Exercise

# Distribution of income

In many datasets, the distribution of income is approximately lognormal, which means that the logarithms of the incomes fit a normal distribution. We'll see whether that's true for the GSS data. As a first step, you'll compute the mean and standard deviation of the log of incomes using NumPy's `np.log10()`

function.

Then, you'll use the computed mean and standard deviation to make a `norm`

object using the `scipy.stats.norm()`

function.

Instructions

**100 XP**

- Extract
`'realinc'`

from`gss`

and compute its logarithm using`np.log10()`

. - Compute the mean and standard deviation of the result.
- Make a
`norm`

object by passing the computed mean and standard deviation to`norm()`

.