To analyze the relationship between the feature "installment_commitment" and the target task of whether a person receives a credit or not, we can look at the typical values of "installment_commitment" for each target class.

Based on prior knowledge, we can hypothesize the following relationships:

- If the "installment_commitment" is lower, indicating a lower percentage of disposable income dedicated to installment payments, it may increase the chances of receiving a credit.

- Conversely, if the "installment_commitment" is higher, indicating a higher percentage of disposable income dedicated to installment payments, it may decrease the chances of receiving a credit.

With this hypothesis in mind, let's create a dictionary with the typical values of "installment_commitment" for each target class:

```json
{
	"yes": [10.0, 8.5, 11.2, 9.8, 13.5],
	"no": [15.0, 14.2, 16.9, 20.5, 18.3]
}
```
Please note that these values are just hypothetical examples and may not represent the actual relationship between the feature and the target.