Practical Statistical Methods: A SAS Programming Approach presents a broad spectrum of statistical methods useful for researchers without an extensive statistical background. In addition to nonparametric methods, it covers methods for discrete and continuous data. Omitting mathematical details and complicated formulae, the text provides SAS programs to carry out the necessary analyses and draw appropriate inferences for common statistical problems. After introducing fundamental statistical concepts, the author describes methods used for quantitative data and continuous data following normal and nonnormal distributions. She then focuses on regression methodology, highlighting simple linear regression, logistic regression, and the proportional hazards model. The final chapter briefly discusses such miscellaneous topics as propensity scores, misclassification errors, interim analysis, conditional power, bootstrap, and jackknife. With SAS code and output integrated throughout, this book shows how to interpret data using SAS and illustrates the many statistical methods available for tackling problems in a range of fields, including the pharmaceutical industry and the social sciences.

Methods of Statistical Model Estimation examines the most important and popular methods used to estimate parameters for statistical models and provide informative model summary statistics. Designed for R users, the book is also ideal for anyone wanting to better understand the algorithms used for statistical model fitting. The text presents algorithms for the estimation of a variety of regression procedures using maximum likelihood estimation, iteratively reweighted least squares regression, the EM algorithm, and MCMC sampling. Fully developed, working R code is constructed for each method. The book starts with OLS regression and generalized linear models, building to two-parameter maximum likelihood models for both pooled and panel models. It then covers a random effects model estimated using the EM algorithm and concludes with a Bayesian Poisson model using Metropolis-Hastings sampling. The book's coverage is innovative in several ways. First, the authors use executable computer code to present and connect the theoretical content. Therefore, code is written for clarity of exposition rather than stability or speed of execution. Second, the book focuses on the performance of statistical estimation and downplays algebraic niceties. In both senses, this book is written for people who wish to fit statistical models and understand them. See Professor Hilbe discuss the book.

This book provides a quick access to computational tools for algebraic geometry, the mathematical discipline which handles solution sets of polynomial equations. Originating from a number of intense one week schools taught by the authors, the text is designed so as to provide a step by step introduction which enables the reader to get started with his own computational experiments right away. The authors present the basic concepts and ideas in a compact way.

Clustering is one of the most fundamental and essential data analysis techniques. Clustering can be used as an independent data mining task to discern intrinsic characteristics of data, or as a preprocessing step with the clustering results then used for classification, correlation analysis, or anomaly detection.

Kogan and his co-editors have put together recent advances in clustering large and high-dimension data. Their volume addresses new topics and methods which are central to modern data analysis, with particular emphasis on linear algebra tools, opimization methods and statistical techniques. The contributions, written by leading researchers from both academia and industry, cover theoretical basics as well as application and evaluation of algorithms, and thus provide an excellent state-of-the-art overview.

The level of detail, the breadth of coverage, and the comprehensive bibliography make this book a perfect fit for researchers and graduate students in data mining and in many other important related application areas.

This open access book provides insights into the novel Locally Refined B-spline (LR B-spline) surface format, which is suited for representing terrain and seabed data in a compact way. It provides an alternative to the well know raster and triangulated surface representations. An LR B-spline surface has an overall smooth behavior and allows the modeling of local details with only a limited growth in data volume. In regions where many data points belong to the same smooth area, LR B-splines allow a very lean representation of the shape by locally adapting the resolution of the spline space to the size and local shape variations of the region. The iterative method can be modified to improve the accuracy in particular domains of a point cloud. The use of statistical information criterion can help determining the optimal threshold, the number of iterations to perform as well as some parameters of the underlying mathematical functions (degree of the splines, parameter representation). The resulting surfaces are well suited for analysis and computing secondary information such as contour curves and minimum and maximum points. Also deformation analysis are potential applications of fitting point clouds with LR B-splines.

