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Welcome to Java 2 Enterprise Edition Bible. This book, which is a follow up to Java 2 Bible, is for readers who wish to know more about the enterprise market. Enterprise programming is a hot topic these days, as more and more companies decide they need an online presence to complement their existing bricks and mortar version. This online presence is more than just a couple of Web pages; it extends to a complete electronic catalogue and purchasing system. XML is one of the biggest drivers of the enterprise market. As companies are starting to realize they need to work together to smooth out the supply chain management, they are building their second or third generation systems. They are doing this in collaboration with their suppliers and partners. To build these systems they need interoperability, and whole industries are springing up around this need alone. Need to add a new partner or supplier Just ask for the XML DTD. Very quickly you can now include the new functionality in your system. Throughout this book we will reference various commercial sites that you will be familiar with as examples of how large scale businesses are integrating not only their own Web sites, but also those of partners and suppliers, into one single system. Order your computer from Dell and you can track it through every stage of the build process. Then, once it has hit the courier, you can use the Dell site to trace the courier's information about its progress. Dell doesn't use just one courier, either yet your experience is identical regardless of which one is used.

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To obtain an inequality in the opposite sense, we use the Banach formula (3.31) (see Exercise 3.8). To do that, notice that this formula remains valid if on

c(t)

The quality of the data is the single most important factor to in uence the quality of the results from any analysis. The data should be reliable and represent the de ned target population. Data is often collected to answer speci c questions using the following types of studies:

1 1 1 1 1 1 1

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Note that the customerId is set to 1. The preceding is an example only because you don't know how to fetch a new customer ID. Some databases support AUTO_INCREMENT for a value in a column. In such cases, you don't need to set an ID; it will be done automatically when you insert a new row into the database. Other databases do not support AUTO_INCREMENT, so your code will need to make other arrangements to locate a unique identifier during this method. In either case, you need to make sure that an appropriate value is located and set for the returned CustomerID instance. Tip If your ejbCreate() method returns the same primary key twice, the EJB server is allowed to throw a DuplicateKeyException to the client code.

Prepayment Risk FHR 1971 S FHR 1688 SA 8.4843 333 360 601 12.88%

Binder, D. A., Kovacevic, M. and Roberts, G. (2004). Design based methods for survey data: alternative uses of estimating functions. In Proceedings of the Survey Research Methods Section, American Statistical Association (pp. 3301 3312). Washington, DC: American Statistical Association. Diggle, P. J., Heagerty, P., Liang, K. Y. and Zeger, S. L. (2002). Analysis of Longitudinal Data, (2nd edn.). New York: Oxford University Press. Fitzmaurice, G. M. (1995). A caveat concerning independence estimating equations with multivariate survey data. Biometrics, 51, 309 317. Fitzmaurice, G. M., Laird, N. M. and Ware, J. H. (2004). Applied Longitudinal Analysis. Hoboken, NJ: John Wiley & Sons, Inc. Graubard, B. I., Korn, E. L. and Midthune, D. (1997). Testing goodness of fit for logistic regression with survey data. In Proceedings of the Section on Survey Research Methods, American Statistical Association (pp. 170 174). Washington, DC: American Statistical Association. Horton, N. J., Bebchuk, J. D., Jones, C. L., Lipsitz, S. R., Catalano, P. J., Zahner, G. E. P. and Fitzmaurice, G. M. (1999). Goodness of fit for GEE: An example with mental health service utilization. Statistics in Medicine, 18, 213 222. Hosmer, D. W. and Lemeshow, S. (1980). A goodness of fit test for the multiple logistic regression. Communications in Statistics, A10, 1043 1069. Hu, F. and Kalbfleisch, J. D. (2000). The estimating function bootstrap. Canadian Journal of Statistics, 28, 449 499. Lee, A. J., Scott., A. J. and Soo, S. O. (1993). Comparing Liang Zeger estimates with maximum likelihood in bivariate logistic regression. Journal of Statistical Computation and Simulation, 44, 133 148. Liang, K. Y. and Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika, 73, 13 22. Liang, K. Y., Zeger, S. and Qaqish, B. (1992). Multivariate regression analysis for categorical data. Journal of the Royal Statistical Society, B, 54, 3 40.

Note: All trade summaries include $100 round-turn trade deductions for slippage and commissions. Data source: CQG, Inc.

JDBC 2.x and 3.0 bring Java into the world of database-intensive computing. Many of the shortcomings of the earlier JDBC specifications have been rectified. Mechanisms are now available to make use of pooled database connections and take maximum advantage of available database and networking resources.

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