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When you want in-depth background information and analysis of the top sports stories, click the ESPN Intelligence link on the ESPN.com home page. ESPN Intelligence offers insights assembled from ESPN.com, ESPN TV, and ESPN The Magazine on all the top sporting events and stories. This is the place for serious sports fans; with ESPN Intelligence you ll discover the stories behind the stories and learn why a team is winning or losing.

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This example talks about manipulating text - Manipulating PDF files with ... text as well - iTextSharp remove text from static PDF document C# [^].

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jQuery was created in 2006 by John Resig as an alternative to more complicated JavaScript libraries. jQuery enables Web designers and developers to write simpler JavaScript that still enables them to perform advanced JavaScript functions on their Web sites. jQuery is awesome because no hardcore programming knowledge is required to perform DOM manipulation. jQuery does have some advanced areas that require prior JavaScript knowledge, such as working with the Ajax methods in forms to get and post content, as reviewed in 9, creating custom jQuery plug-ins, as reviewed in 11, and working with mobile Web sites, as reviewed in 10. Most designers and developers whom I know use, or have used, jQuery at some point in the past four years. When I ask why, they usually answer, Is there anything easier Its ease of use attracts so many people to use jQuery; you don t need a master s degree in computer science to make a form submit via Ajax. You may be wondering what sort of things you can do with the jQuery library. The answer is, everything you can do with the native JavaScript API. I dive deeper into what you can do with jQuery throughout the book, but here is just a quick overview of the main features of jQuery:

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12 Jan 2015 ... Is it possible to remove all text occurrences contained in a specified area (red color rectangle area) of ​​a pdf document? 5th November 2015.

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Multiple regression derived weights that optimally reflect Gf Gc ability BY achievement area BY age interactions

from the mean. This occurs because the correlation between any combination of ability and achievement measures is less than perfect. The WJ III capitalizes on the co-norming of the WJ III COG and ACH to provide slightly different regression adjustments across age or grade when generating the predicted achievement score. The developmental changes in the ability achievement correlations observed in the WJ III norm data are incorporated into the prediction equations. A common practice, particularly when an intelligence test is not co-normed with the administered achievement test, is to select a single point correla-

tion (e.g., .65) and use this value to correct for regression effects via a formula for all ages. The predicted score in the WJ III GIA Ability Achievement Model is based on a developmentally sensitive regression-to-the-mean adjustment procedure. The WJ III GIA ability achievement discrepancy scores are compared against real discrepancy norms (end box in Figure 14.3; McGrew, 1994; McGrew et al., 1991; McGrew & Woodcock, 2001; Mather & Schrank, 2001; Woodcock, 1978). Predicted achievement scores in all relevant achievement domains were generated for all norming subjects. Each

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Analysis is one thing; opinion is another. ESPN.com offers plenty of opinions, from some of the most opinionated personalities in sports journalism including Nick Bakay, Jim Caple, Whitney Casey, Michael Davies, Wayne Drehs, Mike Greenberg, Graham Hays, Tim Keown, Chris McKendry, Jeff Merron, Alysse Minkoff, Brian Murphy, Eric Neel, Stacy Pressman, Darren Rovell, Dan Shanoff, Bill Simmons, Jason Whitlock, Ralph Wiley, and Hunter S. Thompson (!). All of these opinion makers have regular columns in ESPN.com s Page 2 section. Just click the Page 2 link at the top of the hom page, or go directly to espn.go.com/ page2/, to read what these experts are saying about your favorite sport. I par ticularly like Dan Shanoff s Daily Quickie, which offers opinions and insights in fun, bite-sized portions.

subject s predicted and actual achievement difference scores in the same domain produced ability achievement discrepancy scores for the WJ III norm file. The distributional characteristics of each ability achievement discrepancy score distribution were used to produce the WJ III GIA ability achievement discrepancy norms just as age- or grade-specific norms are determined for any test or cluster. In summary, whenever a WJ III GIA ability achievement discrepancy score is calculated, this score incorporates the developmentally appropriate degree of regression-to-the-mean and is then compared against the actual distributions of discrepancy scores in the norming sample. The WJ III GIA ability achievement discrepancy scores may be interpreted in three different metrics (standard score discrepancy, percentile rank, and standard deviation units; McGrew & Woodcock, 2001) as the examiner chooses.4

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