Based on prior knowledge, it is not immediately clear how the feature "fnlwgt" might directly relate to the target variable of whether a person earns more than $50,000 per year. The feature represents the number of units in the target population that the responding unit represents. However, without knowing the specific context or domain knowledge related to this dataset, it would be difficult to determine specific ranges of values that may be correlated with the target variable.

To proceed with the analysis, we can consider the distribution and summary statistics of the "fnlwgt" feature for each class of the target variable. By comparing the feature values for individuals earning more than $50,000 per year (target class 'yes') and individuals earning less than or equal to $50,000 per year (target class 'no'), we might be able to identify any noticeable patterns or differences.

Here is a dictionary providing a markdown code snippet with example feature values for each target class:

```json
{
	"no": [124267, 89015, 40982, 186934, 457812],
	"yes": [153266, 276582, 102879, 384882, 72519]
}
```

These values are just examples and would need to be replaced with actual data extracted from the dataset.

Please note that this analysis only provides a general understanding of the potential relationship between the feature "fnlwgt" and the target variable. Further analysis, such as visualizations or additional modeling techniques, may be required to gain deeper insights if the feature is indeed meaningful in determining whether a person earns more than $50,000 per year.