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

From the given information, we can infer that the purpose of the credit may be an important factor in determining whether a person receives a credit or not. Different loan purposes may vary in their risk profiles, thus influencing the lending decision.

To generate the requested dictionary, we need to analyze the distribution of purposes for both classes of the target variable. Let's infer this based on the available data.

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

Based on the available data, the purposes associated with receiving a credit are: "radio/tv", "education", "furniture/equipment", "new car", "used car", "business", "domestic appliance", and "repairs". 

However, for the purpose of "other" there is no information about whether the person receives a credit or not. Therefore, we cannot determine a clear relationship in this case.

Note: This analysis assumes the given data is representative of the overall population and may not necessarily apply in all scenarios. Further analysis and modeling techniques can provide more accurate insights.