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rippling up at the same time as the other is rippling down, the two cancel each other out, leaving no motion at all. You can see this happening if you drop two stones near each other in a pool of water. Where the waves overlap there will be some points where the water hardly moves and others where the motion is particularly strong. Young argued that exactly the same thing was happening to light after it passed through the two slits. The waves of light were interfering with each other. As they moved out from the slits, at some points they were both rippling up at the same time, producing the bright bands. At others they were cancelling each other out, giving the dark bands.

The recall period interacts with other factors, such as salience of the event, type of response task, etc. (for reviews, see Jabine et al., 1984; Biemer & Trewin 1997; Tourangeau et al., 2000; Bound et al., 2001, pp. 3743 3748).

and H2 O, respectively (illustrated in Figure 1.5). Both CO2 and H2 O have three degrees of translational freedom. Water has three degrees of rotational freedom, but the linear molecule carbon dioxide has only two since no detectable energy is involved in rotation around the O=C=O axis. Subtracting these from 3N , there are 3N 5 degrees of freedom for CO2 (or any linear molecule) and 3N 6 for water (or any non-linear molecule). N in both examples is three, and so CO2 has four vibrational modes and water has three. The degrees of freedom for polyatomic molecules are summarized in Table 1.1.

(We note here that there are other types of anomaly in GPS terminology, which are angles that are useful for calculating the satellite coordinates within its orbital plane) Note that instead of specifying the satellite s anomaly at every relevant time, we could equivalently specify the time that the satellite had passed the perigee, and then compute the satellite s future position based on the known laws of motion of the satellite around an ellipse Finally, the argument of perigee is the angle between the equator and perigee Since the orbit is nearly circular, this orbital parameter is not well de ned, and alternative parameterization schemes are often used Taken together (the eccentricity, semi-major axis, inclination, Right Ascension of the ascending node, the time of perigee passing, and the argument of perigee), these six parameters de ne the satellite orbit These parameters are known as Keplerian elements.

Business to business (B2B) applications are primarily used to swap information without the need for a user interface. In most cases, they are fully automated. That is, one business decides it is low on stock and so automatically sends off a request to a supplier for more. There is most likely no human in the loop. If there is, he or she is using the local system to look at inventory levels and make the request through that interface rather than directly to the supplier.

Gauld, A. (1968). The founders of psychical research. London: Routledge & Kegan Paul. ( It is the most important work . . . (fn. 16) from p. 140.)

Covariance Matrix for Estimated Parameters. As we have already seen, for weighted least squares we can write the computed covariance matrix for estimated parameters as: Cx = (ATWA) 1 We can now write down the full expression for the computed covariance matrix, by substituting for the double differenced data weight matrix W: Cx = (AT(DCDT) 1A) 1 As mentioned above, for the (undifferenced) data covariance C, we often use a diagonal matrix, assuming a value for the standard deviation of an observation. Typical realistic values for this are several mm. Although the receiver can usually measure the phase with better precision than a mm, the post- t residuals typically show several mm standard deviations, due to unmodelled errors such as multipath. Even using such an in ated value for measurement precision might not produce a realistic covariance matrix for station coordinates. This is partly due to two reasons: (1) unmodelled errors can be correlated with the parameters being estimated (an aliasing effect ); and (2) post- t almost always show some degree of time-correlation (e.g., due to multipath). A simple and often surprisingly effective way to deal with this problem is to multiply the nal coordinate covariance matrix by an empirical scaling factor, inferred by experience , according to the brand of software being used, the observation scenario, and the estimation strategy used.