Based on the description, the feature "day" represents the last contact day of the month. To analyze the relationship between this feature and the task of whether the client subscribes to a term deposit, we can look at possible patterns or trends in the data.

One hypothesis could be that there may be certain days of the month when clients are more likely to subscribe to a term deposit. For example, clients with higher incomes might prefer to be contacted towards the end of the month after receiving their monthly paycheck.

To generate the dictionary with specific details, we need to analyze the feature "day" in relation to the target variable. Here is an example of the dictionary with 5 typical values for each target class:

```json
{
  "no": [5.0, 10.0, 15.0, 20.0, 25.0],
  "yes": [7.0, 12.0, 17.0, 22.0, 27.0]
}
```

This dictionary suggests that clients who do not subscribe to a term deposit tend to have last contact days around the 5th, 10th, 15th, 20th, and 25th of the month. On the other hand, clients who do subscribe to a term deposit have last contact days around the 7th, 12th, 17th, 22nd, and 27th of the month.

Please note that the actual values in the dictionary are just examples and should be based on the analysis of the dataset. Additionally, other patterns or trends may exist, and further analysis is recommended to fully understand the relationship between the feature "day" and the target variable.