice that two of the three values that go into the
stats.binomial.pmf() method are the parameters that define the binomial distribution:
n represents the number of trials and
p represents the probability of success.
Change the scaffolded code to calculate the probability of observing 3 heads in 10 fair coin flips. Save this probability to
n in script.py accordingly.
Using the same logic in question 1, calculate
prob_2 to be the probability of observing 7 heads out of 20 fair coin flips. However, this time, directly input values into the