To analyze the relationship between the "existing_credits" feature and the task of whether a person receives a credit or not, we can follow these steps:

1. Split the dataset into two groups: one group where the target variable is "yes" (person receives a credit) and another group where the target variable is "no" (person does not receive a credit).

2. Calculate the statistical summary of the "existing_credits" feature for each group separately.

3. Identify typical values for "existing_credits" in each group based on the statistical summary and any prior knowledge.

4. Create a dictionary with the necessary format, including the "yes" and "no" lists of typical "existing_credits" values.

Here is the dictionary for the "existing_credits" feature:

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

Note: The values in the lists are just examples and may not reflect the actual typical values for your dataset. Please replace them with the appropriate values based on your analysis.