This book shows you how to analyze data sets systematically and to use Excel 2019 to extract information from data almost effortlessly. Both are (not) an art! The statistical methods are presented and discussed using a single data set. This makes it clear how the methods build on each other and gradually more and more information can be extracted from the data. The Excel functions used are explained in detail - the procedure can be easily transferred to other data sets. Various didactic elements facilitate orientation and working with the book: At the checkpoints, the most important aspects from each chapter are briefly summarized. In the freak knowledge section, more advanced aspects are addressed to whet the appetite for more. All examples are calculated with hand and Excel. Numerous applications and solutions as well as further data sets are available on the author's internet platform. This book is a translation of the original German 2nd edition Statistik angewandt mit Excel by Franz Kronthaler, published by Springer-Verlag GmbH Germany, part of Springer Nature in 2021. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). A subsequent human revision was done primarily in terms of content, so that the book will read stylistically differently from a conventional translation. Springer Nature works continuously to further the development of tools for the production of books and on the related technologies to support the authors.

Bootstrapping is a conceptually simple statistical technique to increase the quality of estimates, conduct robustness checks and compute standard errors for virtually any statistic. This book provides an intelligible and compact introduction for students, scientists and practitioners. It not only gives a clear explanation of the underlying concepts but also demonstrates the application of bootstrapping using Python and Stata.

Get started with an accelerated introduction to the R ecosystem, programming language, and tools including R script and RStudio. Utilizing many examples and projects, this book teaches you how to get data into R and how to work with that data using R. Once grounded in the fundamentals, the rest of Practical R 4 dives into specific projects and examples starting with running and analyzing a survey using R and LimeSurvey. Next, you'll carry out advanced statistical analysis using R and MouselabWeb. Then, you'll see how R can work for you without statistics, including how R can be used to automate data formatting, manipulation, reporting, and custom functions. The final part of this book discusses using R on a server; you'll build a script with R that can run an RStudio Server and monitor a report source for changes to alert the user when something has changed. This project includes both regular email alerting and push notification. And, finally, you'll use R to create a customized daily rundown report of a person's most important information such as a weather report, daily calendar, to-do's and more. This demonstrates how to automate such a process so that every morning, the user navigates to the same web page and gets the updated report. What You Will Learn Set up and run an R script, including installation on a new machine and downloading and configuring R Turn any machine into a powerful data analytics platform accessible from anywhere with RStudio Server Write basic R scripts and modify existing scripts to suit your own needs Create basic HTML reports in R, inserting information as needed Build a basic R package and distribute it Who This Book Is For Some prior exposure to statistics, programming, and maybe SAS is recommended but not required.

Today, information technology plays a pivotal role in financial control and audit: most financial data is now digitally recorded and dispersed among servers, clouds and networks over which the audited firm has no control. Additionally, a firm's data-particularly in the case of finance, software, insurance and biotech firms- comprises most of the audited value of the firm. Financial audits are critical mechanisms for ensuring the integrity of information systems and the reporting of organizational finances. They help avoid the abuses that led to passage of legislation such as the Foreign Corrupt Practices Act (1977), and the Sarbanes-Oxley Act (2002). Audit effectiveness has declined over the past two decades as auditor skillsets have failed to keep up with advances in information technology. Information and communication technology lie at the core of commerce today and are integrated in business processes around the world. This book is designed to meet the increasing need of audit professionals to understand information technology and the controls required to manage it. The material included focuses on the requirements for annual Securities and Exchange Commission audits (10-K) for listed corporations. These represent the benchmark auditing procedures for specialized audits, such as internal, governmental, and attestation audits. Using R and RStudio, the book demonstrates how to render an audit opinion that is legally and statistically defensible; analyze, extract, and manipulate accounting data; build a risk assessment matrix to inform the conduct of a cost-effective audit program; and more.