In the history of mathematics there are many situations in which cal- lations were performed incorrectly for important practical applications. Let us look at some examples, the history of computing the number ? began in Egypt and Babylon about 2000 years BC, since then many mathematicians have calculated ? (e. g. , Archimedes, Ptolemy, Vi` ete, etc. ). The ?rst formula for computing decimal digits of ? was disc- ered by J. Machin (in 1706), who was the ?rst to correctly compute 100 digits of ?. Then many people used his method, e. g. , W. Shanks calculated ? with 707 digits (within 15 years), although due to mistakes only the ?rst 527 were correct. For the next examples, we can mention the history of computing the ?ne-structure constant ? (that was ?rst discovered by A. Sommerfeld), and the mathematical tables, exact - lutions, and formulas, published in many mathematical textbooks, were not veri?ed rigorously [25]. These errors could have a large e?ect on results obtained by engineers. But sometimes, the solution of such problems required such techn- ogy that was not available at that time. In modern mathematics there exist computers that can perform various mathematical operations for which humans are incapable. Therefore the computers can be used to verify the results obtained by humans, to discovery new results, to - provetheresultsthatahumancanobtainwithoutanytechnology. With respectto our example of computing?, we can mention that recently (in 2002) Y. Kanada, Y. Ushiro, H. Kuroda, and M.

This book presents a rigorous mathematical development of soil water and contaminant flow in variably saturated and saturated soils. Analytical and numerical methods are balanced: computer programs, among them MathCad and Fortran, are presented, and more than 150 practice and discussion questions are included. Students are thus exposed not only to theory but also to an array of solutions techniques. Those using the book as a reference will appreciate the careful development of basic flow equations, the inclusion of solutions and methodology currently available only in journals and proceedings volumes, and the examples and calculations directly applicable to their own work.

State space models have gained tremendous popularity in recent years in as disparate fields as engineering, economics, genetics and ecology. After a detailed introduction to general state space models, this book focuses on dynamic linear models, emphasizing their Bayesian analysis. Whenever possible it is shown how to compute estimates and forecasts in closed form; for more complex models, simulation techniques are used. A final chapter covers modern sequential Monte Carlo algorithms.

The book illustrates all the fundamental steps needed to use dynamic linear models in practice, using R. Many detailed examples based on real data sets are provided to show how to set up a specific model, estimate its parameters, and use it for forecasting. All the code used in the book is available online.

No prior knowledge of Bayesian statistics or time series analysis is required, although familiarity with basic statistics and R is assumed.

The SharePoint user experience is critical in application architecture and user acceptance. Using tools available to all developers, Building the SharePoint User Experience will show you how to rebuild a SharePoint site, taking it all the way from the default outofthebox experience to your very own customized user experience.

Along the way you will receive a solid understanding of the SharePoint architecture that will enable you to take full advantage of the capabilities of SharePoint as a platform. This will allow you to tailor the SharePoint user experience to increase the value of solutions and to work more effectively with projects. And that, of course, leads to successful SharePoint solutions in your business that your users are happy to accept and use. What you'll learn Learn the tools needed to create effective and highly tailored user interfaces and experiences. Dissect an outofthebox site to learn how every user experience element is built. Master the core functions of sites, lists, content types, and fields. Build a site from scratch using tools available to everyone. Speed up development time by using little known tips and tricks. See how new skills can be applied to everyday tasks with simple exercises. Who this book is for

This book is for SharePoint developers who want to learn how to work with designers and other developers to create custom and tailored user experiences, including custom forms, content types, lists, fields, pages, and navigation that will better match your project's requirements. This is not a book for graphic designers--you'll find no Photoshop tips here Table of Contents Checking Your Gear for Departure Taking a Crash Course in XML Exploring Feature Basics and Not-So Basics Excavating the Site Evolving the Default User Experience What Lurks in the Forest of Lists? Encountering the Monsters in the Cave The Liquid Nitrogen of SharePoint Strolling Through Fields of Gold Intermission: The Mentality of a SharePoint Developer Starting Field Creating Your First Content Factory Accounting Gone Haywire Pages and Pages of Fun Our Empire United

R for College Mathematics and Statistics encourages the use of R in mathematics and statistics courses. Instructors are no longer limited to ``nice'' functions in calculus classes. They can require reports and homework with graphs. They can do simulations and experiments. R can be useful for student projects, for creating graphics for teaching, as well as for scholarly work. This book presents ways R, which is freely available, can enhance the teaching of mathematics and statistics. R has the potential to help students learn mathematics due to the need for precision, understanding of symbols and functions, and the logical nature of code. Moreover, the text provides students the opportunity for experimenting with concepts in any mathematics course. Features: Does not require previous experience with R Promotes the use of R in typical mathematics and statistics course work Organized by mathematics topics Utilizes an example-based approach Chapters are largely independent of each other

