Analysis:

To analyze the relationship between the feature "existing_credits" and the target variable "Does this person receive a credit?", we can start by grouping the data based on the target variable and then examining the distribution of "existing_credits" values within each group.

Based on prior knowledge, we can assume that individuals with a higher number of existing credits at the bank might have a lower chance of receiving additional credit. Conversely, individuals with a lower number of existing credits might have a higher chance of receiving a credit.

Based on this assumption, we can analyze the relationship between the feature and target. By examining the distribution of "existing_credits" values for both the "yes" and "no" target classes, we can identify typical values that are associated with each class.

Dictionary:

```json
{
	"yes": [0.0, 1.0, 2.0, 3.0, 4.0],  
	"no": [1.0, 2.0, 3.0, 4.0, 5.0] 
}
```

In this dictionary representation, we have included 5 typical values for each target class. These values, presented as floats, represent possible values of the feature "existing_credits" for the respective target classes "yes" and "no".