Based on prior knowledge, it can be assumed that the feature "educational-num" represents the highest level of education achieved by a person. Higher levels of education are often associated with higher income levels, so it is expected that individuals with higher educational-num values are more likely to earn more than $50,000 per year.

To analyze the relationship between the feature and the task, we can examine the distribution of educational-num values for both the "yes" and "no" target classes. Here is the dictionary containing typical educational-num values for each target class:

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

In this dictionary, the "no" class represents individuals who earn less than or equal to $50,000 per year, while the "yes" class represents individuals who earn more than $50,000 per year. The values [1, 2, 3, 4, 5] are typical values for the "no" class, meaning that individuals with educational-num values in this range are more likely to earn less than or equal to $50,000 per year. On the other hand, the values [9, 10, 11, 12, 13] are typical values for the "yes" class, indicating that individuals with educational-num values in this range are more likely to earn more than $50,000 per year.

It's important to note that the exact ranges of educational-num values for each class may vary depending on the specific dataset and the criteria used to determine the target variable. This analysis is based on general assumptions and may require further exploration and validation for a specific dataset.