To analyze the relationship between the feature "Sex" and the task of determining if the coronary angiography shows a heart disease, we can look at the distribution of the "Sex" feature within each target class.

Based on prior knowledge, the presence of heart disease can be influenced by various factors such as age, lifestyle, genetics, etc. It is possible that the "Sex" of the patient may also play a role in the development of heart disease. For example, certain heart diseases may have a higher prevalence in one sex compared to the other.

To generate the required dictionary, we need to examine the distribution of "Sex" within each target class (yes: coronary angiography shows heart disease, no: coronary angiography does not show heart disease) and list the possible values of "Sex" for each class.

Let's assume we have the following data:

| Sex | Target |
|-----|--------|
|  M  |   No   |
|  F  |  Yes   |
|  M  |   No   |
|  M  |  Yes   |
|  F  |   No   |
|  F  |   No   |

By analyzing the data, we can generate the following dictionary:

```json
{
	"no": ["M"], 
	"yes": ["F"]
}
```

The dictionary indicates that for the target class "no" (coronary angiography does not show heart disease), the possible values of the "Sex" feature are "M". For the target class "yes" (coronary angiography shows heart disease), the possible value of the "Sex" feature is "F".

This analysis suggests that in the provided data, males (M) are associated with a higher likelihood of not having heart disease, while females (F) are associated with a higher likelihood of having heart disease.