Based on this task, we need to analyze how the feature "fnlwgt" relates to the target variable of earning more than $50,000 per year. 

The feature "fnlwgt" represents the number of units in the target population that the responding unit represents. It is a numeric variable, and we need to determine the appropriate ranges of values to analyze its relationship with the target variable.

Without additional information about the dataset or the feature itself, it is difficult to determine the exact ranges of "fnlwgt" that are associated with different income levels. However, we can make some assumptions based on common knowledge and general observations:

1. Higher "fnlwgt" values may indicate representing a larger part of the target population, which could imply a higher chance of earning more than $50,000 per year. Therefore, we can expect higher "fnlwgt" values to be more prevalent in the "yes" class.
2. Lower "fnlwgt" values may indicate representing a smaller part of the target population, which could imply a lower chance of earning more than $50,000 per year. Therefore, we can expect lower "fnlwgt" values to be more prevalent in the "no" class.

Now, let's create a dictionary with typical "fnlwgt" values for each target class:

```json
{
	"no": [500, 1000, 1500, 2000, 2500],
	"yes": [50000, 55000, 60000, 65000, 70000]
}
```