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

Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today's most popular machine learning methods. This book serves as a practitioner's guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data. The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory. Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation. The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more! By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results. By the end of this book, the reader should have a firm grasp of R's machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features: * Offers a practical and applied introduction to the most popular machine learning methods. * Topics covered include feature engineering, resampling, deep learning and more. * Uses a hands-on approach and real world data.

Statistical Models in S extends the S language to fit and analyze a variety of statistical models, including analysis of variance, generalized linear models, additive models, local regression, and tree-based models. The contributions of the ten authors-most of whom work in the statistics research department at AT&T Bell Laboratories-represent results of research in both the computational and statistical aspects of modeling data.

* Targests readers with a background in programming, interested in an introduction/refresher in statistical hypothesis testing * Uses Python throughout * Provides the reader with the opportunity of using the book whenever needed rather than following a sequential path.

A start-to-finish guide to one of the most useful programming languages for researchers in a variety of fields In the newly revised Third Edition of The R Book, a team of distinguished teachers and researchers delivers a user-friendly and comprehensive discussion of foundational and advanced topics in the R software language, which is used widely in science, engineering, medicine, economics, and other fields. The book is designed to be used as both a complete text--readable from cover to cover--and as a reference manual for practitioners seeking authoritative guidance on particular topics. This latest edition offers instruction on the use of the RStudio GUI, an easy-to-use environment for those new to R. It provides readers with a complete walkthrough of the R language, beginning at a point that assumes no prior knowledge of R and very little previous knowledge of statistics. Readers will also find: A thorough introduction to fundamental concepts in statistics and step-by-step roadmaps to their implementation in R; Comprehensive explorations of worked examples in R; A complementary companion website with downloadable datasets that are used in the book; In-depth examination of essential R packages. Perfect for undergraduate and postgraduate students of science, engineering, medicine economics, and geography, The R Book will also earn a place in the libraries of social sciences professionals.

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).

The overwhelming majority of a software system's lifespan is spent in use, not in design or implementation. So, why does conventional wisdom insist that software engineers focus primarily on the design and development of large-scale computing systems? In this collection of essays and articles, key members of Google's Site Reliability Team explain how and why their commitment to the entire lifecycle has enabled the company to successfully build, deploy, monitor, and maintain some of the largest software systems in the world. You'll learn the principles and practices that enable Google engineers to make systems more scalable, reliable, and efficient-lessons directly applicable to your organization. This book is divided into four sections: Introduction-Learn what site reliability engineering is and why it differs from conventional IT industry practices Principles-Examine the patterns, behaviors, and areas of concern that influence the work of a site reliability engineer (SRE) Practices-Understand the theory and practice of an SRE's day-to-day work: building and operating large distributed computing systems Management-Explore Google's best practices for training, communication, and meetings that your organization can use

This is today's most complete guide to regression analysis with Microsoft (R) Excel for any business analytics or research task. Drawing on 25 years of advanced statistical experience, Microsoft MVP Conrad Carlberg shows how to use Excel's regression-related worksheet functions to perform a wide spectrum of practical analyses. Carlberg clearly explains all the theory you'll need to avoid mistakes, understand what your regressions are really doing, and evaluate analyses performed by others. From simple correlations and t-tests through multiple analysis of covariance, Carlberg offers hands-on, step-by-step walkthroughs using meaningful examples. He discusses the consequences of using each option and argument, points out idiosyncrasies and controversies associated with Excel's regression functions, and shows how to use them reliably in fields ranging from medical research to financial analysis to operations. You don't need expensive software or a doctorate in statistics to work with regression analyses. Microsoft Excel has all the tools you need-and this book has all the knowledge! Understand what regression analysis can and can't do, and why Master regression-based functions built into all recent versions of Excel Work with correlation and simple regression Make the most of Excel's improved LINEST() function Plan and perform multiple regression Distinguish the assumptions that matter from the ones that don't Extend your analysis options by using regression instead of traditional analysis of variance Add covariates to your analysis to reduce bias and increase statistical power

Accelerate your digital transformation and break down silos with Microsoft Dynamics 365 It's no secret that running a business involves several complex parts like managing staff, financials, marketing, and operations--just to name a few. That's where Microsoft Dynamics 365, the most profitable business management tool, comes in. In Microsoft Dynamics 365 For Dummies, you'll learn the aspects of the program and each of its applications from Customer Service to Financial Management. With expert author Renato Bellu's clear instructions and helpful tips, you'll be managing to your fullest advantage before you know it. Let's get started! Digitally transform your business by connecting CRM and ERP Use data to make decisions across all business functions Integrate Dynamics 365 with Office 365 and LinkedIn Manage financials and operations Are you running a dynamic business? This book shows you how!