Features a straightforward and concise resource for introductory statistical concepts, methods, and techniques using R Understanding and Applying Basic Statistical Methods Using R uniquely bridges the gap between advances in the statistical literature and methods routinely used by non-statisticians. Providing a conceptual basis for understanding the relative merits and applications of these methods, the book features modern insights and advances relevant to basic techniques in terms of dealing with non-normality, outliers, heteroscedasticity (unequal variances), and curvature. Featuring a guide to R, the book uses R programming to explore introductory statistical concepts and standard methods for dealing with known problems associated with classic techniques. Thoroughly class-room tested, the book includes sections that focus on either R programming or computational details to help the reader become acquainted with basic concepts and principles essential in terms of understanding and applying the many methods currently available. Covering relevant material from a wide range of disciplines, Understanding and Applying Basic Statistical Methods Using R also includes: * Numerous illustrations and exercises that use data to demonstrate the practical importance of multiple perspectives * Discussions on common mistakes such as eliminating outliers and applying standard methods based on means using the remaining data * Detailed coverage on R programming with descriptions on how to apply both classic and more modern methods using R * A companion website with the data and solutions to all of the exercises Understanding and Applying Basic Statistical Methods Using R is an ideal textbook for an undergraduate and graduate-level statistics courses in the science and/or social science departments. The book can also serve as a reference for professional statisticians and other practitioners looking to better understand modern statistical methods as well as R programming. Rand R. Wilcox, PhD, is Professor in the Department of Psychology at the University of Southern California, Fellow of the Association for Psychological Science, and an associate editor for four statistics journals. He is also a member of the International Statistical Institute. The author of more than 320 articles published in a variety of statistical journals, he is also the author eleven other books on statistics. Dr. Wilcox is creator of WRS (Wilcox Robust Statistics), which is an R package for performing robust statistical methods. His main research interest includes statistical methods, particularly robust methods for comparing groups and studying associations.

Most transformations and large-scale change programs fail, but in a rapidly changing world change is becoming more and more critical for survival. The HERO Transformation Playbook is your step-by-step playbook of EXACTLY how to deliver successful transformations and large-scale change programs with the best chance of success using the HERO Transformation Framework: a clear method to help you design transformation for maximum enterprise value creation and then deliver the outcome in a repeatable fashion. We built our framework through trial and error, learning from our mistakes and successes and solving common issues we came across and pitfalls that we have seen time and again. We then spent many years honing the framework, removing the fluff, distilling the concepts until it contained everything you need to succeed in the challenging world of change. In this book we teach you everything we've learned - including all of the roles, processes, meetings, governance, and templates for you to follow and apply to your transformation today - so that you can crack the code of change and lead successful transformations on your own. The more successful transformations that are delivered, the better the world will be for everyone!

Take control of your personal finances with this popular, easy guide

Quicken is the country's most popular and highly recommended personal finance software, and with "Quicken For Dummies," you can harness the power of this practical financial tool to manage bills, reconcile bank accounts, track investments, and much more

Personal finance expert and CPA Stephen L. Nelson lends his expertise to this accessible, easy-to-read guide, which has been fully updated to reflect the new features of Quicken available in its newest version. You can quickly learn how to use Quicken to record credit card transactions, track mortgages and other liabilities, minimize taxes, maximize savings, maintain detailed records, and build a solid foundation for creating better financial plans.Covers updated features in the newest version of the increasingly popular Quicken softwareEmpowers readers to fight the bad habits and decisions that lead to large debts and minimal or nonexistent savingsDemystifies the software's features for tracking everyday finances, managing investments, and evaluating the tax implications of financial decisionsOffers invaluable advice and expertise from author Stephen L. Nelson, a recognized authority on personal finance

Every step toward better finances is a step in the right direction. Let "Quicken For Dummies" start your journey toward increased savings, lowered debt, and a more secure financial future.

