Based on prior knowledge, resting electrocardiogram (ECG) results can provide information about the electrical activity of the heart and can be used to detect various heart conditions. In the context of this task, we can analyze the relationship between the RestingECG feature and the presence of heart disease indicated by the coronary angiography.

To do this analysis, we would need a dataset with records that include both the RestingECG values and the corresponding coronary angiography results. Without the dataset, we cannot provide concrete insights into the relationship between RestingECG and heart disease. However, let's assume we have access to a dataset.

Assuming we have the dataset, we can analyze the RestingECG feature by comparing its values for the cases where the coronary angiography indicates heart disease (target class "yes") and those where it does not (target class "no").

Here's an example of how the dictionary might look based on our analysis:

```json
{
	"no": ["Normal"],
	"yes": ["ST", "LVH"]
}
```

In this example, the RestingECG feature values "ST" and "LVH" are associated with the presence of heart disease, as indicated by the coronary angiography. The value "Normal" for RestingECG is associated with the absence of heart disease.

Please note that this is a hypothetical example and the actual relationship between the RestingECG feature and the presence of heart disease can vary depending on the dataset and the specific context. Without access to the dataset, it is not possible to generate a more accurate dictionary.