Based on prior knowledge, we can analyze the relationship between the feature "other_payment_plans" and the task of determining whether a person receives credit.

To analyze this relationship, we can look at the distribution of the "other_payment_plans" feature for each target class (yes or no). This will help us understand if there is any significant difference in the distribution of this feature between the two target classes.

Here is the analysis of the relationship:

For the target class "yes" (person receives credit):
- There may be some individuals who have no other payment plans (category "none").
- There may be individuals who have other payment plans with a bank (category "bank").
- There may be individuals who have other payment plans with stores (category "stores").

For the target class "no" (person does not receive credit):
- There may be some individuals who have no other payment plans (category "none").
- There may be individuals who have other payment plans with a bank (category "bank").
- There may be individuals who have other payment plans with stores (category "stores").

Based on the analysis, it appears that the "other_payment_plans" feature has some variability within each target class. Therefore, it is important to include this feature in the analysis as it can potentially provide useful information for predicting the target variable.

Here is the dictionary representing the relationship between the feature "other_payment_plans" and the target variable:

```json
{
	"yes": ["none", "bank", "stores"],
	"no": ["none", "bank", "stores"]
}
```

Note that even though it is hard to predict the exact values of the feature "other_payment_plans" that are associated with each target class, we include all the possible values in the dictionary to represent the relationship.