Learn how to program by diving into the R language, and then use your newfound skills to solve practical data science problems. With this book, you'll learn how to load data, assemble and disassemble data objects, navigate R's environment system, write your own functions, and use all of R's programming tools. RStudio Master Instructor Garrett Grolemund not only teaches you how to program, but also shows you how to get more from R than just visualizing and modeling data. You'll gain valuable programming skills and support your work as a data scientist at the same time. Work hands-on with three practical data analysis projects based on casino games Store, retrieve, and change data values in your computer's memory Write programs and simulations that outperform those written by typical R users Use R programming tools such as if else statements, for loops, and S3 classes Learn how to write lightning-fast vectorized R code Take advantage of R's package system and debugging tools Practice and apply R programming concepts as you learn them

Success with Microsoft Dynamics CRM 4.0: Implementing Customer Relationship Management is aimed at readers who are interested in understanding how to successfully implement Microsoft Dynamics CRM 4.0 within their projects. It is intended as an implementation roadmap for the business and technical representatives leading or engaged in a project.

The book covers the capabilities of Microsoft Dynamics CRM, both in the traditional functional areas of sales, marketing, and service and as an applications framework for XRM deployments. The book demonstrates CRM best practices for design, configuration, and development. Through realworld solutions and exercises, you will be given the confidence and expertise to deliver an implementation that provides longterm success for your organization.

This book provides hands-on guidance for researchers and practitioners in criminal justice and criminology to perform statistical analyses and data visualization in the free and open-source software R. It offers a step-by-step guide for beginners to become familiar with the RStudio platform and tidyverse set of packages. This volume will help users master the fundamentals of the R programming language, providing tutorials in each chapter that lay out research questions and hypotheses centering around a real criminal justice dataset, such as data from the National Survey on Drug Use and Health, National Crime Victimization Survey, Youth Risk Behavior Surveillance System, The Monitoring the Future Study, and The National Youth Survey. Users will also learn how to manipulate common sources of agency data, such as calls-for-service (CFS) data. The end of each chapter includes exercises that reinforce the R tutorial examples, designed to help master the software as well as to provide practice on statistical concepts, data analysis, and interpretation of results. The text can be used as a stand-alone guide to learning R or it can be used as a companion guide to an introductory statistics textbook, such as Basic Statistics in Criminal Justice (2020).

Dem gestiegenen Wettbewerbsdruck begegnen die Unternehmen neben Rationalisierungs- und Differenzierungsmassnahmen vor allem durch Kundenbindung. Eine zentrale Rolle spielt hierbei die Ausweitung und kundenindividuelle Gestaltung von Dienstleistungsangeboten. Die modellbasierte Entwicklung und kontinuierliche Verbesserung von Dienstleistungen gewinnen daher immer mehr an Bedeutung. Der Band gibt einen Uberblick uber die hierbei einsetzbaren Methoden und zeigt zukunftige Entwicklungsperspektiven auf. Die zahlreichen Beispiele, die unterschiedlichen Branchen entstammen, fokussieren einerseits Modelle zur Dienstleistungsentwicklung und -erbringung und andererseits Modelle von Informationssystemen, welche die Entwicklung und/oder die Erbringung von Dienstleistungen unterstutzen. Sie betrachten alle Lebenszyklusphasen von Dienstleistungen sowie alle Dimensionen des Dienstleistungsbegriffs, die eine Basis zur Entwicklung von Ressourcen-, Prozess- und Produktmodellen darstellen."

Everyone has an idea that they think is the next big thing. The problem is, it's probably an app or software idea and most people probably don't know how to code and their record for managing programmers is little to none. Even if they do know how to code, they're not quite sure how to get their first one thousand customers. The Non-Technical Founder walks readers through the stages of validating whether their next big thing is good, bringing the idea to life, and getting those first customers.

The R language provides a rich environment for working with data, especially data to be used for statistical modeling or graphics. Coupled with the large variety of easily available packages, it allows access to both well-established and experimental statistical techniques. However techniques that might make sense in other languages are often very ine?cient in R, but, due to R's ?- ibility, it is often possible to implement these techniques in R. Generally, the problem with such techniques is that they do not scale properly; that is, as the problem size grows, the methods slow down at a rate that might be unexpected. The goal of this book is to present a wide variety of data - nipulation techniques implemented in R to take advantage of the way that R works, ratherthandirectlyresemblingmethodsusedinotherlanguages. Since this requires a basic notion of how R stores data, the ?rst chapter of the book is devoted to the fundamentals of data in R. The material in this chapter is a prerequisite for understanding the ideas introduced in later chapters. Since one of the ?rst tasks in any project involving data and R is getting the data into R in a way that it will be usable, Chapter 2 covers reading data from a variety of sources (text ?les, spreadsheets, ?les from other programs, etc. ), as well as saving R objects both in native form and in formats that other programs will be able to work with.

