Based on prior knowledge, education level is often correlated with income level, with higher levels of education typically leading to higher income. 

To analyze the relationship between the feature "educational-num" and the task "Does this person earn more than 50000 dollars per year?" and generate the dictionary, we need to examine the distribution of "educational-num" for individuals earning more or less than $50,000 per year.

Assuming "educational-num" represents the highest level of education achieved, we can make some generalizations:

1. Individuals with higher education levels tend to have a higher income. Therefore, we may expect a positive relationship between "educational-num" and earning over $50,000 per year.
2. It is likely that individuals with lower education levels will have a higher chance of earning less than $50,000 per year.
3. There may be exceptions, as factors other than education can also affect income.

Now, let's analyze the data further and create the dictionary:

```json
{
    "no": [1, 2, 3, 4, 5],
    "yes": [8, 9, 10, 11, 12]
}
```

Note: The exact values for "educational-num" may vary depending on the dataset used, but this dictionary represents a general example.