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

Here is the analysis:

- It is reasonable to assume that the purpose of the credit can be a significant factor in determining whether a person receives a credit or not.
- Some purposes, such as "education" or "business", might indicate a higher likelihood of receiving a credit because they are related to investments or assets that can increase the borrower's financial stability.
- On the other hand, purposes like "domestic appliance" or "repairs" might indicate a lower likelihood of receiving a credit because they are related to short-term expenses rather than long-term investments.
- Other purposes such as "radio/tv", "furniture/equipment" or "used car" can vary depending on the individual's financial situation and creditworthiness.

Based on this analysis, we can generate the dictionary as follows:

```json
{
	"yes": ["education", "business"], 
	"no": ["domestic appliance", "repairs"]
}
```

Note that this is just a simplified example, and it is possible that the relationship between the "purpose" feature and the task may vary depending on other factors or the specific dataset being analyzed.