Based on prior knowledge, we can hypothesize that the number of dependents could potentially be related to the likelihood of a person receiving credit. Typically, having more dependents would imply higher financial responsibilities and could potentially affect a person's ability to repay debts. However, this relationship may vary depending on the specific context or dataset.

To analyze the relationship between the feature "num_dependents" and the target variable "Does this person receive a credit? Yes or no?", we can examine the distribution of the feature for each target class. Based on this analysis, we can create a dictionary containing the range of values for the feature "num_dependents" for each target class.

Here is the dictionary:

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

In this dictionary, we have included 5 typical values for the "num_dependents" feature for each target class. However, please note that these values are hypothetical and may vary depending on the specific dataset or context.