Based on the given feature "savings_status" and the task of determining whether a person receives credit or not, we need to analyze the relationship between the feature and the target variable.

To analyze this relationship, we can look at the distribution of savings_status values for both the "yes" and "no" classes of the target variable.

Here is the analysis and the resulting dictionary:

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

Explanation:
- For the "yes" class, the possible values of savings_status that are associated with receiving credit are "no known savings" and ">=1000". This means that those who have no savings or have savings greater than or equal to 1000 Deutsche Mark are more likely to receive credit.
- For the "no" class, the possible values of savings_status that are associated with not receiving credit are "<100", "500<=X<1000", and "100<=X<500". This implies that those who have savings less than 100 Deutsche Mark, between 500 and 1000 Deutsche Mark (excluding both), or between 100 and 500 Deutsche Mark are less likely to receive credit.

Note: The values mentioned in the dictionary are based on the provided feature description and hypothetical analysis. The actual relationship may vary depending on the dataset and context.