Based on prior knowledge, it is difficult to predict the exact relationship between the "purpose" feature and the target variable "Does this person receive a credit?". However, we can still analyze the relationship by examining the distribution of the "purpose" feature for each target class.

To do this, we can calculate the proportion of each purpose category within the "yes" and "no" target classes. Let's assume we have a dataset with the feature "purpose" and the target variable "credit" (yes or no) for each person.

Here is a possible analysis and the resulting dictionary:

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

Explanation:
- Based on the analysis, the "purpose" categories "radio/tv", "education", "furniture/equipment", "new car", and "business" are more commonly associated with the target class "yes" (people who receive credit).
- On the other hand, the "purpose" categories "used car", "domestic appliance", "repairs", "other", and "retraining" are more commonly associated with the target class "no" (people who do not receive credit).
- There might be additional purpose categories that are hard to predict, so they are not included in the dictionary.
- It is important to note that this analysis is based on the assumptions made and the specific data available for analysis. The relationship between the "purpose" feature and the target variable may vary depending on the dataset and other factors.