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a subset of variables over the same ve observations as Table 3.5. This table is limited to six normalized variables (income, age, children, electronic, groceries, and clothes). Distance measures compute a number for any pair of observations. These measures compare the values for each of the variables in the two observations and compute a distance based on some function relating to the differences between these values. The following describes a number of distance measures. Euclidean The euclidean distance is one of the most common distance functions. If a data set only has two variables, then the euclidean distance would calculate the physical distance between the two points plotted on a scatterplot. The following formula calculates a euclidean distance between two observations ( p and q), measured over n variables: n Xq ( pi qi )2 dp,q

If the transmission channel over which the echo travels is linear and invariable in time, where x(t) represents the signal received and y(t) the resulting echo, the following relation is obtained:

So we could refer to the change in market prices to calculate the returns to securities held in the portfolio, in some cases However, returns calculated in this manner have a built-in assumption that there was no trading of the security or income earned by the security for the day We can see this assumption by way of an example Say that the price of our stock increased steadily through the day, from 10 to 11 And instead of holding the stock at the beginning of the day, we purchased the stock during the day If we bought the stock at mid-day for 1050, our dollar gain would have been only 50 cents per share, not $100 So our return would only be 476% ((1100/1050) 1).

AP3.1-4

If we had used the daily price change to calculate the return, the reported return to the fund would be misleadingly high If we bought the security in multiple lots through the day, all at different prices, we would nd it easiest to calculate our return by taking the market value of the position at the end of the day (shares end of day price) and dividing it by the sum of the net amounts of the individual stock trades The difference between our two returns, 10% and 476%, re ects the trading done in the portfolio Sometimes the return calculated without the effect of trading, in this case the 10% return, is called a price-to-price return or a buy-and-hold return.

N category_labels: (Optional) The range that contains the labels for the cate-

Buy-and-hold returns might approximate the actual return earned to the security by the fund, but depending on the type of portfolio, amount of turnover and volatility experienced, the differences can be large The implication of portfolio trading is that if we want to calculate the true fund-speci c return earned on a security, we have to calculate the security level returns for each fund independently Thus, performance measurement at the security level requires more work to implement than many portfolio management functions, for example, calculating the duration or the P/E ratio for each security held within a fund (since these values are the same for every portfolio holding that security) Exhibit 151 shows the difference between the single period price-to-price return and the actual total return, which includes the effect of trading..

R1 O O O R2 H3COOC R2 O O R1 = H, OH, OAc R2 = H, CN, COOH, COOCH3, OCH3, SC2H5, SO2C2H5, CH2NO2, CH(CH3)NO2 R2

The preceding code invokes the resolve_initial_references method to obtain an initial object reference to the server. The string "NameService" is passed on as the initial name service. 8. Use the narrow() method of the NamingContextHelper interface to narrow down the initial object reference obtained (a generic CORBA object) to its naming context object:

46.5

The astute reader may have noticed that this statement was not preceded by the Application object. Most times this reference is not needed, as it is assumed.

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Listing 8.9 MyXSLTExample.java. (continues)

Unlike the similarities of Section B.1, estimating nonuniform scale and rotation at the same time (Fig. B.12) is dif cult to do in closed form. However, since the number of degrees of freedom is close to full af nity (6 vs. 5 in two dimensions or 12 vs. 9 in three dimensions), we can solve rst for an af nity and then upgrade to the nonuniform scale by removing the additional degrees of freedom. This procedure is suboptimal and therefore should be re ned with a nonlinear LSE. Now we reconsider the cases with and without translation. The rst is given by Rn Dn T= T 0 tn 1 (B.35)

4