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The HAPA makes a distinction between a motivation phase and a volition /postdecision phase of health behavior change. The basic idea is that individuals experience a shift of mind-set when moving from the first phase (motivational) to the second (volitional). The moment people commit themselves to an intention to exercise, for example, they enter the volitional phase. In this phase, a division into two subphases appears to be meaningful, wherein people can be labeled as either intenders or actors. First, they intend to act but they remain inactive. Second, they initiate the intended action. Thus, three phases or stages can be distinguished, as shown in Figure 24.6. In the (a) nonintentional stage, a behavioral intention is being developed, which is similar to the contemplation stage in the TTM. Afterward, individuals enter ( b) the intentional stage, where they have already formed an intention but still remain inactive (or at least not active at the recommended level), while the exercise behavior is being planned and prepared. If these plans are translated into action, individuals reside in (c) the action stage. They are then physically active at the recommended goal behavior level. In the nonintentional stage, an intention has to be developed. In this phase, risk perception is a distal antecedent within the motivational phase. Risk perception is sufficient to enable the undecided person to form an intention. Furthermore, it is a prerequisite for a contemplation process

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

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psychological inquiry has been conceptualized as involving the identi cation of deep structural explanatory mechanisms that (it is assumed) underlie overt behavior. Higgins and Kruglanski (1996) outline this vision for social psychological inquiry:

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&dwType, (LPBYTE)pszText, &dwSize); //Do something with dwValue } You expect this code to read five Unicode strings of length 128 bytes from five registry values named Value0 through Value4. But what if any of the actual strings is less than 128 bytes long RegQueryValueEx will return the real length of that string in dwSize. The next RegQueryValueEx call will then say to read only as many bytes as were in the last string. At this point, you can count on all the rest of the values read to be completely unreliable. Believe me, the bugs that result from such an oversight are very difficult to track down. The moral of this story is: Set the lpcbData value properly for each and every call to RegQueryValueEx.

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We can prove that these rates will generate the sales required to yield our desired potential average room rate: Room Nights 5,110 4,471 3,194 12,775 $1,009,248 12,775 Average Rate $86.34 77.71 69.07 Total Sales $ 441,197 347,441 220,610 $1,009,248

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.

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