Based on the given feature "savings_status" and the task of determining whether a person receives credit or not, we can analyze the relationship between these two variables.

To understand the relationship between "savings_status" and the target variable, we can examine the distribution of different values of "savings_status" for each class (yes or no) of the target variable. By doing this, we can identify if certain values of "savings_status" are more prevalent in one class than the other.

Here is the analysis of the relationship between "savings_status" and the target variable:

- For the class "yes" (person receives the credit):
  - Possible values of "savings_status" for this class: ['no known savings', '<100', '500<=X<1000', '>=1000', '100<=X<500']

- For the class "no" (person does not receive the credit):
  - Possible values of "savings_status" for this class: ['no known savings', '<100', '500<=X<1000', '100<=X<500']

Based on this analysis, we can generate the following dictionary:

```json
{
	"yes": ["no known savings", "<100", "500<=X<1000", ">=1000", "100<=X<500"],
	"no": ["no known savings", "<100", "500<=X<1000", "100<=X<500"]
}
```

Note: The values "500<=X<1000" and ">=1000" are present only in the "yes" class and not in the "no" class. Hence, these values are more likely associated with receiving credit.