Based on prior knowledge, we can assume that the credit amount may have an impact on whether a person receives credit or not. Typically, higher credit amounts may be associated with a higher likelihood of receiving credit. However, there might be some thresholds or ranges beyond which the credit amount becomes less influential.

To analyze the relationship between the credit amount and the task of whether a person receives credit or not, we can consider the following steps:

1. Collect and preprocess the data: Gather a dataset that includes information on the credit amount and whether each person receives credit or not. Preprocess the data by cleaning any missing values or outliers, and ensure that the credit amount is expressed as a numeric variable.

2. Visualize the relationship: Create a scatter plot or histogram with the credit amount on the x-axis and the target variable (credit received - yes or no) on the y-axis. Observe if there are any discernible patterns or trends.

3. Analyze credit amount distribution for each target class: Calculate the statistical measures of central tendency (mean, median) and dispersion (standard deviation, interquartile range) for the credit amount variable separately for the 'yes' and 'no' target classes. This will give us an understanding of the typical values for each class.

4. Identify typical credit amount values: Based on the analysis, select 5 representative credit amount values for each target class. These values should capture the range of credit amounts that are typically associated with 'yes' or 'no' credit outcomes.

Here is the dictionary with credit amount values for the 'yes' and 'no' target classes:

```json
{
	"yes": [500.0, 1500.0, 3000.0, 5000.0, 10000.0],
	"no": [1000.0, 2000.0, 4000.0, 8000.0, 15000.0]
}
```

Please note that these values are examples and should be adjusted based on the actual data analysis.