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

To analyze the relationship between the feature "purpose" and the target variable "Does this person receive a credit?", we can examine the distribution of purpose values for each target class (yes or no).

Here is the dictionary with the possible values of feature "purpose" for each target class:

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

In this case, I have included the purpose values that seem to have a more significant impact on the credit decision. However, please note that these are general assumptions and the actual relationship between the purpose feature and the target variable should be validated using appropriate statistical techniques and modeling.