Based on prior knowledge, the native country of an individual can potentially be related to their income level. Some countries may have higher average incomes, while others may have lower average incomes. To analyze the relationship between the native-country feature and the target variable (earning more than 50000 dollars per year), we can examine the distribution of the native-country values for both target classes.

Using this analysis, we can create a dictionary with the values of the native-country feature for each target class.

```json
{
	"no": ["United-States", "?", "Mexico", "Philippines", "Germany", "Canada"],
	"yes": ["United-States", "?", "Mexico", "Philippines", "Germany", "India", "England", "Iran"]
}
```

In this dictionary, the "no" list includes the native-country values that are associated with individuals who do not earn more than 50000 dollars per year. The "yes" list includes the native-country values that are associated with individuals who earn more than 50000 dollars per year.

Please note that "United-States" and "?" are included in both lists, as they are applicable to both target classes. Additionally, this analysis is based on general assumptions and may not capture all complexities of the relationship between the native-country feature and the target variable.