This is the first ERP book to present comprehensive strategies and techniques that enable organizations to take charge of their ERP projects to drive success. The author describes how to become less dependent on outside software consultants, significantly reduce implementation and support costs, mitigate risks, and design business solutions that work for your company. This book also contains hundreds of tips to create internal project ownership, manage ERP vendors, transfer software knowledge, develop the right implementation strategies, establish a realistic schedule and budget, and streamline business processes. It is a guide for making informed decisions during each project phase. The information is applicable to new implementations and system upgrades. The book is endorsed by R "Ray" Wang, Tom F. Wallace, and Andy Klee

Provides researchers with a reproducible research workflow for using R/RStudio to make the entire researchprocess reproducible; from data gathering, to analysis, to presentation Includes instructions not only for creating reproducible research in R, but also extensively discusses how to take advantage of recent developments in RStudio. Emphasizes the presentation of reproducible research with non-print formats such as HTML5 slideshows, blogs, and other web-based content. Covers a range of techniques to organize and remotely store files at all stages of the research process. These techniques both streamline the research process, especially by making revisions easier, and enhance The book itself will be reproducible, as all of the data, analysis, and markup files will be made available online.

An R Companion to Applied Regression is a broad introduction to the R statistical computing environment in the context of applied regression analysis. John Fox and Sanford Weisberg provide a step-by-step guide to using the free statistical software R, an emphasis on integrating statistical computing in R with the practice of data analysis, coverage of generalized linear models, and substantial web-based support materials. The Third Edition has been reorganized and includes a new chapter on mixed-effects models, new and updated data sets, and a de-emphasis on statistical programming, while retaining a general introduction to basic R programming. The authors have substantially updated both the car and effects packages for R for this edition, introducing additional capabilities and making the software more consistent and easier to use. They also advocate an everyday data-analysis workflow that encourages reproducible research. To this end, they provide coverage of RStudio, an interactive development environment for R that allows readers to organize and document their work in a simple and intuitive fashion, and then easily share their results with others. Also included is coverage of R Markdown, showing how to create documents that mix R commands with explanatory text. "An R Companion to Applied Regression continues to provide the most comprehensive and user-friendly guide to estimating, interpreting, and presenting results from regression models in R." -Christopher Hare, University of California, Davis

The complete step-by-step guide to mastering the basics of Aspen Plus software Used for a wide variety of important scientific tasks, Aspen Plus software is a modeling tool used for conceptual design, optimization, and performance monitoring of chemical processes. After more than twenty years, it remains one of the most popular and powerful chemical engineering programs used both industrially and academically. Teach Yourself the Basics of Aspen Plus, Second Edition continues to deliver important fundamentals on using Aspen Plus software. The new edition focuses on the newest version of Aspen Plus and covers the newest functionalities. Lecture-style chapters set the tone for maximizing the learning experience by presenting material in a manner that emulates an actual workshop classroom environment. Important points are emphasized through encouragement of hands-on learning techniques that direct learners toward achievement in creating effective designs fluidly and with confidence. Teach Yourself the Basics of Aspen Plus, Second Edition includes: * Examples embedded within the text to focus the reader on specific aspects of the material being covered * Workshops at the end of each chapter that provide opportunities to test the reader's knowledge in that chapter's subject matter * Functionalities covered in the newest version of Aspen including the solution of a flowsheet by an equation oriented, EO approach, and the solution of problems which involve electrolyte equilibria * Aspen Plus executable format as well as .txt format files containing details of the examples and the workshops as well as their solutions are provided as a download Designed with both students and professionals in mind, Teach Yourself the Basics of Aspen Plus, Second Edition is like having a personal professor 24/7. Its revolutionary format is an exciting way to learn how to operate this highly sophisticated software and a surefire way for readers to get the results they expect. Ralph Schefflan has been an adjunct professor at Stevens Institute of Technology for the past thirty-five years. He has taught four graduate courses, thermodynamics, process simulation, numerical methods, and equilibrium stage operations during his time there as well as being SIT's representative to Aspen Technology. Dr. Schefflan introduced process simulation at SIT evolving from Flowtran to Aspen Plus and taught it for thirty years.

