Based on prior knowledge, the feature "day" represents the last contact day of the month. To analyze its relationship with the target variable, "Does this client subscribe to a term deposit?", we can examine the distribution of the feature for each class.

To create the dictionary, we need to identify 5 typical day values for each target class. Here is an analysis of the relationship between the "day" feature and the target variable:

For target class "no":
- The feature values range from 1 to 31.
- Typically, clients who do not subscribe to a term deposit have last contact days spread across the month.
- Examples of typical day values for target class "no" could be: [5.0, 10.0, 15.0, 20.0, 25.0].

For target class "yes":
- The feature values range from 1 to 31.
- Typically, clients who do subscribe to a term deposit have last contact days spread across the month.
- Examples of typical day values for target class "yes" could be: [3.0, 7.0, 14.0, 21.0, 28.0].

Now, let's create the dictionary:

```json
{
	"no": [5.0, 10.0, 15.0, 20.0, 25.0],
	"yes": [3.0, 7.0, 14.0, 21.0, 28.0]
}
```