Based on the problem statement, we want to analyze the relationship between the feature "duration" (which represents the duration in months) and the target variable "Does this person receive a credit?". To do this, we will identify typical duration values for each target class ('yes' and 'no').

We can start by looking at the distribution of duration values for each target class. Let's assume that a shorter duration may indicate a higher likelihood of receiving a credit, whereas a longer duration may indicate a lower likelihood.

Based on our prior knowledge, we could consider the following typical duration ranges for each target class:
- For the 'yes' class (person receives a credit), typical durations could be in the range of 1-24 months.
- For the 'no' class (person does not receive a credit), typical durations could be in the range of 8-48 months.

Now, let's create a dictionary with the typical duration values for each target class:

```json
{
	"yes": [1.0, 2.0, 3.0, 12.0, 24.0],
	"no": [8.0, 12.0, 18.0, 36.0, 48.0]
}
```

Note: These are just example values and ranges. They can be adjusted based on domain knowledge and the specific dataset being analyzed.