Based on prior knowledge, we can analyze the relationship between age and whether a person receives credit.

Analyzing the relationship involves exploring the distribution of age values for both the 'yes' and 'no' target classes.

Here is a dictionary representing the relationship between the feature 'age' and the target 'credit':

```json
{
	"yes": [25.0, 30.0, 35.0, 40.0, 45.0],
	"no": [18.0, 22.0, 28.0, 32.0, 38.0]
}
```

The 'yes' list represents typical age values for individuals who receive credit, and the 'no' list represents typical age values for individuals who do not receive credit. These values are presented as floats in the lists.