Based on prior knowledge, the purpose of the credit could be an important factor in determining whether a person receives a credit or not. Different purposes may have different creditworthiness, which could affect the decision.

To analyze the relationship between the feature "purpose" and the target variable "Does this person receive a credit? Yes or no?", we can look at the distribution of purpose values for each target class.

Here is the analysis and the generated dictionary:

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

Based on the analysis, the purpose values "radio/tv", "education", "furniture/equipment", "new car", "used car", "business", and "domestic appliance" are associated with the target class "yes" indicating that people with these purposes are more likely to receive a credit. On the other hand, the purpose values "repairs", "other", and "retraining" are associated with the target class "no" indicating that people with these purposes are less likely to receive a credit.

Please note that this analysis is based on the given feature and target variable description. Additional data and analysis might be required for a more accurate relationship analysis.