Based on prior knowledge, the feature `pdays` represents the number of days passed since the client was last contacted from a previous campaign. To analyze the relationship between this feature and the task of whether the client subscribes to a term deposit or not, we can consider the following:

1. If the `pdays` value is very high (e.g., 999), it indicates that the client was not contacted previously. Thus, there may be a higher chance of subscribing to a term deposit (target class 'yes').
2. If the `pdays` value is relatively low (e.g., 0-10), it implies that the client has been recently contacted. Hence, the likelihood of subscription (target class 'yes') may be lower.
3. If the `pdays` value is negative, it could indicate that the client was contacted before the previous campaign. This scenario might apply to new clients or prospects who have not been contacted for a long time. Therefore, the chances of subscription (target class 'yes') may increase.
4. If the `pdays` value is within a medium range (e.g., 20-30), it might signify that the client has been contacted in the past but not too recently. In this case, the subscription probability could be moderate or vary depending on other factors.
5. If the `pdays` value is missing or unknown (e.g., NaN or 0), it suggests that the client has not been contacted previously. This condition may lead to a higher chance of subscription (target class 'yes').

Considering the above observations, let's create the desired dictionary:

```json
{
	"no": [-1.0, 0.0, 10.0, 15.0, 999.0],
	"yes": [1.0, 2.0, 7.0, 20.0, 30.0]
}
```
In this dictionary, we include 5 typical values for each target class ('no' and 'yes') based on the relationship analysis performed. The values are represented as floats in lists.