Based on prior knowledge, we can analyze the relationship between the feature fibr_ter_01 and the target variable of chronic heart failure. Since the feature fibr_ter_01 represents the use of Fibrinolytic therapy by Celiasum 750k IU, we can assume that it may have an impact on the development of chronic heart failure.

To analyze the relationship, we need to examine the values of fibr_ter_01 for both the target classes of chronic heart failure (yes) and non-chronic heart failure (no).

Let's assume we have a dataset with the feature fibr_ter_01 and the target variable chronic_heart_failure. The values of fibr_ter_01 for each target class can be analyzed as follows:

For chronic heart failure (yes):
- "no": In this case, the patient did not receive Fibrinolytic therapy by Celiasum 750k IU.

For non-chronic heart failure (no):
- "yes": In this case, the patient received Fibrinolytic therapy by Celiasum 750k IU.

Based on the above analysis, we can create a dictionary with the following format:

```json
{
	"no": ["no"],
	"yes": ["yes"]
}
```

In this case, we only have a single value for each target class and feature combination, but this format allows for multiple possible values in other cases.