Based on prior knowledge, we can analyze the relationship between the RestingECG feature and the presence of a heart disease as determined by coronary angiography.

RestingECG is a categorical variable with three possible categories: 'Normal', 'ST', and 'LVH'. To determine its relationship with the presence of a heart disease, we need to compare the distribution of RestingECG categories for patients with and without heart disease.

To perform this analysis, we would ideally have a dataset with RestingECG values and corresponding coronary angiography results. By comparing the distribution of RestingECG categories for patients with heart disease (target class 'yes') and without heart disease (target class 'no'), we can infer the relationship between the feature and the target.

Without the dataset, we can make some general observations based on prior knowledge:

- It is possible that 'Normal' RestingECG results are more common among patients without heart disease ('no' class), as a normal resting electrocardiogram may indicate a healthy heart.
- 'ST' abnormalities in the RestingECG could suggest myocardial ischemia or injury, which may be indicative of heart disease. Therefore, 'ST' RestingECG results might be more common among patients with heart disease ('yes' class).
- 'LVH' (Left Ventricular Hypertrophy) is a condition characterized by the enlargement of the left ventricle of the heart. It may be associated with various cardiovascular diseases. However, it is not specific to heart disease, so its relationship with the target class might be less straightforward.

Based on these considerations, let's generate the dictionary:

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

In the absence of a dataset, this dictionary assumes that patients with 'Normal' RestingECG results are more likely to have no heart disease, while patients with 'ST' or 'LVH' RestingECG results are more likely to have heart disease.

It's important to note that without specific data and statistical analysis, this is a general assumption and might not hold true in all cases. Therefore, it is always recommended to perform a thorough analysis on real data to validate these assumptions.