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The residuals from a regression model are ei yi yi, i 1, 2, p , n , where yi is an actual observation and yi is the corresponding tted value from the regression model. Analysis of the residuals is frequently helpful in checking the assumption that the errors are approximately normally distributed with constant variance, and in determining whether additional terms in the model would be useful. As an approximate check of normality, the experimenter can construct a frequency histogram of the residuals or a normal probability plot of residuals. Many computer programs will produce a normal probability plot of residuals, and since the sample sizes in regression are often too small for a histogram to be meaningful, the normal probability plotting method

This line is included in the machine.config file, but it is commented out, as part of the default install of Visual Studio .NET. The processModel account also must belong to the VS Developers group. Therefore, the ASPNET account should be added to the VS Developers group to allow the enumeration of the folders and virtual directories. After adding the account to VS Developers, execute iisreset (or reboot the computer) from the command prompt so that the change is recognized.

Figure 11-9 Patterns for residual plots. (a) satisfactory, (b) funnel, (c) double bow, (d) nonlinear. [Adapted from Montgomery, Peck, and Vining (2001).]

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is preferred It requires judgment to assess the abnormality of such plots (Refer to the discussion of the fat pencil method in Section 6-7) We may also standardize the residuals by computing di ei 2 2, i 1, 2 p , n If the errors are normally distributed, approximately 95% of the standardized residuals should fall in the interval ( 2, 2) Residuals that are far outside this interval may indicate the presence of an outlier, that is, an observation that is not typical of the rest of the data Various rules have been proposed for discarding outliers However, outliers sometimes provide important information about unusual circumstances of interest to experimenters and should not be automatically discarded For further discussion of outliers, see Montgomery, Peck and Vining (2001).

The Disco.exe utility can also be used to perform dynamic discovery on ASP.NET computers. Figure 16.4 shows the output when querying the localhost. Notice that this query does not require an actual filename. The same information is extracted from the Web server, except that the dynamic query finds both the static and dynamic entries.

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It is frequently helpful to plot the residuals (1) in time sequence (if known), (2), against the yi, and (3) against the independent variable x These graphs will usually look like one of the four general patterns shown in Fig 11-9 Pattern (a) in Fig 11-9 represents the ideal situation, while patterns (b), (c), and (d ) represent anomalies If the residuals appear as in (b), the variance of the observations may be increasing with time or with the magnitude of yi or xi Data transformation on the response y is often used to eliminate this problem Widely used variance-stabilizing transformations include the use of 1y, ln y, or 1 y as the response See Montgomery, Peck, and Vining (2001) for more details regarding methods for selecting an appropriate transformation.

If a plot of the residuals against time has the appearance of (b), the variance of the observations is increasing with time Plots of residuals against yi and xi that look like (c) also indicate inequality of variance Residual plots that look like (d) indicate model inadequacy; that is, higher order terms should be added to the model, a transformation on the x-variable or the y-variable (or both) should be considered, or other regressors should be considered EXAMPLE 11-7.

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