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Private Sub AddEmployee() Const newId As String = *NEW ID* dgEmployee.EditItemIndex = 0 Get the DataTable from the Session. Dim dt As DataTable = CType(Session( Employee ), DataTable) Get the DataRow to be updated. Dim dr As DataRow = dt.Rows.Find(newId) If dr Is Nothing Then dr = dt.NewRow() Start editing this row. dr.BeginEdit() This is better with an autonumber key. dr( emp_id ) = newId dt.Rows.Add(dr) Else

P 0.000 0.000 87.1%

2 3 4

Analysis of Variance Source Regression Residual Error Total DF 1 18 19 SS 152.13 21.25 173.38 MS 152.13 1.18 F 128.86

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In this lab, you create a Visual Basic .NET component that can be reused in many applications. This component is installed into the Global Assembly Cache, and configured to be available from the .NET tab of the Add Reference dialog box. Adding the DataComponent Project In this section, you add a new Visual Basic .NET Class Library project to the existing solution. This project compiles to a .dll file. 1. To start this lab, open the OrderEntrySystemSolution from Lab 14.1. Right-click the OrderEntrySystemSolution in the Solution Explorer, and click Check Out. 2. Right-click on the OrderEntrySystemSolution, and click Add, New Project. For the project name, type DataComponent. Be sure to verify that the project is located under the OrderEntrySystemSolution folder. 3. Rename Class1.vb to Db.vb, and open the class file. Rename Class1 to Db. 4. Add the Imports System.Data.SqlClient statement to the top of the code. 5. In the Db class, add a method called ExecuteDataSet. This method requires parameters for the connection, command type, and command text, and an array of command parameters. The return type is a DataSet. 6. In the body of the ExecuteDataSet function, add code to create a new SqlCommand. Assign the connection, command type, and the command text to the command. 7. Execute a call to a method that hasn t been defined yet, called AddParameters. This method requires the command and the parameter array as arguments.

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Predicted Values for New Observations New Obs 1 Fit 89.231 SE Fit 0.354 95.0% CI (88.486, 89.975) 95.0% PI (86.830, 91.632)

an estimate of the true population mean purity when x 1.00%, or as an estimate of a new observation when x = 1.00%. These estimates are, of course, subject to error; that is, it is unlikely that a future observation on purity would be exactly 89.23% when the hydrocarbon level is 1.00%. In subsequent sections we will see how to use con dence intervals and prediction intervals to describe the error in estimation from a regression model. Estimating 2 There is actually another unknown parameter in our regression model, 2 (the variance of the error term ). The residuals ei yi yi are used to obtain an estimate of 2. The sum of squares of the residuals, often called the error sum of squares, is

(11-12) (n 2) 2.

We can show that the expected value of the error sum of squares is E(SSE) Therefore an unbiased estimator of 2 is

8. In the ExecuteDataSet method, create a new DataSet and SqlDataAdapter. Fill the dataset and return the dataset. The completed code should look like the following:

(11-13)

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