Based on prior knowledge, it is expected that higher levels of education are positively correlated with higher income. Therefore, individuals with a higher value of educational-num are more likely to earn more than 50000 dollars per year.

To analyze the relationship between the feature educational-num and the task of determining whether a person earns more than 50000 dollars per year, we can examine the distribution of educational-num values for both target classes.

Let's assume that the feature educational-num takes on integer values ranging from 1 to 16, with 1 representing the lowest level of education and 16 representing the highest level.

Here is the dictionary with the relevant information:

```json
{
	"no": [1, 2, 3, 4, 5], 
	"yes": [12, 13, 14, 15, 16]
}
```

Explanation:
- For the target class "no" (earning less than or equal to 50000 dollars per year), the feature educational-num can take on values such as 1, 2, 3, 4, and 5. These values represent lower levels of education.
- For the target class "yes" (earning more than 50000 dollars per year), the feature educational-num can take on values such as 12, 13, 14, 15, and 16. These values represent higher levels of education.

Please note that these values are just examples and may vary depending on the actual dataset.