Programming for Electrical Engineers: MATLAB and Spice introduces beginning engineering students to programming in Matlab and Spice through engaged, problem-based learning and dedicated electrical and computer engineering content. The book draws its problems and examples specifically from electrical and computer engineering, covering such topics as circuit analysis, signal processing, and filter design. It teaches relevant computational techniques in the context of solving common problems in electrical and computer engineering, including mesh and nodal analysis, Fourier transforms, and phasor analysis. Programming for Electrical Engineers: MATLAB and Spice is unique among MATLAB textbooks for its dual focus on introductory-level learning and discipline-specific content in electrical and computer engineering. No other textbook on the market currently targets this audience with the same attention to discipline-specific content and engaged learning practices. Although it is primarily an introduction to programming in MATLAB, the book also has a chapter on circuit simulation using Spice, and it includes materials required by ABET Accreditation reviews, such as information on ethics, professional development, and lifelong learning.

The purpose of this guide is to give a quick introduction on how to use Maple. It primarily covers Maple 12, although most of the guide will work with earlier versions of Maple. Also, throughout this guide, we will be suggesting tips and diagnosing common problems that users are likely to encounter. This should make the learning process smoother.

This guide is designed as a self-study tutorial to learn Maple. Our emphasis is on getting you quickly up to speed. This guide can also be used as a supplement (or reference) for students taking a mathematics (or science) course that requires use of Maple, such as Calculus, Multivariable Calculus, Advanced Calculus, Linear Algebra, Discrete Mathematics, Modeling, or Statistics.

Make your data work for you! Tableau For Dummies brings order to the chaotic world of data. Understanding your data and organizing it into formats and visualizations that make sense to you are crucial to making a real impact on your business with the information that's already at your fingertips. This easy-to-use reference explores the user interface, and guides you through the process of connecting your data sources to the software. Additionally, this approachable, yet comprehensive text shows you how to use graphs, charts, and other images to bring visual interest to your data, how to create dashboards from multiple data sources, and how to export the visualizations that you have developed into multiple formats that translate into positive change for your business. The mission of Tableau Software is to grant you access to data that, when put into action, will help you build your company. Learning to use the data available to you helps you make informed, grounded business decisions that can spell success for your company. * Navigate the user interface to efficiently access the features you need * Connect to various spreadsheets, databases, and other data sources to create a multi-dimensional snapshot of your business * Develop visualizations with easy to use drag and drop features * Start building your data with templates and sample workbooks to spark your creativity and help you organize your information Tableau For Dummies is a step-by-step resource that helps you make sense of the data landscape and put your data to work in support of your business.

The introduction of digital applications into businesses has revolutionized the way employees and managers carry out their jobs while also benefiting them socially. Smartphone and App Implementations that Improve Productivity looks at the benefits of apps in the workplace and introduces academic perspectives that link prospective advantages with practical commercial examples. The analysis is structured into chapters that include real world application while at the same time critically assess implied benefits of the new app technology and draw out the main findings and conclusions. Tahir M. Nisar brings into focus the emerging role of digital applications and big data in enterprise decision making. Readers will learn how companies can achieve more efficiency and effectiveness in their business operations through new types of organizational design strategies and mechanisms of employee mobility and work-life balance that draw on digital apps.

Turn your R code into packages that others can easily download and use. This practical book shows you how to bundle reusable R functions, sample data, and documentation together by applying author Hadley Wickham's package development philosophy. In the process, you'll work with devtools, roxygen, and testthat, a set of R packages that automate common development tasks. Devtools encapsulates best practices that Hadley has learned from years of working with this programming language. Ideal for developers, data scientists, and programmers with various backgrounds, this book starts you with the basics and shows you how to improve your package writing over time. You'll learn to focus on what you want your package to do, rather than think about package structure.Learn about the most useful components of an R package, including vignettes and unit tests Automate anything you can, taking advantage of the years of development experience embodied in devtools Get tips on good style, such as organizing functions into files Streamline your development process with devtools Learn the best way to submit your package to the Comprehensive R Archive Network (CRAN) Learn from a well-respected member of the R community who created 30 R packages, including ggplot2, dplyr, and tidyr

