Based on prior knowledge, we can assume that the existing_credits feature represents the number of existing credits at the bank for a given person. To analyze the relationship between this feature and the task of whether the person receives a credit or not, we can look at how the distribution of existing credits differs for the target classes "yes" and "no."

- If the existing_credits feature is positively correlated with the target variable "yes," it means that a higher number of existing credits at the bank increases the likelihood of the person receiving a credit.
- If the existing_credits feature is negatively correlated with the target variable "yes," it means that a higher number of existing credits at the bank decreases the likelihood of the person receiving a credit.

To create the dictionary with specific details, let's assume the following typical existing_credits values for each target class:

- For the "yes" class:
  - 0 existing credits
  - 1 existing credit
  - 2 existing credits
  - 3 existing credits
  - 4 existing credits

- For the "no" class:
  - 2 existing credits
  - 5 existing credits
  - 7 existing credits
  - 9 existing credits
  - 12 existing credits

Based on this analysis, the dictionary can be represented as a markdown code snippet:

```json
{
	"yes": [0.0, 1.0, 2.0, 3.0, 4.0],
	"no": [2.0, 5.0, 7.0, 9.0, 12.0]
}
```

Please note that the values in the dictionary are presented as floats, as specified in the task instructions.