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

From a logical perspective, the status of a person's savings account or bonds can be an important factor in determining whether they receive credit or not. Typically, individuals with higher savings or more financial stability are more likely to receive credit, while those with no savings or low savings may have a higher chance of being denied credit.

To conduct a thorough analysis, we can examine the distribution of the "savings_status" feature for each target class (yes and no).

Here is the analysis of the relationship between the feature and the target:

- For the target class "yes" (person receives a credit):
  - Possible values of feature "savings_status":
    - '<100'
    - '500<=X<1000'
    - '>=1000'
    - '100<=X<500'

- For the target class "no" (person does not receive a credit):
  - Possible values of feature "savings_status":
    - 'no known savings'

Based on this analysis, we can now create the dictionary:

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

This dictionary represents the possible values of the "savings_status" feature for each target class. The list for the target class "yes" contains the values '<100', '500<=X<1000', '>=1000', and '100<=X<500', indicating that individuals with these savings statuses are more likely to receive credit. The list for the target class "no" contains the value 'no known savings', indicating that individuals with this savings status are less likely to receive credit.