Based on prior knowledge, we can analyze the relationship between the age feature and the task of earning more than $50,000 per year. It is reasonable to assume that individuals who are older and have more work experience are more likely to earn higher salaries.

Here is the dictionary with the analysis:

```json
{
    "no": [16, 17, 18, 19, 20], 
    "yes": [45, 50, 55, 60, 65]
}
```

In this dictionary, for the "no" class (individuals who earn less than or equal to $50,000 per year), we have listed five age values: 16, 17, 18, 19, and 20. These values represent younger individuals who may not have entered the job market or have limited work experience.

For the "yes" class (individuals who earn more than $50,000 per year), we have listed five higher age values: 45, 50, 55, 60, and 65. These values represent older individuals who are likely to have more work experience and higher salaries.

Note that the age values are chosen based on assumptions and may not be accurate for a specific dataset. It is important to validate these assumptions using real data before making definitive conclusions.