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

Intuitively, the savings status of an individual can be an important determinant of whether they receive credit or not. People with higher savings are generally considered to have a lower risk of defaulting on credit, and hence are more likely to receive credit compared to those with lower or no savings. Therefore, we can expect that individuals with higher savings_status are more likely to receive credit.

Based on this analysis, we can create a dictionary with the possible values of savings_status for each target class:

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

Here, we include the savings_status values that are likely to have a strong relationship with the target class, while omitting values that may not have a clear prediction. It is important to note that these predictions are based on general knowledge and assumptions, and further analysis with actual data would provide more accurate insights.