Based on prior knowledge, we can analyze the relationship between the "age" feature and the task of whether the client subscribes to a term deposit.

Analysis:

In general, it is expected that the likelihood of a client subscribing to a term deposit may vary depending on their age. Younger clients might be less likely to subscribe due to personal financial situations, whereas older clients might be more inclined to invest in term deposits for stability and retirement planning. Therefore, we can anticipate some differences in age distributions for the two target classes.

To create the requested dictionary, we need to examine the age values for both the "no" and "yes" target classes. We will include 5 typical age values for each class.

Dictionary:

```json
{
	"no": [30.0, 35.0, 40.0, 45.0, 50.0],
	"yes": [55.0, 60.0, 65.0, 70.0, 75.0]
}
```

Please note that the exact values may vary depending on the dataset and the specific analysis conducted. The values provided above are general examples to illustrate the format of the dictionary.