Based on prior knowledge, we can assume that the savings_status feature may have some impact on whether a person receives credit or not. 

To analyze the relationship between the savings_status feature and the target variable, we can look at the distribution of savings_status categories for each target class (yes and no).

Here is the analysis and resulting dictionary:

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

Based on the analysis, the savings_status categories "500<=X<1000", ">=1000", and "100<=X<500" are associated with the target class "yes", indicating that people with these savings_status values are more likely to receive credit. On the other hand, the "no known savings" and "<100" categories are associated with the target class "no", suggesting that people with these savings_status values are less likely to receive credit.

Please note that the savings_status categories ">=1000" and "100<=X<500" may be hard to predict accurately, but they are still included in the dictionary as they are associated with the target class "yes".