Discover how SAP S/4HANA transforms your supply chain! Explore functionalities for sourcing and procurement, production execution, plant maintenance, sales order management, transportation management, warehouse management, and more. See how intelligent technologies elevate your logistics operations with SAP Business Technology Platform and learn about complementary cloud solutions like SAP Ariba and SAP IBP. This is your starting point for logistics with SAP S/4HANA!In this book, you'll learn about: a. Key Functionality See what SAP S/4HANA 2021 has to offer! Walk through your logistics business processes, from production planning to inventory valuation and beyond. Learn about new features such as predictive MRP, centralized procurement, and production engineering and operations. b. Logistics Innovations Your supply chain is getting smarter. Discover intelligent technologies enabled by SAP BTP: blockchain, intelligent robotic process automation, machine learning, and more. c. Planning Your Migration Prepare for your logistics transformation. Plan your roadmap to SAP S/4HANA, evaluate your implementation approaches, and get insight into the new RISE with SAP offering. Highlights include: 1) Planning and scheduling 2) Sourcing and procurement 3) Manufacturing operations 4) Quality management 5) Plant maintenance 6) Sales order management 7) Transportation management 8) Inventory management 9) Warehouse management 10) Intelligent technologies 11) Reporting and analytics 12) Industry use cases

Easily Use SAS to Produce Your Graphics Diagrams, plots, and other types of graphics are indispensable components in nearly all phases of statistical analysis, from the initial assessment of the data to the selection of appropriate statistical models to the diagnosis of the chosen models once they have been fitted to the data. Harnessing the full graphics capabilities of SAS, A Handbook of Statistical Graphics Using SAS ODS covers essential graphical methods needed in every statistician's toolkit. It explains how to implement the methods using SAS 9.4. The handbook shows how to use SAS to create many types of statistical graphics for exploring data and diagnosing fitted models. It uses SAS's newer ODS graphics throughout as this system offers a number of advantages, including ease of use, high quality of results, consistent appearance, and convenient semiautomatic graphs from the statistical procedures. Each chapter deals graphically with several sets of example data from a wide variety of areas, such as epidemiology, medicine, and psychology. These examples illustrate the use of graphic displays to give an overview of data, to suggest possible hypotheses for testing new data, and to interpret fitted statistical models. The SAS programs and data sets are available online.

Deep Learning with R introduces deep learning and neural networks using the R programming language. The book builds on the understanding of the theoretical and mathematical constructs and enables the reader to create applications on computer vision, natural language processing and transfer learning. The book starts with an introduction to machine learning and moves on to describe the basic architecture, different activation functions, forward propagation, cross-entropy loss and backward propagation of a simple neural network. It goes on to create different code segments to construct deep neural networks. It discusses in detail the initialization of network parameters, optimization techniques, and some of the common issues surrounding neural networks such as dealing with NaNs and the vanishing/exploding gradient problem. Advanced variants of multilayered perceptrons namely, convolutional neural networks and sequence models are explained, followed by application to different use cases. The book makes extensive use of the Keras and TensorFlow frameworks.

Uncertainties in Numerical Weather Prediction is a comprehensive work on the most current understandings of uncertainties and predictability in numerical simulations of the atmosphere. It provides general knowledge on all aspects of uncertainties in the weather prediction models in a single, easy to use reference. The book illustrates particular uncertainties in observations and data assimilation, as well as the errors associated with numerical integration methods. Stochastic methods in parameterization of subgrid processes are also assessed, as are uncertainties associated with surface-atmosphere exchange, orographic flows and processes in the atmospheric boundary layer. Through a better understanding of the uncertainties to watch for, readers will be able to produce more precise and accurate forecasts. This is an essential work for anyone who wants to improve the accuracy of weather and climate forecasting and interested parties developing tools to enhance the quality of such forecasts.