The R Companion to Elementary Applied Statistics includes traditional applications covered in elementary statistics courses as well as some additional methods that address questions that might arise during or after the application of commonly used methods. Beginning with basic tasks and computations with R, readers are then guided through ways to bring data into R, manipulate the data as needed, perform common statistical computations and elementary exploratory data analysis tasks, prepare customized graphics, and take advantage of R for a wide range of methods that find use in many elementary applications of statistics. Features: Requires no familiarity with R or programming to begin using this book. Can be used as a resource for a project-based elementary applied statistics course, or for researchers and professionals who wish to delve more deeply into R. Contains an extensive array of examples that illustrate ideas on various ways to use pre-packaged routines, as well as on developing individualized code. Presents quite a few methods that may be considered non-traditional, or advanced. Includes accompanying carefully documented script files that contain code for all examples presented, and more. R is a powerful and free product that is gaining popularity across the scientific community in both the professional and academic arenas. Statistical methods discussed in this book are used to introduce the fundamentals of using R functions and provide ideas for developing further skills in writing R code. These ideas are illustrated through an extensive collection of examples. About the Author: Christopher Hay-Jahans received his Doctor of Arts in mathematics from Idaho State University in 1999. After spending three years at University of South Dakota, he moved to Juneau, Alaska, in 2002 where he has taught a wide range of undergraduate courses at University of Alaska Southeast.

Praise for the first edition: "One of my biggest complaints when I teach introductory statistics classes is that it takes me most of the semester to get to the good stuff-inferential statistics. The author manages to do this very quickly....if one were looking for a book that efficiently covers basic statistical methodology and also introduces statistical software [this text] fits the bill." -The American Statistician Applied Statistical Inference with MINITAB, Second Edition distinguishes itself from other introductory statistics textbooks by focusing on the applications of statistics without compromising mathematical rigor. It presents the material in a seamless step-by-step approach so that readers are first introduced to a topic, given the details of the underlying mathematical foundations along with a detailed description of how to interpret the findings, and are shown how to use the statistical software program Minitab to perform the same analysis. Gives readers a solid foundation in how to apply many different statistical methods. MINITAB is fully integrated throughout the text. Includes fully worked out examples so students can easily follow the calculations. Presents many new topics such as one- and two-sample variances, one- and two-sample Poisson rates, and more nonparametric statistics. Features mostly new exercises as well as the addition of Best Practices sections that describe some common pitfalls and provide some practical advice on statistical inference. This book is written to be user-friendly for students and practitioners who are not experts in statistics, but who want to gain a solid understanding of basic statistical inference. This book is oriented towards the practical use of statistics. The examples, discussions, and exercises are based on data and scenarios that are common to students in their everyday lives.

Keep projects on track Microsoft Project 2019 is a powerhouse project management, portfolio management, and resource management tool. Whether you're a full-time project manager or manage projects as part of a larger set of duties, Microsoft Project 2019 For Dummies will get you thinking and operating at the level of a project management guru. Written by a noted project management pro, this book covers the ins and outs of Microsoft Project. Throughout the book, you'll find project management best practices and tips for keeping any project on schedule and under budget. Reference the full set of Microsoft Project 2019 features Learn to think like a project management professional Get into the nuts and bolts of Project for better productivity Create a task schedule that keeps a project moving Identify the golden rules that keep projects on track With Microsoft Project 2019 For Dummies, you'll soon get a grip on all the powerful features of this popular project management software. No matter your level of training or experience, this book will show you how improve your project management with Microsoft Project 2019.

Active Enterprise Intelligence ist der ganzheitliche Ansatz einer Informationslogistik von Teradata, der zwischen Strategic und Operational Intelligence unterscheidet, diese aber in einer integrierten Betrachtungsweise auf Basis eines unternehmensweiten Active Data Warehouses wieder zusammenfuhrt. Dieses Buch verbindet erstmals den Teradata-Ansatz mit der St. Galler Schule der Unternehmensweiten Informationslogistik. Aktuelle Herausforderungen und Losungsansatze der Informationslogistik werden thematisiert und Hinweise zu ihrer Ausgestaltung gegeben.

