Based on my prior knowledge, I would analyze the relationship between the feature "day" and the target variable "subscribe to a term deposit" as follows:

The "day" feature represents the last contact day of the month. Since there are only a limited number of days in a month, I would expect this feature to have a relatively equal distribution of values across both target classes.

However, it is possible that certain days of the month may have a higher likelihood of leading to a subscription. For example, individuals may be more likely to consider financial commitments towards the end of the month when they have a better understanding of their available funds. 

To analyze the relationship, I would first calculate the average or distribution of the "day" feature for both the "no" and "yes" target classes. I would then identify the typical day values for each target class based on the proportion of instances.

Based on this analysis, the dictionary would be:

```json
{
	"no": [3.0, 5.0, 9.0, 13.0, 20.0],
	"yes": [4.0, 14.0, 17.0, 21.0, 28.0]
}
```

These values are just examples and should be replaced with actual values obtained from the analysis of the dataset.