The 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 interestn
: the number of trialsp
: 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))
Output:
# 0.205078
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.
Instructions
Change the scaffolded code to calculate the probability of observing 3 heads in 10 fair coin flips. Save this probability to prob_1
.
Update x
and 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 stats.binomial.pmf()
method.
Then print prob_2
.