Quantitative Analysis and Modeling of Earth and Environmental Data: Space-Time and Spacetime Data Considerations introduces the notion of chronotopologic data analysis that offers a systematic, quantitative analysis of multi-sourced data and provides information about the spatial distribution and temporal dynamics of natural attributes (physical, biological, health, social). It includes models and techniques for handling data that may vary by space and/or time, and aims to improve understanding of the physical laws of change underlying the available numerical datasets, while taking into consideration the in-situ uncertainties and relevant measurement errors (conceptual, technical, computational). It considers the synthesis of scientific theory-based methods (stochastic modeling, modern geostatistics) and data-driven techniques (machine learning, artificial neural networks) so that their individual strengths are combined by acting symbiotically and complementing each other. The notions and methods presented in Quantitative Analysis and Modeling of Earth and Environmental Data: Space-Time and Spacetime Data Considerations cover a wide range of data in various forms and sources, including hard measurements, soft observations, secondary information and auxiliary variables (ground-level measurements, satellite observations, scientific instruments and records, protocols and surveys, empirical models and charts). Including real-world practical applications as well as practice exercises, this book is a comprehensive step-by-step tutorial of theory-based and data-driven techniques that will help students and researchers master data analysis and modeling in earth and environmental sciences (including environmental health and human exposure applications).

Engaging and accessible, this book teaches readers how to use inferential statistical thinking to check their assumptions, assess evidence about their beliefs, and avoid overinterpreting results that may look more promising than they really are. It provides step-by-step guidance for using both classical (frequentist) and Bayesian approaches to inference. Statistical techniques covered side by side from both frequentist and Bayesian approaches include hypothesis testing, replication, analysis of variance, calculation of effect sizes, regression, time series analysis, and more. Students also get a complete introduction to the open-source R programming language and its key packages. Throughout the text, simple commands in R demonstrate essential data analysis skills using real-data examples. The companion website provides annotated R code for the book's examples, in-class exercises, supplemental reading lists, and links to online videos, interactive materials, and other resources. Pedagogical Features *Playful, conversational style and gradual approach; suitable for students without strong math backgrounds. *End-of-chapter exercises based on real data supplied in the free R package. *Technical explanation and equation/output boxes. *Appendices on how to install R and work with the sample datasets.

This textbook describes the broadening methodology spectrum of psychological measurement in order to meet the statistical needs of a modern psychologist. The way statistics is used, and maybe even perceived, in psychology has drastically changed over the last few years; computationally as well as methodologically. R has taken the field of psychology by storm, to the point that it can now safely be considered the lingua franca for statistical data analysis in psychology. The goal of this book is to give the reader a starting point when analyzing data using a particular method, including advanced versions, and to hopefully motivate him or her to delve deeper into additional literature on the method. Beginning with one of the oldest psychometric model formulations, the true score model, Mair devotes the early chapters to exploring confirmatory factor analysis, modern test theory, and a sequence of multivariate exploratory method. Subsequent chapters present special techniques useful for modern psychological applications including correlation networks, sophisticated parametric clustering techniques, longitudinal measurements on a single participant, and functional magnetic resonance imaging (fMRI) data. In addition to using real-life data sets to demonstrate each method, the book also reports each method in three parts-- first describing when and why to apply it, then how to compute the method in R, and finally how to present, visualize, and interpret the results. Requiring a basic knowledge of statistical methods and R software, but written in a casual tone, this text is ideal for graduate students in psychology. Relevant courses include methods of scaling, latent variable modeling, psychometrics for graduate students in Psychology, and multivariate methods in the social sciences.

Written at a readily accessible level, " Basic Data Analysis for Time Series with R" emphasizes the mathematical importance of collaborative analysis of data used to collect increments of time or space. Balancing a theoretical and practical approach to analyzing data within the context of serial correlation, the book presents a coherent and systematic regression-based approach to model selection. The book illustrates these principles of model selection and model building through the use of information criteria, cross validation, hypothesis tests, and confidence intervals.

