Based on prior knowledge, we can assume that the duration of a credit can have some influence on whether a person receives credit or not. Let's conduct a thorough analysis of the relationship between the feature "duration" and the target variable "Does this person receive a credit?"

To analyze this relationship, we can consider the following approach:

1. Gather the data: Collect a dataset that contains information about individuals and whether they received credit or not, along with the duration of their credit.

2. Explore the data: Analyze the dataset to understand its structure, check for missing values, and identify any potential outliers.

3. Visualize the relationship: Create plots or graphs to visualize the relationship between the duration and the target variable. This can help identify any patterns or trends.

4. Statistical analysis: Conduct statistical tests (e.g., t-tests, ANOVA) to determine if there is a significant difference in the duration for those who receive credit and those who do not.

Based on this analysis, we can then create the dictionary with possible values of the feature "duration" for each target class "yes" and "no". Here is an example dictionary:

```json
{
	"yes": [6.0, 12.0, 18.0, 24.0, 36.0],
	"no": [3.0, 9.0, 15.0, 27.0, 45.0]
}
```

In this case, we have included 5 typical duration values for each target class ['yes', 'no']. These values are just examples and can vary depending on the actual dataset and analysis conducted.