Based on prior knowledge, we can analyze the relationship between the feature "purpose" and the task of whether a person receives a credit or not.

To do so, we can examine the distribution of different purpose categories for those who receive credit (target class: "yes") and those who do not (target class: "no").

Here is the analysis and the resulting dictionary:

```json
{
	"yes": ["radio/tv", "education", "furniture/equipment", "new car", "business", "domestic appliance", "repairs"], 
	"no": ["used car", "other", "retraining"]
}
```

In this analysis, the purpose values that are more commonly associated with receiving credit (target class "yes") include "radio/tv", "education", "furniture/equipment", "new car", "business", "domestic appliance", and "repairs".

On the other hand, purpose values such as "used car", "other", and "retraining" are more commonly associated with not receiving credit (target class "no").

Note that purpose values that are hard to predict are not included in the dictionary, but lists for each target class are not empty.