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who had been marginalized, excluded, or otherwise made invisible in psychology. In recognizing diversity as a value, it is instructive to remember that not all that long ago there was little celebration and plenty of struggle. The social movements of the 1960s were about many things, civil rights being chief among them. Civil rights based on demographics such as age, gender, and race/ethnicity had

analysis, perhaps leading to conclusions that would not stand a test of replication in the absence of the errant case There are a number of measures of each type of regression diagnostic We present a recommended choice of measure of each type, and illustrate its use with our numerical example More complete treatments are given in Fox (1991) and in Cohen et al (2003, chap 10) Leverage Measures of leverage are based on the distance between an individual point and the centroid of the sample The centroid of the sample is the point representing the mean on each predictor variable In a simple regression equation containing only the risk and bene ts predictors, the centroid is the point at the means of these two predictors: MRISK = 3989; MBENEFIT = 3321, as given in Table 191, Panel A.

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Runner s anxiety and mood on running and non-running days: An in situ daily monitoring study Psychology, Health, and Medicine, 3, 193 199..

The farther an individual observation is from the centroid of the predictors, the greater the potential of that case to change the outcome of the regression analysis We consider a measure of leverage, referred to as hii, which is commonly employed Distance or Discrepancy Measures of distance (discrepancy) are all based on the resid ual for a case (Yi Yi ) We focus on one of a number of these measures, the externally studentized residual (or studentized deleted residual) This measure has two characteristics First, it is a standardized residual that follows a t distribution (residual/standard deviation of residual) Second, it is based on a regression equation derived from an analysis in which the case in question has been deleted.

The reason for the deletion of the case during computation of the regression model is that if the case is in the sample and if the case is unduly affecting the regression analysis, then the regression line (or regression plane) will be pulled toward the point In such cases, the residual will be small because the point is so in uential, even though the point may be grossly unrepresentative of the remainder of the sample In uence Measures of in uence assess the extent to which a single case determines the outcome of the regression analysis The measure DFFITS is a standardized measure (in z score form) of the difference in the predicted score for a case if the case is included in the analysis versus if the case is excluded from the analysis.

Version. Used to indicate the version of RIP. Currently set to 1 for RIP version 1. Family of net x. Used to show the diversity of the RIP protocol and to indicate the protocol that owns the packet. It will be set to 2 for IP. Since XNS could possibly be running on the same network as IP, the RIP frames would be similar. This shows that the same RIP frame can be used for multiple protocol suites. AppleTalk, Novell NetWare s IPX, XNS, and TCP/IP all use the RIP packet. Each packet is changed a little for each protocol. IP address. Indicates the IP address of a specific destination network. This would be filled in by the requesting station. An address of 0.0.0.0 indicates the default route (explained later). The address field needs only 4 bytes of the available 14 bytes, so all other bytes must be set to 0. This will be explained in RIP Version 2. If this is a request packet and there is only one entry, with the address family ID of 0 and a metric of 1, then this is a request for the entire routing table.

When considering the architecture of a complicated system, such as a DBE, there are several different ways we can view the details. In this section, we will provide a very brief and high-level view useful for discussing the archetypical DBE architectures used later in the chapter and later in the book. We will revisit architectural concerns in s 12 to 15. For our purposes here, we will merely consider services, components, subsystems, and sites.

Finally, a series of measures, DFBETAS, one for each regression coef cient, are standardized measures of the change in each regression coef cient if the case is included.

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