To analyze the relationship between the "existing_credits" feature and the target variable of whether the person receives credit or not, we can examine the distribution of "existing_credits" values for each class ("yes" and "no").

Based on prior knowledge, let's assume that individuals with a higher number of existing credits at the bank may be less likely to receive credit. Conversely, individuals with a lower number of existing credits might have a higher chance of receiving credit. 

To create the dictionary, we will consider five typical values for each target class.

Here is the dictionary with the information requested:

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

In the "yes" class, the typical values for the "existing_credits" feature are 0.0, 1.0, 2.0, 3.0, and 4.0. 
In the "no" class, the typical values for the "existing_credits" feature are 1.0, 2.0, 3.0, 4.0, and 5.0.