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SOLUTIONS TO CHAP. 9 REVIEW QUESTIONS AND PROBLEMS, page 416 12. 0 (orthogonal), 19, [ 56, 7, 22] 14. 744, 744, 744, 77 16. 389, 389, 389. Of course, the third expression is just the usual notation for the scalar triple product, and the first two expressions justify such a notation that does not indicate where the dot and the cross is being used because it does not matter. 18. Unit vectors (1/ 62)[3, 2, 7], (1/ 65)[1, 8, 0] 20. 0.90 62 77 11.79 139 16.65 62 77. This illustrates the triangle inequality (Sec. 9.2) and consequences of it. 22. cos [4, 3, 1] [1, 1, 1]/ 26 3 6/ 26 3 0.6794, 47.2 24. [ 19, 13, 3], the negative of the sum of those vectors 26. q [2, 5, 0] 5 28. See Prob. 41 in Problem Set 9.2. 30. m r p [ 4, 3, 0] [4, 2, 0] [0, 0, 20]. The moment has the tendency to produce a clockwise rotation. 1 32. v r [ 5 sin t, cos t, 2], where t _ , so that 4 v(P) r (P)

Correlation Correlation measures the degree of association between two investments. In our case, it measures the degree of association between two or more return streams. It measures how strongly the variables are related, or change, with each other. If two variables tend to move up or down together, they are said to be positively correlated. If they tend to move in opposite directions, they are said to be negatively correlated. The return streams can include both manager/fund returns and index returns. The quantitative measure of the degree of correlation between two return streams is called the correlation coef cient. Two return streams are considered perfectly correlated when their correlation coef cient is equal to 1 and perfectly negatively correlated when their correlation coef cient is equal to 1. As with all statistical measures, the more data points (the longer the performance history), the more signi cant the results. As a rule of thumb, I tend to consider two years worth of monthly data (24 data points) to be the absolute minimum when calculating correlation and the other regression-based statistics that follow.

As the investigation unfolded and new details began to surface, Ted provided me with some astounding information. Apparently when customers went to the main branch of Buck s Bank and used a particular ATM, the transactions were immediately duplicated on the bank s end meaning that someone was retrieving stored and encrypted information from the

1640 (cm 1 )

P1: JYS WY022-17 WY022/Pfaffenberger WY022/Pfaffenberger-v3.cls June 8, 2004 15:18

All disciplines collect data about things or objects. Medical researchers collect data on patients, the automotive industry collects data on cars, retail companies collect data on transactions. Patients, cars and transactions are all objects. In a data set there may be many observations for a particular object. For example, a data set about cars may contain many observations on different cars. These observations can be described in a number of ways. For example, a car can be described by listing the vehicle identi cation number (VIN), the manufacturer s name, the weight, the number of cylinders, and the fuel ef ciency. Each of these features describing a car is a variable. Each observation has a speci c value for each variable. For example, a car may have: VIN IM8GD9A_KP042788 Manufacturer Ford Weight 2984 pounds Number of cylinders 6 Fuel ef ciency 20 miles per gallon Data sets used for data analysis/data mining are almost always described in tables. An example of a table describing cars is shown in Table 3.1. Each row of the table describes an observation (a speci c car). Each column describes a variable (a speci c attribute of a car). In this example, there are two observations and these observations are described using ve variables: VIN, Manufacturer, Weight,

If, instead, this is an update, we don t have to seek at all. We simply reference the existing record through its previously retrieved and saved CEOID, and write the new values directly into it:

The bean is now capable of supporting either CMP or the classic BMP implementation. The decision on which to use can be made by modifying the deployment descriptor as desired. This is an excellent way to try CMP first, before abandoning the BMP implementation entirely.