To analyze the relationship between the Recency feature and the task of whether a person donated blood or not, we would consider the following factors:

1. Prior Knowledge: Based on previous studies or domain expertise, we might expect a correlation between the Recency feature and the likelihood of donation. Generally, if the time since the last donation is shorter, the probability of donating blood again might increase.

2. Hypothesis Testing: We can perform hypothesis testing to determine if there is a significant difference in the average Recency values between those who donated blood and those who did not. This would provide statistical evidence of the relationship between the feature and the target variable.

Based on these considerations, let's create the dictionary:

```json
{
	"yes": [2.0, 5.0, 7.0, 9.0, 11.0],
	"no": [12.0, 16.0, 20.0, 22.0, 25.0]
}
```

In this example, we assume that lower Recency values between 2 and 11 months are associated with individuals who donated blood ('yes' class), while higher Recency values between 12 and 25 months are associated with individuals who did not donate blood ('no' class). The specific values are just examples and can vary based on the dataset and analysis.