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38), (b) initial-level means ( 0 = 32) at Age = 5, and (c) a linear slope mean ( s = 81) for each 5-year period after Age = 5 The goodness-of- t of the DCS model can be compared to that of every other nested alternative, and these comparisons show the best t was achieved using this model ( 2 = 785 on df = 2; 2 = 485 on df = 1; 2 = 385 on df = 1) From these results we calculate the expected group trajectories and the 5-year latent change accumulation as the combination of Equations 1832.

Capnet Category Income $50,000 > $50,000 Total 574 582 Loss 49.7% 50.3% 1156 None 17,635 4,133 81.0% 19.0% 21,768 Gain < $5000 685 140 83.0% 17.0% 825 Gain $5000 122 1129 9.8% 90.2% 1251

Private Sub Workbook_BeforePrint(Cancel As Boolean) For Each sht In ThisWorkbook.Sheets sht.PageSetup.LeftFooter = _ &8 & ThisWorkbook.FullName Next sht End Sub

Part of our strategy was to report two types of best models, one (containing no principal components) for use solely in target prediction, and the other (containing principal components) for all other purposes, including customer pro ling. The subset of variables that were highly correlated with each other were shunted to a principal components analysis, which extracted two components from these seven correlated variables. Principal component 1 represented purchasing habits and was expected to be highly indicative of promotion response. Next, the BIRCH clustering algorithm was applied. Three clusters were uncovered: (1) moderate-spending career shoppers, (2) low-spending casual shoppers, and (3) frequent, high-spending, responsive shoppers. Cluster 3, as expected, had the highest promotion response rate. Thus, the classi cation models contained the following inputs: r Model collection A (included principal components analysis: models appropriate for customer pro ling, variable analysis, or prediction

Another potential cause of your Internet woes may be that Windows is using an incorrect IP address. To con gure Windows to automatically obtain a valid IP address, do the following:

This JavaScript sets two cookies. The first, GMAIL_LOGIN2, is set with a value of Tstart_time/start_time/now where both start_time and now are the datetime exactly then. As you can see from the comments in the code, Google intends to replace this in the future. The second cookie is called GMAIL_RTT2 and contains the time it takes to retrieve a 1-pixel image file from the Gmail servers. RTT, presumably, stands for Round Trip Time. You won t look at it in this book, but the rest of the JavaScript code on that page presents a very nice listing of a browser check that removes the login window if the browser isn t capable of using Gmail. If you watch the Gmail login sequence from your own browser, you will see that it goes through more redirects before it settles into HTTP again, and you can see what is going on from the Tcpflow trace file. Hitting stop on the browser at just the right time (and that is, to quote the fine words of my editor, a total crapshoot), gives you this URL:

Here ri 2 1; 2; . . . ; N is an integer number for the ranking order of the ith neuron (i 1, 2, . . ., N) to the sth data point (s 1, 2, . . ., M). This ranking order is de ned by the Euclidean distance between the ith neuron and the sth data point. The closest neuron (in the sense of minimal Euclidean distance) is called the winning neuron and gets ranking order r 1. The second closest neuron gets r 2, and so on. The initial learning rate R0 and the steepness parameters TC and TW have to be preset before the learning. In the further simulation we use the following settings: T 500; R0 0:15 and TC TW T=5: One typical example for information compression of a raw data set containing M 800 data by using N 60 neurons is given in Figure 11.3. The unsupervised learning algorithm described above is referred to as algorithm Fixed when compared to the next two learning algorithms presented in Section 11.4.