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Proof: The proof involves a generalization of the sandwich argument [20] used to prove the AEP in Section 16.8. The details of the proof (in Algoet and Cover [21]) are omitted. Finally, we consider the example of the horse race once again. The horse race is a special case of the stock market in which there are m stocks corresponding to the m horses in the race. At the end of the race, the value of the stock for horse i is either 0 or oi , the value of the odds for horse i. Thus, X is nonzero only in the component corresponding to the winning horse. In this case, the log-optimal portfolio is proportional betting, known as Kelly gambling (i.e., bi = pi ), and in the case of uniform fair odds (i.e., oi = m, for all i), W = log m H (X). (16.73)
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Click the Help drop-down menu. Click Hide the Of ce Assistant (see Figure 8-2).
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With high base doping concentrations, the intrinsic temperature, Ti, of a SiGe HBT device can be signi cantly higher than a Si BJT device that provides a device less prone to thermal instability. The intrinsic temperature, Ti, is the temperature at which the intrinsic carrier concentration exceeds the dopant concentration. Thermal runaway or second breakdown is the point (I, V) at which a semiconductor device is at the onset of a negative resistance state because of thermal feedback initiated by Joule self-heating. The thermal instability is typically irreversible and leads to either leakage or destruction of the semiconductor structure. From Joule heating, the intrinsic carrier concentration increases with increasing temperature. It is well known that Si-BJT-device thermal instability typically occurs in the low-doped base region, where the intrinsic temperature is low due to the low base doping concentration. For SiGe devices, the position-dependent Ge concentration also plays a role. With a position-dependent Ge concentration, the intrinsic carrier concentration can be expressed as [28,29], n2 x gn2 e E x =kT i io and,
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In this exercise, I tried to demonstrate the process of developing VBA procedures a valuable lesson even if you re not yet a VBA veteran. I communicated the following points: Developing a successful procedure is not necessarily a linear process. It s often useful to use short procedures or the Immediate window to test ideas or approaches before incorporating them into your work. Even though a procedure appears to work correctly, appearances can be deceiving. Therefore, it s important to test your work in a realistic situation. For example, despite lots of testing on my part, it took someone else to identify the problem that may occur when sheets are hidden. You can learn a lot by recording your actions and studying the code that is recorded. Although I didn t actually use any of the recorded code, I learned several things by examining the code. A project s original requirements aren t always complete. In this example, I didn t think to require that the original active sheet remain the active sheet after the sorting.
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roughly the same at all values of another continuous variable. Failures of linearity and homoscedasticity of residuals are illustrated in Figure 5.8 (panels C and D). Heteroscedasticity, the failure of homoscedasticity, occurs because one of the variables is not normally distributed (i.e., one variable is linearly related to some transformation of the other), because there is greater error of measurement of one variable at some levels, or because one of the variables is spread apart at some levels by its relationship to a third variable (measured in the design or not), as seen in Figure 5.9. An example of true heteroscedasticity is the relationship between age (X1) and income (X2), as depicted in Figure 5.9, panel B. People start out making about the same salaries, but with increasing age, people spread farther apart on income. In this example, income is positively skewed, and transformation of income is likely to improve the homoscedasticity of its relationship with age. An example of heteroscedasticity caused by greater error of measurement at some levels of an IV might be weight. People in the age range of 25 to 45 are probably more concerned about their weight than are people who are younger or older. Older
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easily. Labor analysis of number of guest rooms cleaned by room attendants and amount of labor hours required to clean guest rooms is performed faster, and the daily housekeeper s report is quickly generated. Inventory of equipment and guest room supplies is also readily available. Maintenance requests for guest rooms can be communicated through the PMS. The maintenance department staff can also check room status information to determine if the housekeeping staff noted repairs to be made. If the maintenance department wants to take a room out of service for a few days to perform repairs, this information can be relayed to the housekeeping and front desk staff through the housekeeping module.
You can edit groups without ungrouping them. You can also select an individual object in a group for editing, without ungrouping the objects.
19 MULTIPLE LINEAR REGRESSION 483 Leona S. Aiken, Stephen G. West, and Steven C. Pitts LOGISTIC REGRESSION 509 Alfred DeMaris META-ANALYSIS 533 Frank L. Schmidt and John E. Hunter SURVIVAL ANALYSIS 555 Judith D. Singer and John B. Willett TIME SERIES ANALYSIS 581 Wayne F. Velicer and Joseph L. Fava
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