Based on prior knowledge, the feature "existing_credits" can be analyzed to understand its relationship with the target variable "Does this person receive a credit?". This will help in determining the appropriate ranges of values for the feature and identify any trends or patterns.

To analyze the relationship, we can group the data based on the target variable and observe the distribution of values for the feature. Let's assume the target variable has two classes - "yes" and "no".

After analyzing the data, the dictionary with the appropriate values can be generated. Here is an example:

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

This dictionary suggests that for the target class "yes", the feature "existing_credits" typically has values ranging from 0.0 to 4.0, while for the target class "no", the feature values range from 1.0 to 5.0. It provides an overview of the possible values for the feature in each target class.

Please note that these values are only examples and should be modified or expanded based on the actual data and analysis.