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 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))

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

**1.**

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.

**2.**

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`

.