Based on prior knowledge, we can assume that the higher the frequency (total number of donations), the more likely the person is to have donated blood. To analyze the relationship between the feature "Frequency" and the target variable "Did the person donate blood?", we can analyze the distribution of the feature values for each target class.

Here is a dictionary with the requested format:

```json
{
	"yes": [10.0, 9.0, 8.0, 7.0, 11.0],
	"no": [2.0, 1.0, 3.0, 4.0, 5.0]
}
```

For the target class "yes", the feature values for Frequency could be [10.0, 9.0, 8.0, 7.0, 11.0]. These values represent individuals who have donated blood.

For the target class "no", the feature values for Frequency could be [2.0, 1.0, 3.0, 4.0, 5.0]. These values represent individuals who have not donated blood.

Note that these values are just examples and may not reflect the real distribution, as actual data is required to perform a more accurate analysis.