Based on prior knowledge and assuming that age is a relevant feature for determining whether a person receives credit, we can conduct an analysis of the relationship between age and the credit decision.

Here is a dictionary representing the analysis:

```json
{
	"yes": [25.0, 32.0, 40.0, 45.0, 50.0],
	"no": [20.0, 28.0, 35.0, 55.0, 60.0]
}
```

In this analysis, for the target class "yes", we have identified typical age values to be 25, 32, 40, 45, and 50. These values represent ages of individuals who are more likely to receive credit.

For the target class "no", the typical age values are 20, 28, 35, 55, and 60. These values indicate ages of individuals who are less likely to receive credit.

Please note that these age ranges are just hypothetical examples and might vary depending on the specific dataset and domain.