Based on prior knowledge, we can analyze the relationship between age and the target variable of earning more than $50,000 per year.

Analysis:

Typically, as individuals gain more experience and progress in their careers, their earnings tend to increase. Therefore, we can expect older individuals to have a higher likelihood of earning more than $50,000 per year. However, this relationship may not hold true for individuals who are too young or haven't yet entered the workforce.

Based on this analysis, we can create a dictionary with typical age values for each target class:

```json
{
	"no": [18, 25, 30, 40, 55],
	"yes": [30, 35, 45, 50, 60]
}
```

This dictionary suggests that for the target class "no" (earning less than or equal to $50,000 per year), typical age values could be 18, 25, 30, 40, and 55. For the target class "yes" (earning more than $50,000 per year), typical age values could be 30, 35, 45, 50, and 60.

Please note that these values are not exhaustive and may vary depending on the dataset and specific domain knowledge.