Based on prior knowledge, we can analyze the relationship between the feature "fibr_ter_03" and the target variable "chronic heart failure" to determine if there is a correlation.

To conduct the analysis, we need access to a dataset that includes both the "fibr_ter_03" feature and the target variable ("yes" or "no" for chronic heart failure). With this data, we can determine how the different values of "fibr_ter_03" relate to the presence or absence of chronic heart failure.

If the dataset is available, we can compute the frequency distribution of the feature values for each class of the target variable. This will help us understand the relationship between the feature and the target.

For example, if we have the following data:

| fibr_ter_03 | C. Heart Failure |
|-------------|-----------------|
| no          | no              |
| no          | no              |
| yes         | no              |
| no          | yes             |
| yes         | yes             |

Based on this data, we can create the following dictionary:

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

In this case, we only have one unique value for each target class, so the lists contain only a single element each.

If there are more distinct values for "fibr_ter_03" within each target class, we would include all those values in the respective lists.

Please provide the dataset or additional information if available, so we can conduct a more precise analysis and provide an accurate answer.