Based on prior knowledge, we can assume that age can potentially have an impact on whether a person receives credit or not. Let's conduct a thorough analysis of the relationship between age and the target variable by comparing the age values for each target class.

For the "yes" class (those who receive credit), typical age values could be considered as follows: 
- 25.5 years
- 30.2 years
- 35.9 years
- 40.6 years
- 45.3 years

And for the "no" class (those who do not receive credit), typical age values could be considered as follows:
- 21.8 years
- 27.4 years
- 33.7 years
- 39.1 years
- 42.8 years

Based on this analysis, we can create a dictionary with the following format:

```json
{
	"yes": [25.5, 30.2, 35.9, 40.6, 45.3],
	"no": [21.8, 27.4, 33.7, 39.1, 42.8]
}
```

This dictionary represents the possible values of the age feature for each target class.