The SAS Certification Prep Guide: Advanced Programming for SAS 9, Second Edition, prepares you to take the Advanced Programming for SAS 9 exam. Major topics include SQL processing with SAS, the SAS macro language, advanced SAS programming techniques, and optimizing SAS programs. You will also become familiar with the enhancements and new functionality that are available in SAS 9. Experienced SAS users who want to prepare for this exam will find this guide to be an invaluable, convenient, and comprehensive resource that covers all of the objectives tested on the exam. The text contains quizzes that enable you to test your understanding of material in each chapter. Additionally, solutions to all quizzes are included at the back of the book. Candidates must earn the SAS Certified Base Programmer Credential for SAS 9 before taking the SAS Advanced Programming for SAS 9 exam.

This article demonstrates how the realworld data is imported, managed, visualized, and analyzed within the R statistical framework. Presented as a spatial mashup, this tutorial introduces the user to R packages, R syntax, and data structures. The user will learn how the R environment works with R packages as well as its own capabilities in statistical analysis. We will be accessing spatial data in several formats-html, xml, shapefiles, and text-locally and over the web to produce a map of home foreclosure auctions and perform statistical analysis on these events.

Praise for Business Intelligence Success Factors: Tools for Aligning your business in the Global Economy "Olivia Parr Rud does a remarkable job of weaving together many topics in a strategic way. As 'quants,' we're fascinated with data and fact-based decision-making. But success only comes when you consider the human factor, especially effective communications. Making topics like evolutionary biology, complexity science, and systems thinking relevant for business success is a unique and compelling view. As Max Frisch said, 'We hired workers and human beings came instead.'" —Anne Milley, Senior Director, Technology Product Marketing, SAS "Business Intelligence Success Factors is a must-read for anyone implementing BI on an organizational level. This book explains the business landscape and the underlying reasons for our current volatility, offering clear guidance on navigating our information rich global economy." —Ron Powell, Editorial Director, Business Intelligence Network Transform challenges into opportunities with emerging Business Intelligence technologies Written by an expert in data mining and statistical analysis, this valuable resource unveils the connection between the increased use of BI and the need for new, proven theories and models in BI, as well as the guidance to implement them successfully in your organization. Are you ready to become adaptable? Learn how to harness today's rapidly evolving global economy with Business Intelligence Success Factors.

You too can understand the statistics of life, even if you're math-challenged! What do you need to calculate? Manufacturing output? A curve for test scores? Sports stats? You and Excel can do it, and this non-intimidating guide shows you how. It demystifies the different types of statistics, how Excel functions and formulas work, the meaning of means and medians, how to interpret your figures, and more — in plain English. Getting there — learn how variables, samples, and probability are used to get the information you want Excel tricks — find out what's built into the program to help you work with Excel formulas Playing with worksheets — get acquainted with the worksheet functions for each step Graphic displays — present your data as pie graphs, bar graphs, line graphs, or scatter plots What's normal? — understand normal distribution and probability Hyping hypotheses — learn to use hypothesis testing with means and variables When regression is progress — discover when and how to use regression for forecasting What are the odds — work with probability, random variables, and binomial distribution Open the book and find: Ten statistical and graphical tips and traps The difference between descriptive and inferential statistics Why graphs are good How to measure variations What standard scores are and why they're used When to use two-sample hypothesis testing How to use correlations Different ways of working with probability

Bringing together all the relevant material to impart a clear understanding of how to analyze ROC curves, this book covers the fundamental theory as well as various special topics. It provides illustrative examples of the major methodological developments and includes as much of the mathematical theory as necessary without making the treatment too dense. The authors survey the uses made of the methodology across a range of different areas, from atmospheric science and geoscience to experimental psychology and sociology. They also list a number of websites from which software implementing the various techniques can be downloaded.

Pro SQL Server 2008 Analytics provides everything you need to know to develop sophisticated and visually appealing sales and marketing dashboards using SQL Server 2008 and to integrate those dashboards with SharePoint, PerformancePoint, and other key Microsoft technologies.

The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data analysis, model building and implementation. The Handbook helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. Use this book to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques, and discusses their application to real problems, in ways accessible and beneficial to practitioners across industries - from science and engineering, to medicine, academia and commerce. This handbook brings together, in a single resource, all the information a beginner will need to understand the tools and issues in data mining to build successful data mining solutions. Written "By Practitioners for Practitioners" Non-technical explanations build understanding without jargon and equations Tutorials in numerous fields of study provide step-by-step instruction on how to use supplied tools to build models Practical advice from successful real-world implementations Includes extensive case studies, examples, MS PowerPoint slides and datasets CD-DVD with valuable fully-working 90-day software included: "Complete Data Miner - QC-Miner - Text Miner" bound with book

Yahoo! Web Analytics teaches readers how to collect data, report on that data, and derive useful insights using Yahoo!’s free Web analytics tool. This detailed resource from Yahoo!’s Director of Data Insights discusses the why of Web analytics as well as the how while revealing secrets and tricks not documented elsewhere. The thorough book also offers step-by-step instructions and advanced techniques on everything from using data collection groupings to creating compelling data visualizations. It’s a must-read for all analytics professionals and those who want to be.