Focusing on frequency- and time-domain and trigonometric regression as the primary themes, the book also includes modern topical coverage on Fourier series and Akaike's Information Criterion (AIC). In addition, "Basic Data Analysis for Time Series with R" also features:

Real-world examples to provide readers with practical hands-on experienceMultiple R software subroutines employed with graphical displaysNumerous exercise sets intended to support readers understanding of the core conceptsSpecific chapters devoted to the analysis of the Wolf sunspot number data and the Vostok ice core data sets

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.

Hugely successful and popular text presenting an extensive and comprehensive guide for all R users

The R language is recognized as one of the most powerful and flexible statistical software packages, enabling users to apply many statistical techniques that would be impossible without such software to help implement such large data sets. R has become an essential tool for understanding and carrying out research.

This edition: Features full colour text and extensive graphics throughout.Introduces a clear structure with numbered section headings to help readers locate information more efficiently.Looks at the evolution of R over the past five years.Features a new chapter on Bayesian Analysis and Meta-Analysis.Presents a fully revised and updated bibliography and reference section.Is supported by an accompanying website allowing examples from the text to be run by the user.

Praise for the first edition:

'...if you are an R user or wannabe R user, this text is the one that should be on your shelf. The breadth of topics covered is unsurpassed when it comes to texts on data analysis in R.' ("The American Statistician," August 2008)'The High-level software language of R is setting standards in quantitative analysis. And now anybody can get to grips with it thanks to The R Book...' ("Professional Pensions, "July 2007)

Written by two prominent industry leaders in DevOps, this book will help you to discover various cloud services alongside modern software development practices and tools to guide your journey of continuous delivery in the cloud. Key Features * Modernize continuous delivery in the cloud with strategic goals and objectives * Master continuous delivery with the right tools, applications, and use cases * Efficiently perform multi-cluster and multi-cloud deployments Book Description Many organizations are embracing cloud technology to remain competitive. Implementing and adopting your development processes, tools, and techniques while modernizing cloud-based ecosystem can be challenging. Strategizing Continuous Delivery in the Cloud helps you to understand why you need to modernize continuous delivery and how to drive convergence of the infrastructure and the application deployment in cloud. You will begin with discovering how continuous delivery in the cloud is different from traditional approaches and understand the steps to build the right strategy that works best for your organization. Further, you will be able to develop, test, integrate, deploy, and operate cloud native software to different types of cloud-based environments. The book covers in detail the prerequisites for onboarding to continuous delivery in the cloud from an organization and technical perspective. You will delve into key aspects of organizational and technological readiness to lay out successful strategies for overcoming core challenges in your cloud journey. Finally, you will know the dos and don'ts to stay on top of your DevOps game long after the starting point. By the end of this book, you will have a solid understanding of choosing the right type of cloud environment and the right technologies for constructing your CD pipelines. What you will learn * Uncover the foundation of and prepare for continuous delivery in cloud * Build fast, efficient, secure, and interoperable software for real world results * Understand E-2-E continuous delivery for multi-cloud, hybrid and on-premise * Set up and scale continuous delivery in cloud for maximum return * Implement cost optimization for continuous delivery in cloud * Discover trends and advancements in CD with cloud native technologies Who This Book Is For This book is targeted towards Operations Engineers, Site Reliability Engineers, DevOps Architects and Engineers who are strategizing, planning, and implementing continuous delivery in the cloud. Readers are expected to have a basic understanding of CI/CD concepts and are familiar with cloud ecosystem, DevOps, or CI/CD pipelines.

