Business Analytics Principles, Concepts, and Applications with SAS

What, Why, and How

Author: Marc J. Schniederjans,Dara G. Schniederjans,Christopher M. Starkey

Publisher: Pearson Education

ISBN: 0133989577

Category: Computers

Page: 352

View: 4926

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Learn everything you need to know to start using business analytics and integrating it throughout your organization. Business Analytics Principles, Concepts, and Applications with SAS brings together a complete, integrated package of knowledge for newcomers to the subject. The authors present an up-to-date view of what business analytics is, why it is so valuable, and most importantly, how it is used. They combine essential conceptual content with clear explanations of the tools, techniques, and methodologies actually used to implement modern business analytics initiatives. They offer a proven step-wise approach to designing an analytics program, and successfully integrating it into your organization, so it effectively provides intelligence for competitive advantage in decision making. Using step-by-step examples, the authors identify common challenges that can be addressed by business analytics, illustrate each type of analytics (descriptive, prescriptive, and predictive), and guide users in undertaking their own projects. Illustrating the real-world use of statistical, information systems, and management science methodologies, these examples help readers successfully apply the methods they are learning. Unlike most competitive guides, this text demonstrates the use of SAS software, permitting instructors to spend less time teaching software and more time focusing on business analytics itself. Business Analytics Principles, Concepts, and Applications with SAS will be a valuable resource for all beginning-to-intermediate level business analysts and business analytics managers; for MBA/Masters' degree students in the field; and for advanced undergraduates majoring in statistics, applied mathematics, or engineering/operations research.

Business Analytics Principles, Concepts, and Applications

What, Why, and How

Author: Marc J. Schniederjans,Dara G. Schniederjans,Christopher M. Starkey

Publisher: Pearson Education

ISBN: 0133552187

Category: Business & Economics

Page: 350

View: 7955

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Learn everything you need to know to start using business analytics and integrating it throughout your organization. Business Analytics Principles, Concepts, and Applications brings together a complete, integrated package of knowledge for newcomers to the subject. The authors present an up-to-date view of what business analytics is, why it is so valuable, and most importantly, how it is used. They combine essential conceptual content with clear explanations of the tools, techniques, and methodologies actually used to implement modern business analytics initiatives. They offer a proven step-wise approach to designing an analytics program, and successfully integrating it into your organization, so it effectively provides intelligence for competitive advantage in decision making. Using step-by-step examples, the authors identify common challenges that can be addressed by business analytics, illustrate each type of analytics (descriptive, prescriptive, and predictive), and guide users in undertaking their own projects. Illustrating the real-world use of statistical, information systems, and management science methodologies, these examples help readers successfully apply the methods they are learning. Unlike most competitive guides, this text demonstrates the use of IBM's menu-based SPSS software, permitting instructors to spend less time teaching software and more time focusing on business analytics itself. A valuable resource for all beginning-to-intermediate-level business analysts and business analytics managers; for MBA/Masters' degree students in the field; and for advanced undergraduates majoring in statistics, applied mathematics, or engineering/operations research.

Business Statistics Made Easy in SAS

Author: Gregory Lee

Publisher: SAS Institute

ISBN: 162960044X

Category: Computers

Page: 384

View: 6201

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Learn or refresh core statistical methods for business with SAS® and approach real business analytics issues and techniques using a practical approach that avoids complex mathematics and instead employs easy-to-follow explanations. Business Statistics Made Easy in SAS® is designed as a user-friendly, practice-oriented, introductory text to teach businesspeople, students, and others core statistical concepts and applications. It begins with absolute core principles and takes you through an overview of statistics, data and data collection, an introduction to SAS®, and basic statistics (descriptive statistics and basic associational statistics). The book also provides an overview of statistical modeling, effect size, statistical significance and power testing, basics of linear regression, introduction to comparison of means, basics of chi-square tests for categories, extrapolating statistics to business outcomes, and some topical issues in statistics, such as big data, simulation, machine learning, and data warehousing. The book steers away from complex mathematical-based explanations, and it also avoids basing explanations on the traditional build-up of distributions, probability theory and the like, which tend to lose the practice-oriented reader. Instead, it teaches the core ideas of statistics through methods such as careful, intuitive written explanations, easy-to-follow diagrams, step-by-step technique implementation, and interesting metaphors. With no previous SAS experience necessary, Business Statistics Made Easy in SAS® is an ideal introduction for beginners. It is suitable for introductory undergraduate classes, postgraduate courses such as MBA refresher classes, and for the business practitioner. It is compatible with SAS® University Edition.

Managerial Analytics

An Applied Guide to Principles, Methods, Tools, and Best Practices

Author: Michael Watson,Derek Nelson

Publisher: Pearson Education

ISBN: 013340742X

Category: Business & Economics

Page: 236

View: 4992

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The field of analytics is rapidly evolving, making it difficult for professionals and students to keep up the most current and effective applications. Managerial Analytics will help readers sort through all these new options and identify the appropriate solution. In this reference, authors Watson, Nelson and Cacioppi accurately define and identify the components of analytics and big data, giving readers the knowledge needed to effectively assess new aspects and applications. Building on this foundation, they review tools and solutions, identify the offerings best aligned to one's requirements, and show how to tailor analytics applications to an organization's specific needs. Drawing on extensive experience implementing, planning, and researching advanced analytics for business, the authors clearly explain all this, and more: What analytics is and isn't: great examples of successful usage – and other examples where the term is being degraded into meaninglessness The difference between using analytics and “competing on analytics” How to get started with big data, by analyzing the most relevant data Components of analytics systems, from databases and Excel to BI systems and beyond Anticipating and overcoming “confirmation bias” and other pitfalls Understanding predictive analytics and getting the high-quality random samples necessary Applying game theory, Efficient Frontier, benchmarking, and revenue management models Implementing optimization at the small and large scale, and using it to make “automatic decisions”

