Based on prior knowledge, we can assume that the number of dependents may have an impact on whether a person receives credit or not. Generally, individuals with more dependents may have higher financial responsibilities and therefore may be less likely to receive credit. 

To analyze the relationship between the feature "num_dependents" and the target variable "Does this person receive credit? (Yes or No)", we can consider the following steps:

1. Obtain the dataset with the relevant variables.
2. Divide the dataset into two groups based on the target variable: "Yes" and "No".
3. For each group, calculate the distribution or frequency of the "num_dependents" variable and observe the possible values.
4. Identify the typical values for "num_dependents" in each target class, specifically choosing five values for both "Yes" and "No" groups.
5. Compile the analysis into a dictionary with the required format.

Here's the dictionary with the analysis:

```json
{
	"yes": [0.0, 1.0, 2.0, 3.0, 4.0],
	"no": [0.0, 1.0, 2.0, 3.0, 4.0]
}
```

Please note that the values provided in the dictionary are just examples and should be adjusted based on your actual dataset.