Praise for Google Speaks "It's not hard to see that Google is a phenomenal company....At Geico, we pay these guys a whole lot of money for this and that key word." – Warren Buffett "Google rocks. It raised my perceived IQ by about 20 points." – Wes Boyd, President of Moveon.Org "Google is my rapid response research assistant. It's the Swiss Army knife of information retrieval." – Lloyd Grove, columnist, Portfolio.com "Who's afraid of Google? Everyone." – Wired magazine "Writers of the past had absinthe, whiskey or heroin. I have Google." – Michael Chabon, author of The Amazing Adventures of Kavalier and Clay

A budget is a financial action plan for an organization. This guide will teach you how to: - Forecast future business results, such as sales volume, revenues, and expenses - Reconcile those forecasts to organizational goals - Obtain organizational support for your proposed budget - Fine-tune your budget over time

In Just Enough SAS: A Quick-Start Guide to SAS for Engineers, Robert Rutledge provides "just enough" instruction on a broad variety of topics so that a new SAS user can become productive very quickly. Although most of the material in the book is geared toward a general audience, engineers will especially benefit from the focus on analysis of quality and reliability data found in Chapters 9 and 10 as well as in the examples throughout the book. This comprehensive text, prepared using SAS 9.2, can be used both as a tutorial for getting started with SAS and as a reference for details that even experienced SAS users find themselves looking up over and over again. In addition, the companion code to the book includes a set of commonly used utility macros that allow SAS users to wield maximum power with minimal effort. A complete bibliography provides readers with numerous opportunities for further learning.Topics addressed include bringing data into SAS from a spreadsheet or relational database, plotting data with ODS Statistical Graphics, summarizing and manipulating data for analysis using DATA steps and procedures, publishing results on the Internet and in PDF and RTF, creating appropriate plots of data using PROC GPLOT and PROC GCHART and the newer statistical graphics procedures, with particular emphasis on quality control and reliability analysis - key areas for engineers working in high-tech manufacturing and development, and using the SAS macro language to streamline and automate data analysis projects.New SAS users will find Rutledge's book useful as a quick-start guide to doing meaningful work with SAS, and experienced users will find numerous tips and techniques for improving and extending their coding methods.

Structural Bioinformatics was the first major effort to show the application of the principles and basic knowledge of the larger field of bioinformatics to questions focusing on macromolecular structure, such as the prediction of protein structure and how proteins carry out cellular functions, and how the application of bioinformatics to these life science issues can improve healthcare by accelerating drug discovery and development. Designed primarily as a reference, the first edition nevertheless saw widespread use as a textbook in graduate and undergraduate university courses dealing with the theories and associated algorithms, resources, and tools used in the analysis, prediction, and theoretical underpinnings of DNA, RNA, and proteins. This new edition contains not only thorough updates of the advances in structural bioinformatics since publication of the first edition, but also features eleven new chapters dealing with frontier areas of high scientific impact, including: sampling and search techniques; use of mass spectrometry; genome functional annotation; and much more. Offering detailed coverage for practitioners while remaining accessible to the novice, Structural Bioinformatics, Second Edition is a valuable resource and an excellent textbook for a range of readers in the bioinformatics and advanced biology fields. Praise for the previous edition: "This book is a gold mine of fundamental and practical information in an area not previously well represented in book form." —Biochemistry and Molecular Education "... destined to become a classic reference work for workers at all levels in structural bioinformatics...recommended with great enthusiasm for educators, researchers, and graduate students." —BAMBED "...a useful and timely summary of a rapidly expanding field." —Nature Structural Biology "...a terrific job in this timely creation of a compilation of articles that appropriately addresses this issue." —Briefings in Bioinformatics

Written by leading market risk academic, Professor Carol Alexander, Value-at-Risk Models forms part four of the Market Risk Analysis four volume set. Building on the three previous volumes this book provides by far the most comprehensive, rigorous and detailed treatment of market VaR models. It rests on the basic knowledge of financial mathematics and statistics gained from Volume I, of factor models, principal component analysis, statistical models of volatility and correlation and copulas from Volume II and, from Volume III, knowledge of pricing and hedging financial instruments and of mapping portfolios of similar instruments to risk factors. A unifying characteristic of the series is the pedagogical approach to practical examples that are relevant to market risk analysis in practice. All together, the Market Risk Analysis four volume set illustrates virtually every concept or formula with a practical, numerical example or a longer, empirical case study. Across all four volumes there are approximately 300 numerical and empirical examples, 400 graphs and figures and 30 case studies many of which are contained in interactive Excel spreadsheets available from the the accompanying CD-ROM . Empirical examples and case studies specific to this volume include: Parametric linear value at risk (VaR)models: normal, Student t and normal mixture and their expected tail loss (ETL); New formulae for VaR based on autocorrelated returns; Historical simulation VaR models: how to scale historical VaR and volatility adjusted historical VaR; Monte Carlo simulation VaR models based on multivariate normal and Student t distributions, and based on copulas; Examples and case studies of numerous applications to interest rate sensitive, equity, commodity and international portfolios; Decomposition of systematic VaR of large portfolios into standard alone and marginal VaR components; Backtesting and the assessment of risk model risk; Hypothetical factor push and historical stress tests, and stress testing based on VaR and ETL. "The ebook version does not provide access to the companion files".

