Based on prior knowledge, let's analyze the relationship between the feature "Age" and the task of determining if the coronary angiography of a patient shows a heart disease.

In general, age can be a significant factor in the development of heart diseases. As individuals age, there is an increased risk of developing various cardiac conditions due to factors such as accumulation of risk factors, degenerative changes in the cardiovascular system, and lifestyle factors. However, it is crucial to note that age alone cannot definitively determine the presence or absence of a heart disease; it is just one of many factors considered during diagnosis.

To complete the analysis, we need more specific information about the relationship between age and heart disease in the dataset or relevant medical research studies. Without this information, we can't make any specific conclusions about the correlation between age and a patient's coronary angiography accurately.

Nevertheless, let's assume that we have a dataset available with information on patients' ages and whether or not they have heart disease. Based on this dataset, we can generate a dictionary with sample values for the age feature for each target class:

```json
{
  "no": [45.2, 47.9, 51.3, 56.8, 60.5],
  "yes": [61.1, 64.6, 67.2, 70.9, 72.4]
}
```

Please note that these values are hypothetical examples and do not represent actual data. The purpose is to demonstrate the format and structure of the dictionary requested, assuming we have access to real-world data. In practice, more comprehensive analysis and investigation would be necessary to determine the relationship between age and the presence of heart disease accurately.