Based on prior knowledge, higher levels of education are usually associated with higher earning potential. Therefore, we can assume that as the educational-num increases, the likelihood of earning more than 50000 dollars per year also increases.

To analyze the relationship between the educational-num feature and the task of whether a person earns more than 50000 dollars per year, we can look at the distribution of educational-num values for both classes (yes and no).

Let's generate the dictionary:

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

In this example, we selected 5 typical educational-num values for each class. It is important to note that these values are not absolute and can vary depending on the dataset. The values provided here are just for illustration purposes and may not accurately represent the actual distribution in the dataset.