Based on my analysis, the relationship between the feature "savings_status" and the target variable "Does this person receive a credit?" can be outlined as follows:

- "savings_status" is a categorical variable that represents the status of a savings account/bonds in Deutsche Mark.
- By looking at the possible categories of "savings_status": ['no known savings', '<100', '500<=X<1000', '>=1000', '100<=X<500'], we can assume that higher savings amounts might indicate a higher probability of receiving credit.
- It is reasonable to expect that individuals with higher savings (e.g., '500<=X<1000', '>=1000') are more likely to receive credit compared to those with lower savings or no savings at all (e.g., 'no known savings', '<100').

Based on this analysis, the dictionary can be generated as follows:

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

Please note that this is a generalized analysis, and the relationship between the feature and the target variable can be more accurately determined by training a machine learning model on the dataset.