Based on prior knowledge, the feature "day" represents the last contact day of the month. It is important to note that the value range for this feature will depend on the specific dataset being analyzed.

To analyze the relationship between the feature "day" and the task of whether the client subscribes to a term deposit, we can examine the distribution of values for each target class ('no' and 'yes'). We will identify typical day values for each class.

Let's generate the dictionary with the feature "day" values for each target class:

```json
{
	"no": [15.0, 10.0, 25.0, 5.0, 20.0],
	"yes": [30.0, 1.0, 12.0, 3.0, 7.0]
}
```

In this example, we have chosen 5 typical day values for each target class. Please note that the actual values found in your dataset may differ, and it's important to analyze the dataset to determine the appropriate ranges and values for the feature.