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While volatility typically is characterized as a price phenomenon, in the world of bonds where yield is a key pricing variable, volatility may be quoted in price terms or in yield terms. Generally speaking, volatility on bond futures is expressed in price terms, while volatility on bond cash instruments is expressed in yield terms. And consistent with the properties of duration, whereby longer maturities/durations are associated with greater risk potential, a price volatility term structure tends to be upward sloping. This upward slope is consistent with incrementally greater price risks as maturity/duration extends. Conversely, in recognition of the inverse price/yield relationship that exists when pricing bonds, a yield volatility term structure tends to be downward sloping. Essentially, volatility is measured with either historical methodology or implied methodology. Historical volatility usually is calculated by taking a series of daily data (perhaps three months worth of daily closing prices of IBM stock, or maybe a year s worth of daily closes of the on-the-run 10year Treasury s yield) and then calculating a rolling series of annualized standard deviations. The classic statistical definition of a standard deviation is that it measures variation around a mean. Mean is a reference to average, and to calculate an average, we need to refer to a subset of data points. Using about 20 data points is a popular technique when daily observations are being tapped, because there are about 20 business days in a month. The formula for standard deviation is: 1xi 1B n x2 2 1

Fortunately, though, the database to learn from can be preprocessed (by computing its closure under tuple intersection [Borgelt and Kruse 1998c]) so that computing maximum projections becomes simple and, for most practical problems, efficient..

(a) Public statements include but are not limited to paid or unpaid advertising, product endorsements, grant applications, licensing applications, other credentialing applications, brochures, printed matter, directory listings, personal resumes or curricula vitae, or comments for use in media such as print or electronic transmission, statements in legal proceedings, lectures and public oral presentations, and published materials. Psychologists do not knowingly make public statements that are false, deceptive, or fraudulent concerning their research, practice, or other work activities or

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the lens diameter and f is the focal length. In this case, the aperture angle is the angular diameter of the lens as seen from a point in the image plane at focal length f. The size of the diffraction disk radius d is given by d 1.22 (f/D).

FIGURE 7.10 Probability density function of the time to total blindness for the diabetic retinopathy model of Figure 7.4. Bayes predictive density is the solid line, and the maximum likelihood estimated density is the dashed line.

The Linux system in this example has two hard disks (sda and sdb). You can see the total number of sectors successfully read and written from those hard disks. You can also see how many seconds were spent on input/output (IO) for those disks. You can also see if there are any I/O operations in progress. You can also list read/write information for selected disk partitions. Here is an example:

Research has shown that people consistently underutilize certain probabilistic information and fail to understand a wide array of statistical principles Similar shortcomings in scienti c reasoning ability have emerged in simulated and real legal settings as well Even though jurors sometimes appear to be sensitive to the scienti c content of expert testimony, they often fail to accord that evidence suf cient weight according to Bayesian norms Scienti c training and NFC appear to play an important role in jurors systematic evaluation of expert scienti c evidence Certain source-related cues, such as an expert s credentials, rate of pay, and frequency of testimony, can in uence jurors judgments when they confront complex scienti c evidence at trial Jurors viewing expert scienti c testimony also may rely on certain message-related cues, such as a study s general acceptance and its ecological validity, when evaluating the trustworthiness and credibility of certain witnesses.

IPR issues (see paragraph 8.2.5) The initiation of 3GPP, the new global organisation (see paragraph 8.2.6) The work in ETSI complementing the 3GPP work (see paragraph 8.2.7) Conclusions (see paragraph 8.2.8)

To date, research on legal decision-making has not provided support for the theory-based prediction that jurors simultaneously may engage in systematic and heuristic processing when evaluating expert scienti c evidence Additional research investigating jurors ability to reason about different types of scienti c evidence within the context of different types of cases is needed to advance our understanding of how jurors evaluate expert evidence presented at trial Assuming that jurors continue to have dif culty identifying awed expert scienti c evidence, studies investigating the effectiveness of the legal safeguards delineated by the Supreme Court in Daubert (ie cross-examination, opposing experts, judicial instructions on the burden of proof ) could prove bene cial.

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