Based on prior knowledge, we can assume that the level of education achieved can be a significant factor in determining whether a person earns more than $50,000 per year or not.

To analyze the relationship between the feature "educational-num" and the task of earning more than $50,000 per year, we need to consider the typical educational-num values for each target class.

Typically, people with higher levels of education tend to earn more, so we can expect that higher educational-num values will be associated with the target class "yes" (earning more than $50,000 per year), while lower educational-num values will be associated with the target class "no" (earning less than or equal to $50,000 per year).

Based on this analysis, here is the dictionary representing the relationship between the feature "educational-num" and the task:

```json
{
	"no": [1, 2, 3, 4, 5],
	"yes": [9, 10, 11, 12, 13]
}
```

In this dictionary, the target class "no" represents individuals earning less than or equal to $50,000 per year, and the target class "yes" represents individuals earning more than $50,000 per year. The list of integers provided in the dictionary represents the typical values of the feature "educational-num" for each target class.