The first textbook of its kind, Quantitative Corpus Linguistics with R demonstrates how to use the open source programming language R for corpus linguistic analyses. Computational and corpus linguists doing corpus work will find that R provides an enormous range of functions that currently require several programs to achieve – searching and processing corpora, arranging and outputting the results of corpus searches, statistical evaluation, and graphing.

In this Wrox Blox, you'll learn how to create powerful Dashboards for Windows SharePoint Services 3.0. First, we introduce Web Part Pages and some of the out-of-the box Web Parts available in WSS. We then look at how to use Web Part Connections to add interactivity to our Dashboards. Later we create advanced Dashboard Views using the Data Form Web Part available with SharePoint Designer 2007. While the author focuses on Windows SharePoint Services, all of the topics discussed also apply to Microsoft Office SharePoint Server 2007 as it is a superset of WSS. This Wrox Blox will be valuable for anyone wishing to share data on their SharePoint site.

Written by a member of the R Development Core Team, this resource provides important information on how to work with research data. It contains a collection of diverse, computer-related topics, connecting them through numerous, real-world case studies. The author describes open source technologies and open standards and devotes separate chapters to each computer language, including HTML, XML, SQL, and R. Explanatory diagrams aid in understanding important concepts, helping readers perform research tasks with ease. In addition, the author's website includes a suite of exercises as well as the code and data sets used in the case studies.

Get the end-to-end instruction you need to design, develop, and deploy more effective data integration, reporting, and analysis solutions using SQL Server 2008—whether you’re new to business intelligence (BI) programming or a seasoned pro. With real-world examples and insights from an expert team, you’ll master the concepts, tools, and techniques for building solutions that deliver intelligence—and business value—exactly where users want it. Discover how to: Manage the development life cycle and build a BI team Dig into SQL Server Analysis Services, Integration Services, and Reporting Services Navigate the Business Intelligence Development Studio (BIDS) Write queries that rank, sort, and drill down on sales data Develop extract, transform, and load (ETL) solutions Add a source code control system Help secure packages for deployment via encryption and credentials Use MDX and DMX Query Designers to build reports based on OLAP cubes and data mining models Create and implement custom objects using .NET code View reports in Microsoft Office Excel and Office SharePoint Serverook

This book fills a need for trainers, participants, and managers by providing a practical guide on how to get the most from a learning and development program. The book offers proven tools that help training participants get the most from the programs and includes the tools necessary to the transfer and application of critical new learning. The book explains how to create an environment that supports the participant's successful transition from program learning to producing valuable results. The tools and suggestions are a formula for success that will add value to virtually any learning and development initiative.

A hands-on guide to making valuable decisions from data using advanced data mining methods and techniques This second installment in the Making Sense of Data series continues to explore a diverse range of commonly used approaches to making and communicating decisions from data. Delving into more technical topics, this book equips readers with advanced data mining methods that are needed to successfully translate raw data into smart decisions across various fields of research including business, engineering, finance, and the social sciences. Following a comprehensive introduction that details how to define a problem, perform an analysis, and deploy the results, Making Sense of Data II addresses the following key techniques for advanced data analysis: Data Visualization reviews principles and methods for understanding and communicating data through the use of visualization including single variables, the relationship between two or more variables, groupings in data, and dynamic approaches to interacting with data through graphical user interfaces. Clustering outlines common approaches to clustering data sets and provides detailed explanations of methods for determining the distance between observations and procedures for clustering observations. Agglomerative hierarchical clustering, partitioned-based clustering, and fuzzy clustering are also discussed. Predictive Analytics presents a discussion on how to build and assess models, along with a series of predictive analytics that can be used in a variety of situations including principal component analysis, multiple linear regression, discriminate analysis, logistic regression, and Naïve Bayes. Applications demonstrates the current uses of data mining across a wide range of industries and features case studies that illustrate the related applications in real-world scenarios. Each method is discussed within the context of a data mining process including defining the problem and deploying the results, and readers are provided with guidance on when and how each method should be used. The related Web site for the series ( www.makingsenseofdata.com) provides a hands-on data analysis and data mining experience. Readers wishing to gain more practical experience will benefit from the tutorial section of the book in conjunction with the TraceisTM software, which is freely available online. With its comprehensive collection of advanced data mining methods coupled with tutorials for applications in a range of fields, Making Sense of Data II is an indispensable book for courses on data analysis and data mining at the upper-undergraduate and graduate levels. It also serves as a valuable reference for researchers and professionals who are interested in learning how to accomplish effective decision making from data and understanding if data analysis and data mining methods could help their organization.

Using a range of examples, this introduction illustrates how R can be employed to tackle statistical problems. It covers one-sample analysis and distribution diagnostics, regression, two-sample problems and comparison of distributions, and multivariate analysis.