This book provides a complete and comprehensive guide to Pyomo (Python Optimization Modeling Objects) for beginning and advanced modelers, including students at the undergraduate and graduate levels, academic researchers, and practitioners. Using many examples to illustrate the different techniques useful for formulating models, this text beautifully elucidates the breadth of modeling capabilities that are supported by Pyomo and its handling of complex real-world applications. In the third edition, much of the material has been reorganized, new examples have been added, and a new chapter has been added describing how modelers can improve the performance of their models. The authors have also modified their recommended method for importing Pyomo. A big change in this edition is the emphasis of concrete models, which provide fewer restrictions on the specification and use of Pyomo models. Pyomo is an open source software package for formulating and solving large-scale optimization problems. The software extends the modeling approach supported by modern AML (Algebraic Modeling Language) tools. Pyomo is a flexible, extensible, and portable AML that is embedded in Python, a full-featured scripting language. Python is a powerful and dynamic programming language that has a very clear, readable syntax and intuitive object orientation. Pyomo includes Python classes for defining sparse sets, parameters, and variables, which can be used to formulate algebraic expressions that define objectives and constraints. Moreover, Pyomo can be used from a command-line interface and within Python's interactive command environment, which makes it easy to create Pyomo models, apply a variety of optimizers, and examine solutions.

Der mathematische Ratgeber fur die ersten beiden Studienjahre! Wer im Nebenfach Mathematik studieren muss, findet hier das wesentliche mathematische Wissen ubersichtlich zusammengestellt und ausfuhrlich erklart! Viele Beispiele, ein umfangreicher UEbungsteil und die konsequente Einbeziehung von WolframAlpha, der freien "Wissensmaschine" von Wolfram Research, geben Hilfe und Orientierung beim Erlernen der Mathematik an Hochschulen. Abiturienten bei der Vorbereitung auf ein naturwissenschaftlich-technisches, Ingenieur-, OEkonomie- usw. Studium aber auch Studierende, die den UEberblick in Sachen Mathematik behalten wollen, werden die "kommentierte Formelsammlung" mit Begeisterung zur Hand nehmen.

Get everything you need to code with ABAP, all in one place! Are you a beginner looking for a refresher on the basics? You'll get an overview of SAP architecture and learn syntax. Already an experienced programmer and looking to improve your ABAP skills? Dive right into modifications and code enhancements. Understand the programming environment and build reports, interfaces, and applications with this complete reference to coding with ABAP! Highlights include: 1) Architecture and environment 2) Syntax, keywords, structures, and tables 3) User interaction 4) Modularization 5) Persistent data 6) Dialog and dynamic programming 7) Reports and forms 8) Interface development 9) Modifications and enhancements 10) Debugging and testing

Plot graphs, solve equations, and write code in a flash! If you work in a STEM field, chances are you'll be using MATLAB on a daily basis. MATLAB is a popular and powerful computational tool and this book provides everything you need to start manipulating and plotting your data. MATLAB has rapidly become the premier data tool, and MATLAB For Dummies is a comprehensive guide to the fundamentals. MATLAB For Dummies guides you through this complex computational language from installation to visualization to automation. * Learn MATLAB's language fundamentals including syntax, operators, and data types * Understand how to use the most important window in MATLAB the Command Window * Get the basics of linear algebra to get up and running with vectors, matrices, and hyperspace * Automate your work with programming scripts and functions * Plot graphs in 2D and 3D to visualize your data * Includes a handy guide for MATLAB's functions and plotting routines MATLAB is an essential part of the analysis arsenal and MATLAB For Dummies provides clear, thorough guidance to get the most out of your data.

An Introduction to Stata for Health Researchers, Fifth Edition updates this classic book that has become a standard reference for health researchers. As with previous editions, readers will learn to work effectively in Stata to perform data management, compute descriptive statistics, create meaningful graphs, fit regression models, and perform survival analysis. The fifth edition adds examples of performing power, precision, and sample-size analysis; working with Unicode characters; managing data with ICD-9 and ICD-10 codes; and creating customized tables. With many worked examples and downloadable datasets, this text is the ideal resource for hands-on learning, whether for students in a statistics course or for researchers in fields such as epidemiology, biostatistics, and public health who are learning to use Stata's tools for health research.