Based on prior knowledge about the relationship between savings status and credit approval, it is expected that people with higher savings status are more likely to receive credit. 

To analyze this relationship, we can examine the distribution of savings_status values for each target class (yes and no).

Here is the dictionary representing the analysis:

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

Explanation:
- For the target class 'yes', the possible values of savings_status that indicate a higher likelihood of receiving credit are "500<=X<1000", ">=1000", and "100<=X<500".
- For the target class 'no', the possible values of savings_status that indicate a lower likelihood of receiving credit are "no known savings" and "<100".

Note: To keep the dictionary concise and representative, we omitted the category "<100" from the 'yes' list and the categories "500<=X<1000" and "100<=X<500" from the 'no' list as they do not provide clear predictions. However, this analysis may vary depending on the specific dataset and the accuracy of the predictive model.