Based on prior knowledge, a higher maximum heart rate achieved (MaxHR) is generally associated with a higher likelihood of having a heart disease. However, it is important to note that there can be individual variations, and MaxHR alone may not be sufficient to determine the presence or absence of heart disease.

To analyze the relationship between MaxHR and the target variable (heart disease detection), we need access to a dataset where MaxHR values and corresponding coronary angiography results are available. By examining this dataset, we can identify typical MaxHR values for each target class.

Assuming we have access to such a dataset, here is an example dictionary representing the relationship between MaxHR and the target variable:

```json
{
    "no": [160, 163, 157, 152, 156],
    "yes": [175, 180, 168, 172, 178]
}
```

In this example, the "no" category represents patients without heart disease, and the "yes" category represents patients with heart disease. The provided MaxHR values are considered typical for each class, but it's important to note that these are just examples and may not accurately represent the actual data distribution.

Keep in mind that this analysis is based on prior knowledge and hypothetical data. To obtain more accurate and reliable information about the relationship between MaxHR and heart disease detection, it is crucial to analyze a real dataset specific to the task.