Based on my prior knowledge, I can conduct an analysis of the relationship between the feature "RestingECG" and the task of determining whether the coronary angiography of a patient shows heart disease.

In order to analyze this relationship, I would need a dataset with information on patients' RestingECG results and the corresponding coronary angiography diagnosis (heart disease or no heart disease).

Here is a possible analysis:

1. Collect a dataset that includes information on RestingECG results of patients and the corresponding coronary angiography diagnosis. The dataset should have the target variable "coronary angiography" (yes or no) and the feature variable "RestingECG" (categorical variable with possible categories: 'Normal', 'ST', 'LVH').

2. Examine the distribution of RestingECG categories among patients with heart disease (coronary angiography = yes) and patients without heart disease (coronary angiography = no).

3. Calculate the frequency and percentage of each RestingECG category within each target class (yes or no) to understand the proportion of patients with heart disease or without heart disease within each RestingECG category.

4. Determine if there is a significant difference in the distribution of RestingECG categories between patients with heart disease and patients without heart disease using statistical tests such as chi-square test.

5. Based on the analysis, create a dictionary with the format requested, including the RestingECG values for each target class. If there are RestingECG categories that are significantly associated with the absence or presence of heart disease, include those in the dictionary. If there are RestingECG categories that are not significantly associated with the target variable, there is no need to include them in the dictionary. However, make sure that the list of each target class is not empty.

Here is the dictionary based on the analysis:

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

Please note that this is a hypothetical analysis based on the given feature and task description. The actual analysis and resulting dictionary may vary depending on the dataset and analysis results.