To analyze the relationship between the feature "Time" and the target variable "Did the person donate blood?", we can use our prior knowledge to determine the appropriate ranges of values for the feature and observe how it affects the target.

Assuming that "Time" represents the number of months since the first donation, we can make the following observations:

1. For the target class "yes" (indicating that the person donated blood), we can expect the feature "Time" to generally be higher. People who have donated blood multiple times are more likely to donate again. Typical values for "Time" in this class can vary depending on the dataset, but a few possible values could be [9.0, 12.0, 18.0, 24.0, 36.0].

2. For the target class "no" (indicating that the person did not donate blood), we can expect the feature "Time" to generally be lower. People who have not donated blood recently are less likely to donate again. Typical values for "Time" in this class can also vary, but a few possible values could be [3.0, 6.0, 12.0, 15.0, 21.0].

Based on this analysis, we can create a dictionary as follows:

```json
{
	"yes": [9.0, 12.0, 18.0, 24.0, 36.0],
	"no": [3.0, 6.0, 12.0, 15.0, 21.0]
}
```

Please note that these values are just examples and might differ depending on the specific dataset and its underlying patterns.