Based on prior knowledge, we can analyze the relationship between the feature "other_payment_plans" and the target variable "Does this person receive a credit?". 

The feature "other_payment_plans" represents the other installment plans that the person has, categorized as "none", "bank", or "stores". 

To analyze the relationship, we can examine the distribution of the feature values for the target classes "yes" (people who receive credit) and "no" (people who do not receive credit). 

Here is a possible analysis:

- For the target class "yes":
  - It is likely that people who have other installment plans with banks are more likely to receive credit.
  - Having other installment plans with stores might also be a positive factor for receiving credit, but it is not as clear.
  - The category "none" suggests that the person does not have any other installment plans, which might have a negative influence on receiving credit.

- For the target class "no":
  - Having other installment plans with banks or stores might indicate a higher risk for credit denial.
  - The category "none" might not have a significant impact on the credit decision.

Based on this analysis, we can create a dictionary with the possible values of the feature "other_payment_plans" for each target class:

```json
{
	"yes": ["bank", "stores"],
	"no": ["none", "bank", "stores"]
}
```
Note that this is a hypothetical analysis and the actual relationship might be different depending on the dataset and context.