anchor.diff:  
   The bucket span has a significant impact on the analysis. When you're trying to
   determine what value to use, take into account the granularity at which you
  -want to perform the analysis, the frequency of the input data, and the frequency
  -at which alerting is required.
  -//TBD: Talk about overlapping buckets? "To avoid this, you can use overlapping
  -//buckets (how/where?). We analyze the data points in two buckets simultaneously,
  -//one starting half a bucket span later than the other. Overlapping buckets are
  -//only beneficial for aggregating functions, and should not be used for
  -//non-aggregating functions.
  +want to perform the analysis, the frequency of the input data, the duration of typical anomalies
  +and the frequency at which alerting is required.
   --
   
   . Determine whether you want to process all of the data or only part of it. If
  
positive.code: data feeds, and results.
  
  By default, you can perform all of the steps in this tutorial by using the
  built-in `elastic` user. If you are performing these steps in a production
  environment, take extra care because that user has the `superuser` role and you
  could inadvertently make significant changes to the system. You can
  alternatively assign the `machine_learning_admin` and `kibana_user` roles to a
  
negative.code: analytical task.
  
  NOTE: This tutorial uses Kibana to create jobs and view results, but you can
  alternatively use APIs to accomplish these tasks.
  For API reference information, see <<ml-apis>>.
  
  To work with jobs in Kibana:
  