To analyze the relationship between the feature "installment_commitment" and the task of determining whether a person receives credit or not, we need to examine the distribution of "installment_commitment" values for each target class ('yes' and 'no').

Based on prior knowledge, we can assume that higher values of installment_commitment might indicate a higher risk of defaulting on credit payments, leading to a lower likelihood of receiving credit. Conversely, lower values of installment_commitment might suggest a lower risk of defaulting and a higher chance of receiving credit.

Here is the dictionary with the relevant information:

```json
{
  "yes": [10.0, 12.5, 15.0, 17.5, 20.0],
  "no": [40.0, 42.5, 45.0, 47.5, 50.0]
}
```

Please note that the provided values are just examples and not an exhaustive list. The exact values will depend on the specific dataset and the distribution of data for the given feature.