Based on prior knowledge and assuming that higher levels of education generally correlate positively with higher income, we can analyze the relationship between the feature "educational-num" and the task "Does this person earn more than 50000 dollars per year?"

Here's the analysis:

- "educational-num" represents the highest level of education achieved by an individual.
- Generally, individuals with a higher educational level tend to earn more.
- We can expect to see higher values of "educational-num" for individuals who earn more than $50,000 per year.
- However, there may be exceptions, as income is influenced by various factors other than education, such as occupation, experience, etc.

Based on this analysis, let's create the dictionary:

```json
{
	"no": [1, 2, 3, 4, 5],
	"yes": [10, 11, 12, 13, 14]
}
```

In this dictionary, we have included five typical values of "educational-num" for each target class. The values in the "no" list represent individuals who do not earn more than $50,000 per year, while the values in the "yes" list represent individuals who earn more than $50,000 per year. Please note that these values are just examples and may vary depending on the dataset and specific analysis.