Main Screen Features 
Every aspect of the measurement uncertainty analysis process can be accessed from the Main Screen. Various options, functions and features include a menu bar, an analysis path checklist for quick access to drill-down analysis screens and worksheets, a section containing key analysis information, a table containing a list of measurement error components and related fields for entering and display associated uncertainty information, the total combined uncertainty display, a combined error distribution plot and a section that displays parameter bias estimates obtained from Bayesian methods.

The Menu bar provides access to a wide variety of program functions and options including saving and opening analysis files, printing options, editing options, opening and updating the measurement units, selecting distribution plot options, viewing a pareto chart of error source contributions to overall uncertainty, and running external applications.   
 
The user-interactive Analysis Path checklist, located on the left side of the Main Screen, provides a structured walk-through of the basic steps in analyzing uncertainty for typical measurement situations.  Selecting any one of the listed items launches the appropriate worksheet or screen. 

The Analysis Details section of the screen is where you enter the analysis title, select the measurement area, nominal units and uncertainty units for your analysis.  A nominal value can also be entered if applicable.
 
The Error Component table lists the primary error components common measurement uncertainty analysis: Subject Parameter (or Measurand), Measuring Parameter, Measuring Environment and Operator.   including them in the total, combined uncertainty.  The table contains columns for entering data, displaying computed results, and selecting which error components to include in the combined uncertainty estimate.  Clicking on the desired Error Component button activates the appropriate drill-down screen or worksheet for entering data and estimating bias uncertainty, resolution uncertainty, repeatability uncertainty, etc. 
 

The Parameter Bias Estimates section of the screen displays the refined estimates of the biases and in-tolerance probabilities for the measuring parameter and subject parameter obtained from Bayesian analysis or SMPC (statistical measurement process control).  In measurement situations where we have a priori knowledge of measuring parameter and subject parameter statistics, the roles of measuring parameter and subject parameter are reversible.  This means that, with SMPC, we can use information from a measurement result to estimate the values or biases of both the subject parameter and the measuring parameter. Using SMPC, we can also estimate the uncertainties in these values or biases.