python ocr library windows

python ocr library windows

python ocr library windows

python ocr library windows

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as shown on the control chart. These are the 3-sigma control limits referred to above. Note that the use of 3-sigma limits implies that 0.0027; that is, the probability that the point plots outside the control limits when the process is in control is 0.0027. The width of the control limits is inversely related to the sample size n for a given multiple of sigma. Choosing the control limits is equivalent to setting up the critical region for testing the hypothesis H0: H1: 74 74

Private Sub DataGrid1_ItemDataBound(ByVal sender As Object, _ ByVal e As System.Web.UI.WebControls.DataGridItemEventArgs) _ Handles DataGrid1.ItemDataBound Dim EidCol As Integer EidCol = Integer.Parse(DataGrid1.Attributes( EidCol )) If TypeOf e.Item.DataItem Is Employee Then Dim currentEmployee As Employee = _ CType(e.Item.DataItem, Employee) e.Item.Cells(EidCol).Text = _ String.Format( {0:D3} , currentEmployee.EID) End If End Sub

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where 0.01 is known. Essentially, the control chart tests this hypothesis repeatedly at different points in time. In designing a control chart, we must specify both the sample size to use and the frequency of sampling. In general, larger samples will make it easier to detect small shifts in the process. When choosing the sample size, we must keep in mind the size of the shift that we are trying to detect. If we are interested in detecting a relatively large process shift, we use smaller sample sizes than those that would be employed if the shift of interest were relatively small. We must also determine the frequency of sampling. The most desirable situation from the point of view of detecting shifts would be to take large samples very frequently; however, this is usually not economically feasible. The general problem is one of allocating sampling effort. That is, either we take small samples at short intervals or larger samples at longer intervals. Current industry practice tends to favor smaller, more frequent samples, particularly in high-volume manufacturing processes or where a great many types of assignable causes can occur. Furthermore, as automatic sensing and measurement technology develops, it is becoming possible to greatly increase frequencies. Ultimately, every unit can be tested as it is manufactured. This capability will not eliminate the need for control charts because the test system will not prevent defects. The increased data will increase the effectiveness of process control and improve quality.

16-4.3

Figure 6.13 shows the browser output. The first statement in this code turned off the automatic generation of the columns. If this setting were not set to false, the manual columns and the autogenerated columns would be displayed. The statements that follow set up each of the columns in order. Each column has additional formatting to set the width of the column and the alignment of the text. The last part of the code is the ItemDataBound event method. This only contains code to assign the employee ID, because the other columns were easily bound by their field name in the InitDataBind method. Another interesting item is the persistence of the column numbers. After the column was added, the column number is persisted to an attribute in the DataGrid. This means that a peek at the browser source will reveal these attributes on DataGrid1 s table. This attributes can be retrieved when binding, selecting, editing, and updating the data. The benefit of this approach is realized when more columns are added. There is no need to update the column numbers throughout the code.

A fundamental idea in the use of control charts is to collect sample data according to what Shewhart called the rational subgroup concept. Generally, this means that subgroups or samples should be selected so that to the extent possible, the variability of the observations within a subgroup should include all the chance or natural variability and exclude the assignable variability. Then, the control limits will represent bounds for all the chance variability and not the assignable variability. Consequently, assignable causes will tend to generate points that are outside of the control limits, while chance variability will tend to generate points that are within the control limits. When control charts are applied to production processes, the time order of production is a logical basis for rational subgrouping. Even though time order is preserved, it is still possible to form subgroups erroneously. If some of the observations in the subgroup are taken at the end of one 8-hour shift and the remaining observations are taken at the start of the next 8-hour shift,

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