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16-1, which is a graphical display of a quality characteristic that has been measured or computed from a sample versus the sample number or time Often, the samples are selected at periodic intervals such as every hour The chart contains a center line (CL) that represents the average value of the quality characteristic corresponding to the in-control state (That is, only chance causes are present) Two other horizontal lines, called the upper control limit (UCL) and the lower control limit (LCL), are also shown on the chart These control limits are chosen so that if the process is in control, nearly all of the sample points will fall between them In general, as long as the points plot within the control limits, the process is assumed to be in control, and no action is necessary.

Public Class EmployeeData Inherits Employee Public Sub New(ByVal EID As Integer, _ ByVal LastName As String, _ ByVal FirstName As String, _ ByVal Salary As Decimal) MyBase.New(EID, LastName, FirstName, Salary) End Sub Public ReadOnly Property EmployeeID() As Integer Get Return EID End Get End Property End Class

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However, a point that plots outside of the control limits is interpreted as evidence that the process is out of control, and investigation and corrective action are required to nd and eliminate the assignable cause or causes responsible for this behavior The sample points on the control chart are usually connected with straight-line segments so that it is easier to visualize how the sequence of points has evolved over time Even if all the points plot inside the control limits, if they behave in a systematic or nonrandom manner, this is an indication that the process is out of control For example, if 18 of the last 20 points plotted above the center line but below the upper control limit and only two of these points plotted below the center line but above the lower control limit, we would be very suspicious that something was wrong.

If the process is in control, all the plotted points should have an essentially random pattern Methods designed to nd sequences or nonrandom patterns can be applied to control charts as an aid in detecting out-of-control conditions A particular nonrandom pattern usually appears on a control chart for a reason, and if that reason can be found and eliminated, process performance can be improved There is a close connection between control charts and hypothesis testing Essentially, the control chart is a test of the hypothesis that the process is in a state of statistical control A point plotting within the control limits is equivalent to failing to reject the hypothesis of statistical control, and a point plotting outside the control limits is equivalent to rejecting the hypothesis of statistical control..

This class could be used to create an EmployeeData collection instead of the Employee collection. The EmployeeData collection could be assigned to the data source of the DataGrid.

We give a general model for a control chart. Let W be a sample statistic that measures some quality characteristic of interest, and suppose that the mean of W is W and the standard deviation of W is W.* Then the center line, the upper control limit, and the lower control limit become

(16-1)

This is one example method of populating the Employee ID column. Another method of populating the Employee ID column is to use an objectoriented approach as described in the sidebar titled Object-Oriented Method to Display Hidden Data in a DataGrid.

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