Based on prior knowledge, the purpose feature could be an important factor in determining whether a person receives credit or not. Different purposes may have different levels of risk associated with them, which could impact the credit decision.

To analyze the relationship, we can compare the distribution of purpose values for the two target classes (yes and no). By examining the values that are more predominant in one class compared to the other, we can understand the relationship between the purpose and the credit decision.

Here's the dictionary:

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

In the "yes" class, the purpose values ["radio/tv", "education", "business", "domestic appliance"] are more likely to indicate that a person will receive credit. On the other hand, in the "no" class, the purpose values ["furniture/equipment", "new car", "used car", "repairs"] are more likely to indicate that a person will not receive credit.

Please note that these lists are based on the assumption that there are clear patterns in the data that can be used to predict the credit decision. If the purpose feature doesn't show strong predictive power, the lists might be empty or less meaningful.