anchor.diff:  [[ml-gs-sampledata]]
   ==== Obtaining a Sample Data Set
   
  -In this step we will upload some sample data to Elasticsearch. This is standard
  -Elasticsearch functionality, and is needed to set the stage for using {ml}.
  +In this step we will upload some sample data to {es}. This is standard
  +{es} functionality, and is needed to set the stage for using {ml}.
   
   The sample data for this tutorial contains information about the requests that
   are received by various applications and services in a system. A system
  
positive.code: //granularity is a bucket, but if you have a big time range selected, then they will span many buckets
  
  Single Metric Viewer::
    This view contains a chart that represents the actual and expected values over time. 
    This is only available for jobs which analyze a single time series
    and where `model_plot_config` is enabled.
    As in the **Anomaly Explorer**, anomalous data points are shown in
    different colors depending on their score.
  
  [float]
  [[ml-gs-job1-analyze]]
  ==== Exploring Single Metric Job Results
  
  By default when you view the results for a single metric job,
  the **Single Metric Viewer** opens:
  image::images/ml-gs-job1-analysis.jpg["Single Metric Viewer for total-requests job"]
  
  The blue line in the chart represents the actual data values. 
  The shaded blue area represents the bounds for the expected values. 
  The area between the upper and lower bounds are the most likely values for the model. 
  If a value is outside of this area then it can be said to be anomalous.
  //TBD: What is meant by "95% prediction bounds"? Because we are using probability 
  //to "predict" the values.. 
  
  If you slide the time selector from the beginning of the data to the end of the
  data, you can see how the model improves as it processes more data. At the
  
negative.code: 
  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, 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
  you want to analyze all of the existing data, click
  **Use full transaction_counts data**. If you want to see what happens when you
  stop and start data feeds and process additional data over time, click the time
  picker in the {kib} toolbar. Since the sample data spans a period of time
  between March 26, 2017 and April 22, 2017, click **Absolute**. Set the start
  time to March 26, 2017 and the end time to April 1, 2017, for example. Once
  you've got the time range set up, click the **Go** button.
  