After analyzing the relationship between the "purpose" feature and the task of determining whether a person receives credit or not, it is likely that certain purposes may have a stronger association with the target variable.

Based on prior knowledge, some possible relationships between "purpose" and the task could be:

- "education" and "retraining" could have a positive association with receiving credit, as they are related to personal development and acquiring new skills.
- "business" could also have a positive association with receiving credit, as it implies someone is starting or expanding a business.
- "radio/tv", "furniture/equipment", "new car", "used car", "domestic appliance", and "repairs" may have a moderate association with receiving credit, as they are related to purchasing or investing in tangible assets or goods.
- "other" is a broad category that is hard to predict. It may not provide much information about the likelihood of receiving credit.

Based on this analysis, the dictionary would be:

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

Please note that the values mentioned in the dictionary are based on assumptions and prior knowledge. Actual relationships may vary depending on the specific dataset and context.