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

To conduct the analysis, we can consider the following approach:
1. Obtain a dataset that includes the "credit_amount" feature and the target variable indicating whether a person receives credit.
2. Split the dataset into two groups based on the target variable ('yes' and 'no').
3. Analyze the distribution of "credit_amount" values within each group.
4. Identify typical credit_amount values for each target class based on the distribution analysis.

Considering the given task, we can assume that lower credit amounts might be typical for the 'no' class, while higher credit amounts might be more common for the 'yes' class. However, to provide more accurate and detailed information, we need access to the actual dataset.

Therefore, let's assume that we have the necessary dataset and perform the analysis using this assumption. Here is a dictionary representing the relationship between the "credit_amount" feature and the target variable:

```json
{
    "yes": [2000.0, 3000.0, 5000.0, 10000.0, 15000.0],
    "no": [500.0, 1000.0, 1500.0, 2500.0, 4000.0]
}
```

Please note that the credit_amount values provided above are fictional and may not reflect the actual distribution in the dataset. To obtain more accurate information, analyzing the dataset would be necessary.