Based on my prior knowledge, the age feature is likely to have a significant impact on the task of determining whether a person receives a credit or not. Younger individuals may have more trouble establishing a credit history or may have less stable financial situations, while older individuals may have more established credit histories or higher incomes.

To analyze the relationship between age and credit approval, I would start by examining the distribution of age values for each target class (yes or no) and identify if there are any noticeable patterns or differences.

Based on this analysis, I can create a dictionary with the requested information:

```json
{
	"yes": [20.0, 25.0, 30.0, 35.0, 40.0],  
	"no": [45.0, 50.0, 55.0, 60.0, 65.0]
}
```

In this example, I have provided five typical age values for each target class, representing a younger age range (20-40 years) for the "yes" class and an older age range (45-65 years) for the "no" class. Please note that the provided age values are just examples and should be adjusted based on the actual data analysis.