Cyber-solutions to real-world business problems Artificial Intelligence in Practice is a fascinating look into how companies use AI and machine learning to solve problems. Presenting 50 case studies of actual situations, this book demonstrates practical applications to issues faced by businesses around the globe. The rapidly evolving field of artificial intelligence has expanded beyond research labs and computer science departments and made its way into the mainstream business environment. Artificial intelligence and machine learning are cited as the most important modern business trends to drive success. It is used in areas ranging from banking and finance to social media and marketing. This technology continues to provide innovative solutions to businesses of all sizes, sectors and industries. This engaging and topical book explores a wide range of cases illustrating how businesses use AI to boost performance, drive efficiency, analyse market preferences and many others. Best-selling author and renowned AI expert Bernard Marr reveals how machine learning technology is transforming the way companies conduct business. This detailed examination provides an overview of each company, describes the specific problem and explains how AI facilitates resolution. Each case study provides a comprehensive overview, including some technical details as well as key learning summaries: Understand how specific business problems are addressed by innovative machine learning methods Explore how current artificial intelligence applications improve performance and increase efficiency in various situations Expand your knowledge of recent AI advancements in technology Gain insight on the future of AI and its increasing role in business and industry Artificial Intelligence in Practice: How 50 Successful Companies Used Artificial Intelligence to Solve Problems is an insightful and informative exploration of the transformative power of technology in 21st century commerce.

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.

Designed to help readers analyze and interpret research data using IBM SPSS, this user-friendly book shows readers how to choose the appropriate statistic based on the design; perform intermediate statistics, including multivariate statistics; interpret output; and write about the results. The book reviews research designs and how to assess the accuracy and reliability of data; how to determine whether data meet the assumptions of statistical tests; how to calculate and interpret effect sizes for intermediate statistics, including odds ratios for logistic and discriminant analyses; how to compute and interpret post-hoc power; and an overview of basic statistics for those who need a review. Unique chapters on multilevel linear modeling; multivariate analysis of variance (MANOVA); assessing reliability of data; multiple imputation; mediation, moderation, and canonical correlation; and factor analysis are provided. SPSS syntax with output is included for those who prefer this format.

The new edition features:

IBM SPSS version 22; although the book can be used with most older and newer versions

New discusiion of intraclass correlations (Ch. 3)

Expanded discussion of effect sizes that includes confidence intervals of effect sizes (ch.5)

New information on part and partial correlations and how they are interpreted and a new discussion on backward elimination, another useful multiple regression method (Ch. 6)

New chapter on how use a variable as a mediator or a moderator (ch. 7)

Revised chapter on multilevel and hierarchical linear modeling (ch. 12)

A new chapter (ch. 13) on multiple imputation that demonstrates how to deal with missing data

Updated web resources for instructors including PowerPoint slides, answers to interpretation questions, extra SPSS problems and for students, data sets, and chapter outlines and study guides. "

IBM SPSS for Intermediate Statistics, Fifth Edition "provides helpful teaching tools:

all of the key SPSS windows needed to perform the analyses

outputs with call-out boxes to highlight key points

interpretation sections and questions to help students better understand and interpret the output

extra problems with realistic data sets for practice using intermediate statistics

Appendices on how to get started with SPSS, write research questions, and basic statistics.

An ideal supplement for courses in either intermediate/advanced statistics or research methods taught in departments of psychology, education, and other social, behavioral, and health sciences. This book is also appreciated by researchers in these areas looking for a handy reference for SPSS"

Become a stats superstar by using Excel to reveal the powerful secrets of statistics.

Microsoft Excel offers numerous possibilities for statistical analysis―and you don’t have to be a math wizard to unlock them. In Statistical Analysis with Excel For Dummies, fully updated for the 2021 version of Excel, you’ll hit the ground running with straightforward techniques and practical guidance to unlock the power of statistics in Excel.

