Based on prior knowledge, we can analyze the relationship between the feature "existing_credits" and the task of whether a person receives a credit.

By observing the potential influence of the number of existing credits at this bank, we can expect the following:

1. "existing_credits" may have a positive correlation with the likelihood of receiving a credit. A higher number of existing credits could indicate a responsible credit history and thereby increase the chances of obtaining additional credit.

2. On the other hand, "existing_credits" can also have a negative correlation with the likelihood of receiving a credit. A higher number of existing credits may imply a high level of debt and financial burden, making it less likely for an individual to be granted further credit.

To analyze the relationship further, we can consider the average values of "existing_credits" for both target classes, 'yes' and 'no'.

Here's the dictionary with typical values for 'yes' and 'no':

```json
{
	"yes": [1.2, 1.6, 2.1, 1.8, 1.4],
	"no": [2.5, 2.9, 2.3, 3.2, 2.7]
}
```

Please note that these values are for illustrative purposes only and do not represent actual data. The specific values should be determined based on the available dataset and statistical analysis.