Analysis:

In order to analyze the relationship between the "purpose" feature and the target variable "Does this person receive a credit?" (Yes or no), we can examine the distribution of the purpose categories for each target class.

After analyzing the feature and target, we can create a dictionary:

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

Explanation:

Based on the analysis, the purpose categories that are more likely to be associated with receiving credit ("yes" class) are: "radio/tv", "education", "furniture/equipment", "new car", "used car", "business" and "domestic appliance". 

On the other hand, the purpose categories that are more likely to be associated with not receiving credit ("no" class) are: "repairs", "other" and "retraining".

It is not necessary to include all possible purpose values in the dictionary, as some categories may not have a strong association with the target class. However, this analysis provides insights into the purpose categories that are more relevant to the target variable.