Based on prior knowledge, we can assume that age can be a significant factor in determining whether someone receives a credit or not. Younger individuals might have less credit history or income, while older individuals might have a lower likelihood of repayment due to potential health issues or retirement.

To analyze the relationship between age and receiving credit, we can look at typical age ranges for each target class ('yes' and 'no').

Here's the dictionary with typical age values for each target class:

```json
{
	"yes": [30.0, 35.0, 40.0, 45.0, 50.0],  
	"no": [25.0, 28.0, 32.0, 37.0, 42.0]  
}
```

This implies that individuals in the 30-50 age range are more likely to receive credit ('yes' class), while those in the 25-42 age range are less likely to receive credit ('no' class). These are just typical values, and the relationship between age and receiving credit might vary depending on the specific dataset and other factors.