Based on prior knowledge and understanding of the feature "existing_credits" and the task of determining whether a person receives a credit or not, we can analyze the relationship between the feature and the target variable.

The feature "existing_credits" represents the number of existing credits at this bank. It is a numeric variable, meaning it can take on continuous values. In order to determine the appropriate ranges of values, we can consider typical values for the target classes 'yes' and 'no'.

Here is the analysis of the relationship between "existing_credits" and the target variable:

- For the target class 'yes' (person receives a credit):
  - Typical values of "existing_credits" might include: 1, 2, 3, 4, 5
  - It is possible for the range of "existing_credits" for 'yes' to be between 1 and 10.

- For the target class 'no' (person does not receive a credit):
  - Typical values of "existing_credits" might include: 0, 1, 2, 3, 4
  - It is possible for the range of "existing_credits" for 'no' to be between 0 and 8.

Based on this analysis, we can create the following dictionary:

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

These lists contain 5 typical values for each target class, representing possible values of the feature "existing_credits" for the 'yes' and 'no' classes, respectively.