Based on prior knowledge, we can assume that the number of dependents may have an influence on whether a person receives credit or not. Here is a possible analysis of the relationship between the feature "num_dependents" and the task of determining if a person receives credit or not:

- If the number of dependents is very high, it may indicate a higher financial burden on the individual, potentially reducing their chances of receiving credit.
- On the other hand, if the number of dependents is very low, it may indicate a higher disposable income or fewer financial responsibilities, potentially increasing their chances of receiving credit.
- The relationship between the feature and the target variable may not be linear, and there might be an optimal range for the number of dependents where the chances of receiving credit are higher.

Based on the analysis, here is the dictionary with typical num_dependents values for each target class:

```json
{
	"yes": [0.0, 0.0, 1.0, 1.0, 2.0],
	"no": [3.0, 4.0, 4.0, 5.0, 6.0]
}
```

Please note that these values are just examples and may not accurately represent the actual relationship between the feature and the target variable. Further analysis and data exploration may be required to determine the true relationship.