Based on prior knowledge, the RestingECG feature is a categorical variable that represents the results of a resting electrocardiogram. The feature can take three possible values: 'Normal', 'ST', and 'LVH'. 

To analyze the relationship between the RestingECG feature and the presence of heart disease shown in coronary angiography, we can inspect the distribution of RestingECG values for each target class (yes and no). If there are discernible patterns or significant differences in the distribution of RestingECG values between the two target classes, we can infer that the RestingECG feature may be relevant in predicting heart disease indicated by coronary angiography.

Let's assume we have a dataset with the RestingECG feature and the target variable (heart disease) for multiple patients who have undergone coronary angiography. We can analyze the distribution of RestingECG values for each class (yes and no) to determine their relationship.

Here's the dictionary that represents the relationship between the RestingECG feature and the target variable:

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

In this case, patients who have a RestingECG result of 'Normal' are classified as 'no' (no heart disease indicated), while patients with 'ST' or 'LVH' RestingECG results are classified as 'yes' (heart disease indicated).

Please note that this analysis is based on hypothetical assumptions and should be validated with real-world data.