Based on prior knowledge of the relationship between the feature "savings_status" and the task of determining whether a person receives credit, we can analyze how different values of "savings_status" may relate to the target variable.

Here is the analysis:

1. "no known savings" - It is likely that individuals without any known savings may have a higher chance of not receiving credit since they do not have any financial assets to rely on.

2. "<100" - This category represents individuals with savings less than 100 Deutsche Mark. It is possible that individuals with very low savings may also have a lower chance of receiving credit.

3. "500<=X<1000" - This category represents individuals with savings between 500 and 1000 Deutsche Mark. Individuals in this category may have a moderate chance of receiving credit, but it cannot be conclusively determined without further analysis.

4. ">=1000" - This category represents individuals with savings equal to or greater than 1000 Deutsche Mark. Individuals with higher savings are likely to have a better chance of receiving credit as they have more financial stability.

5. "100<=X<500" - This category represents individuals with savings between 100 and 500 Deutsche Mark. Similar to the previous category, individuals in this category may have a moderate chance of receiving credit, but further analysis is required to draw definitive conclusions.

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

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

Please note that for the categories that are hard to predict, such as "500<=X<1000" and "100<=X<500", we include them in the dictionary but mention that further analysis is required to draw definitive conclusions.