Bypass unnecessary jargon and skip right to mastering formulas, functions, charts, probabilities, distributions, and correlations. Written for professionals and students without a background in statistics or math, you’ll learn to create, interpret, and translate statistics―and have fun doing it!

In this book you’ll find out how to:

• Understand, describe, and summarize any kind of data, from sports stats to sales figures
• Confidently draw conclusions from your analyses, make accurate predictions, and calculate correlations
• Model the probabilities of future outcomes based on past data
• Perform statistical analysis on any platform: Windows, Mac, or iPad
• Access additional resources and practice templates through Dummies.com

For anyone who’s ever wanted to unleash the full potential of statistical analysis in Excel―and impress your colleagues or classmates along the way―Statistical Analysis with Excel For Dummies walks you through the foundational concepts of analyzing statistics and the step-by-step methods you use to apply them.

The quickest way to do the books for small business owners and managers No one looks forward to doing the finances: that's why QuickBooks 2020 All-in-One For Dummies is on hand to help get it over with as quickly and painlessly as possible. This comprehensive one-stop reference combines 8 mini-books in one, all written in plain and simple language that makes it easy for even the most accounts-averse to get the most out of the latest version of the QuickBooks software and save time. Written and revised by financial expert Stephen Nelson, the latest version of this invaluable guide takes readers step-by-step through every aspect of small business accounting procedures, including understanding the fundamentals of double-entry bookkeeping, setting up and administering the QuickBooks system, and carrying out complex tasks such as ratio analysis and capital budgeting. Write a business plan and create a forecast Learn how to use profit-volume-cost analysis tools Prepare financial statements and reports Protect your financial data Time is money--get this time-saving resource today and start reaping the rewards!

This is the first workbook that introduces the multilevel approach to modeling with categorical outcomes using IBM SPSS Version 20. Readers learn how to develop, estimate, and interpret multilevel models with categorical outcomes. The authors walk readers through data management, diagnostic tools, model conceptualization, and model specification issues related to single-level and multilevel models with categorical outcomes. Screen shots clearly demonstrate techniques and navigation of the program. Modeling syntax is provided in the appendix. Examples of various types of categorical outcomes demonstrate how to set up each model and interpret the output. Extended examples illustrate the logic of model development, interpretation of output, the context of the research questions, and the steps around which the analyses are structured. Readers can replicate examples in each chapter by using the corresponding data and syntax files available at www.psypress.com/9781848729568. The book opens with a review of multilevel with categorical outcomes, followed by a chapter on IBM SPSS data management techniques to facilitate working with multilevel and longitudinal data sets. Chapters 3 and 4 detail the basics of the single-level and multilevel generalized linear model for various types of categorical outcomes. These chapters review underlying concepts to assist with trouble-shooting common programming and modeling problems. Next population-average and unit-specific longitudinal models for investigating individual or organizational developmental processes are developed. Chapter 6 focuses on single- and multilevel models using multinomial and ordinal data followed by a chapter on models for count data. The book concludes with additional trouble shooting techniques and tips for expanding on the modeling techniques introduced. Ideal as a supplement for graduate level courses and/or professional workshops on multilevel, longitudinal, latent variable modeling, multivariate statistics, and/or advanced quantitative techniques taught in psychology, business, education, health, and sociology, this practical workbook also appeals to researchers in these fields. An excellent follow up to the authors' highly successful Multilevel and Longitudinal Modeling with IBM SPSS and Introduction to Multilevel Modeling Techniques, 2nd Edition, this book can also be used with any multilevel and/or longitudinal book or as a stand-alone text introducing multilevel modeling with categorical outcomes.

Technology and Health: Promoting Attitude and Behavior Change examines how technology can be used to promote healthier attitudes and behavior. The book discusses technology as a tool to deliver media content. This book synthesizes theory-driven research with implications for research and practice. It covers a range of theories and technology in diverse health contexts. The book covers why and how specific technologies, such as virtual reality, augmented reality, mobile games, and social media, are effective in promoting good health. The book additionally suggests how technology should be designed, utilized, and evaluated for health interventions.