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Click the Help on this function link in the Insert Function or Function Arguments dialog box to access Excel s help files.The function s help includes an example of the function being used and tips about how to use the function.

where XY is the covariance between Y and X. The conditional distribution of Y for a given value of X fY 0 x 1 y2 where

(11-36)

8

(11-37) (11-38) x is (11-39)

(11-40)

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Deleting a DataRow with the DataGrid is a relatively easy task. A Delete button column and a little bit of code are needed to handle the button. The new Delete button code follows:

and variance 2 0 x . Thus, the mean of the conditional distribution of Y given X x is a Y simple linear regression model. Furthermore, there is a relationship between the correlation 0, then 1 0, which coef cient and the slope 1. From Equation 11-38 we see that if implies that there is no regression of Y on X. That is, knowledge of X does not assist us in predicting Y. The method of maximum likelihood may be used to estimate the parameters 0 and 1. It can be shown that the maximum likelihood estimators of those parameters are

X 1

(11-41)

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Dim colDel As New ButtonColumn() colDel.CommandName = Delete colDel.Text = Delete colDel.ButtonType = ButtonColumnType.PushButton colDel.ItemStyle.Width = New Unit(colWidth, UnitType.Pixel) dgEmployee.Columns.Add(colDel)

(11-42)

We note that the estimators of the intercept and slope in Equations 11-41 and 11-42 are identical to those given by the method of least squares in the case where X was assumed to be a mathematical variable. That is, the regression model with Y and X jointly normally distributed is equivalent to the model with X considered as a mathematical variable. This follows because the random variables Y given X x are independently and normally distributed with 2 mean 0 1x and constant variance Y 0 x . These results will also hold for any joint distribution of Y and X such that the conditional distribution of Y given X is normal. It is possible to draw inferences about the correlation coef cient in this model. The estimator of is the sample correlation coef cient a Yi 1Xi

This code adds a ButtonColumn and sets the button type to a PushButton. The next code snippet shows the code for the Delete button.

2 12

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One of the most popular Excel functions is the AutoSum function. AutoSum automatically totals the contents of cells. For example, you can quickly total a column of sales figures. One way to use AutoSum is to select a cell and let the function guess which surrounding cells you want to total. Alternatively, you can specify exactly which cells to sum.

(11-43)

Private Sub dgEmployee_DeleteCommand( _ ByVal source As Object, _ ByVal e As System.Web.UI.WebControls.DataGridCommandEventArgs) _ Handles dgEmployee.DeleteCommand Get the DataTable from the Session. Dim dt As DataTable = CType(Session( Employee ), DataTable) Get the DataRow to be updated. Dim PrimaryKey As String = dgEmployee.DataKeys(e.Item.DataSetIndex) Dim dr As DataRow = dt.Rows.Find(PrimaryKey) dr.CancelEdit() Delete the row. dr.Delete() dt.DefaultView.RowFilter = dgEmployee.EditItemIndex = -1 dgEmployee.SelectedIndex = -1 BindTable() End Sub

(11-44)

so the slope 1 is just the sample correlation coef cient R multiplied by a scale factor that is the square root of the spread of the Y values divided by the spread of the X values.Thus, and R are closely related, although they provide somewhat different information. The 1 sample correlation coef cient R measures the linear association between Y and X, while 1 measures the predicted change in the mean of Y for a unit change in X. In the case of a mathematical variable x, R has no meaning because the magnitude of R depends on the choice of spacing of x. We may also write, from Equation 11-44, R2

This code locates the DataRow and marks it for deletion. The last items ensure that the RowFilter is clear and no Item is being edited. The completed DataGrid with the Delete button is shown in Figure 8.11.

which is just the coef cient of determination. That is, the coef cient of determination R2 is just the square of the correlation coef cient between Y and X. It is often useful to test the hypotheses H0 : H1: 21 0 0 (11-45)

2 R2

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