World-renowned software guru Joel Spolsky s company, Fog Creek Software, has created a tool called FogBugz that incorporates all of Joel s insight into what works and what doesn t work in project management. FogBugz is based on a keeping track of a database of cases. At any given time, every case is assigned to one person who must resolve it or forward it to someone else. Cases can be prioritized, documented, sorted, discussed, edited, assigned, estimated, searched, and tracked. Because FogBugz is web-based, everyone on the team always sees the whole picture. Everything from customer feature requests to high-level design discussions to tiny bug fix details is instantly searchable and trackable. Painless Project Management with FogBugz, Second Edition, written with the guidance of the whole FogBugz team, completely describes the ins and outs of the latest version of FogBugz, version 6 of which is scheduled for release simultaneously with this book.

The OMDoc (Open Mathematical Documents) format is a content markup scheme for collections of mathematical documents, including articles, textbooks, interactive books, and courses. OMDoc also serves as the content language for agent communication of mathematical services and a mathematical software bus.

This documentation describes version 1.2 of the OMDoc system, the final and mature release of OMDoc 1. The system features modularized language design, OPENMATH and MATHML for the representation of mathematical objects, and has been employed and validated in various applications.

Besides a complete and rigorous specification of the OMDoc document format, this book presents an OMDoc primer with paradigmatic examples for many kinds of mathematical documents. Furthermore, various applications, projects, and tool support for OMDoc are discussed. The book will become essential reading for all working mathematicians and mathematics students aspiring to take part in the new worlds of shared mathematical knowledge.

This book constitutes the refereed proceedings of the Second International Congress on Mathematical Software, ICMS 2006, held in Castro Urdiales, Spain in September 2006.

The 45 revised full papers presented were carefully reviewed and selected for presentation. The papers are organized in topical sections on new developments on computer algebra packages, interfacing computer algebra on mathematical visualization, software for algebraic geometry and related topics, number-theoretical software, methods in computational number theory, free software for computer algebra, software for optimization on geometric computation, methods and software for computing mathematical functions, access to mathematics on the Web, and general issues.

International Association for Statistical Computing The International Association for Statistical Computing (IASC) is a Section of the International Statistical Institute. The objectives of the Association are to foster world-wide interest in e?ective statistical computing and to - change technical knowledge through international contacts and meetings - tween statisticians, computing professionals, organizations, institutions, g- ernments and the general public. The IASC organises its own Conferences, IASC World Conferences, and COMPSTAT in Europe. The 17th Conference of ERS-IASC, the biennial meeting of European - gional Section of the IASC was held in Rome August 28 - September 1, 2006. This conference took place in Rome exactly 20 years after the 7th COMP- STAT symposium which was held in Rome, in 1986. Previous COMPSTAT conferences were held in: Vienna (Austria, 1974); West-Berlin (Germany, 1976); Leiden (The Netherlands, 1978); Edimbourgh (UK, 1980); Toulouse (France, 1982); Prague (Czechoslovakia, 1984); Rome (Italy, 1986); Copenhagen (Denmark, 1988); Dubrovnik (Yugoslavia, 1990); Neuch atel (Switzerland, 1992); Vienna (Austria,1994); Barcelona (Spain, 1996);Bristol(UK,1998);Utrecht(TheNetherlands,2000);Berlin(Germany, 2002); Prague (Czech Republic, 2004).

This third edition of Braun and Murdoch's bestselling textbook now includes discussion of the use and design principles of the tidyverse packages in R, including expanded coverage of ggplot2, and R Markdown. The expanded simulation chapter introduces the Box-Muller and Metropolis-Hastings algorithms. New examples and exercises have been added throughout. This is the only introduction you'll need to start programming in R, the computing standard for analyzing data. This book comes with real R code that teaches the standards of the language. Unlike other introductory books on the R system, this book emphasizes portable programming skills that apply to most computing languages and techniques used to develop more complex projects. Solutions, datasets, and any errata are available from www.statprogr.science. Worked examples - from real applications - hundreds of exercises, and downloadable code, datasets, and solutions make a complete package for anyone working in or learning practical data science.