Interpreting anomaly detection results

Our content about how to interpret anomaly detection job is currently spread about and it would be useful to see if it can be improved by pulling it together into a procedure that (ideally) could then be re-used in whole or in part in multiple solutions.

It should include:

- Differences between Anomaly Explorer and Single Metric Viewer (covered at high level in [tutorial](https://www.elastic.co/guide/en/machine-learning/current/ml-gs-results.html))
- Information about what you can glean from [influencers](https://www.elastic.co/guide/en/machine-learning/current/ml-influencers.html)
- Interpreting [multi-bucket](https://www.elastic.co/guide/en/machine-learning/current/ml-buckets.html#ml-bucket-results) anomalies
- A summary of the different types of results (e.g. model plot results, [influencer results](https://www.elastic.co/guide/en/machine-learning/current/ml-influencers.html#ml-influencer-results), [bucket results](https://www.elastic.co/guide/en/machine-learning/current/ml-buckets.html#ml-bucket-results), [record results](https://www.elastic.co/guide/en/elasticsearch/reference/7.4/ml-results-resource.html)
- Interpreting anomaly scores and how they are calculated and how they differ from probability.(covered at high level in [tutorial](https://www.elastic.co/guide/en/machine-learning/current/ml-gs-results.html))
- Meaning of "actual" and "typical" values (covered at high level in [tutorial](https://www.elastic.co/guide/en/machine-learning/current/ml-gs-results.html))

Comienza con el tutorial de resultados enlazado y las páginas enlazadas sobre influencers, anomalías de varios buckets y resultados de ML. Consolida el material solicitado en un procedimiento reutilizable que cubra los visores, los tipos de resultados, las puntuaciones y probabilidades, los valores reales y típicos, los influencers y las anomalías de varios buckets.

Escrito por el modelo de indexación a partir del texto del issue.