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It is the difference between going to a site to buy things and going to a site for an experience and then buying In the future, branding will be built more on experience and discussion about the experience, although traditional advertising and promotion will still have some in uence For some leading brands, the future is now Dialogues about experiences are already shaping and reinforcing what community members think, feel, say, and do about these brands Ordinarily it takes a long time to build a brand, but the social web has shown this is not always true It took Google about eight years to become a verb YouTube did not spend a dime on advertising and became a national brand in less than three years iVillage has become a brand for information for women MySpace, BlackPlanet, hulu, and more have become brand names almost overnight.

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.

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How did that happen Through the dialogue on the sites and the conversations visitors have with each other, the brands became strong Not just dialogue but also the ful llment of a promise made through that dialogue, either by offering information a visitor wants or by getting to the product through the information Brands will become more and more powerful as they ful ll the promise made in their dialogues with customers and the dialogues among customers Any brand that tries to game the system or has a shoddy product or shady proposal will be found out more quickly and punished more severely on the social web The super-duper Kryptonite bike lock (which someone discovered could be picked by a Bic pen and.

EXAMPLE 8.2 (a) An incident ray is split into two rays at the boundary between two optical media with indices of refraction equal to n1 and n2 , respectively. This is visualized in Figure 8.3. Show that n1 sin y1 n2 sin y2 This is called Snell s law. (b) For rays traveling close to the optical axis, show that n1 y1 n2 y2 This is called paraxial approximation in geometrical optics. Solution: (a) The optical path length between A and C is given by Lp n1 AB n2 BC By Fermat s principle, Lp is to be minimized. Lp can be written as

Diversification of negatively and/or uncorrelated trading systems is one of the most effective methods of improving rates of return without proportionately increasing the risk assumed to achieve these enhanced levels of performance. To illustrate this point, let us examine a trend-following system from 3 (MACD) with our diversified futures portfolio, and a directionally biased intermediate-term mean reversion system from 4 (RSI Extremes with the 200-day moving average filter) with our mean reversion portfolio, and then compare these results with the combined performance of both trading systems. In comparing Tables 9.3 and 9.4 to Table 9.5, the first and most important improvement is in the profit to maximum drawdown ratio. This is due to the fact that low correlations between the trend-following and mean reversion systems led to a smoothing of equity drawdowns for the performance of the combined trading system results. Although the maximum drawdown column shown in Table 9.5 was larger than in Table 9.3 or 9.4, it represented an increase only of roughly 17 percent and 20 percent respectively. By contrast, because Table 9.5 took all signals generated by both systems, its total net profits were additive, thereby leading to an overall improvement in performance results.

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