Modern Analytics Methodologies

Driving Business Value with Analytics

Author: Michele Chambers,Thomas W Dinsmore

Publisher: Pearson Education

ISBN: 0133498832

Category: Computers

Page: 272

View: 5849

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Create a complete roadmap for capitalizing on analytics to grow topline revenue and build shareholder value in your unique organization! Modern Analytics Methodologies goes far beyond the classic Analytics Maturity Model to help you overcome the gaps between your current analytics capabilities and where you need to go. Pioneering analytics experts Michele Chambers and Thomas Dinsmore help you implement analytics that supports your strategy, aligns with your culture, and serves your customers and stakeholders. Drawing on work with dozens of leading enterprises, Michele Chambers and Thomas Dinsmore describe high-value applications from many industries, and help you systematically identify and deliver on your company's best opportunities. Writing for both professionals and students, they show how to: Leverage the convergence of macro trends ranging from "flattening" and "green" to Big Data and machine learning Go beyond the Analytics Maturity Model: power your unique business strategy with an equally focused analytics strategy Link key business objectives with core characteristics of your organization, value chain, and stakeholders Take advantage of game changing opportunities before competitors do Effectively integrate the managerial and operational aspects of analytics Measure performance with dashboards, scorecards, visualization, simulation, and more Prioritize and score prospective analytics projects Identify "Quick Wins" you can implement while you're planning for the long-term Build an effective Analytic Program Office to make your roadmap persistent Update and revise your roadmap for new needs and technologies Modern Analytics Methodologies will be an indispensable resource for any executive or professional concerned with analytics, including Chief Analytics Officers; Chief Data Officers; Chief Scientists; Chief Marketing Officers; Chief Risk Officers; Chief Strategy Officers; VPs of Analytics or Big Data; data scientists; business strategists; and line-of-business executives.

Data Mining with Rattle and R

The Art of Excavating Data for Knowledge Discovery

Author: Graham Williams

Publisher: Springer Science & Business Media

ISBN: 144199890X

Category: Mathematics

Page: 374

View: 7575

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Data mining is the art and science of intelligent data analysis. By building knowledge from information, data mining adds considerable value to the ever increasing stores of electronic data that abound today. In performing data mining many decisions need to be made regarding the choice of methodology, the choice of data, the choice of tools, and the choice of algorithms. Throughout this book the reader is introduced to the basic concepts and some of the more popular algorithms of data mining. With a focus on the hands-on end-to-end process for data mining, Williams guides the reader through various capabilities of the easy to use, free, and open source Rattle Data Mining Software built on the sophisticated R Statistical Software. The focus on doing data mining rather than just reading about data mining is refreshing. The book covers data understanding, data preparation, data refinement, model building, model evaluation, and practical deployment. The reader will learn to rapidly deliver a data mining project using software easily installed for free from the Internet. Coupling Rattle with R delivers a very sophisticated data mining environment with all the power, and more, of the many commercial offerings.

SAS Hash Object Programming Made Easy

Author: Michele M. Burlew

Publisher: SAS Institute

ISBN: 1612900984

Category: Mathematics

Page: 208

View: 859

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Hash objects, an efficient look-up tool in the SAS DATA step, are object-oriented programming structures that function differently from traditional SAS language statements. Michele Burlew's SAS Hash Object Programming Made Easy shows readers how to use these powerful features, which they can program to quickly look up and manage data and to conserve computing resources. SAS provides various look-up techniques, and hash objects are among the newest, so therefore many users may not have yet used them. Because the examples presented vary in complexity, SAS Hash Object Programming Made Easy is useful to SAS users of all experience levels, from novice programmer to advanced programmer. Novice programmers can adapt some of the simpler hash programming techniques as they develop their SAS programming skills. This book helps more experienced programmers learn how to take advantage of hash object programming by comparing traditional processing techniques to those that use hash objects. Additionally, users from diverse fields with different requirements can adapt the examples in SAS Hash Object Programming Made Easy to fit their unique situations. This book is part of the SAS Press program.

Reinventing the Supply Chain Life Cycle

Strategies and Methods for Analysis and Decision Making

Author: Marc J. Schniederjans,Stephen B. LeGrand

Publisher: FT Press

ISBN: 0132963906

Category: Business & Economics

Page: 496

View: 402

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Optimize supply chains throughout their entire lifecycle: creation, growth, maturity, and decline! Reflecting up-to-the-minute "in-the-trenches" experience and pioneering research, this book illuminates the complex transformational processes associated with managing complex supply chains that incorporate multiple products and services within ever-changing networks. Marc J. Schniederjans and Stephen B. Legrand walk you through: starting, creating, and building new supply chains; then, realigning those supply chains for growth, adjusting to dynamic change, readjusting networks, building flexibility, and managing new supply chain risks. Next, they offer practical, realistic guidance for realigning "mature" supply chains, innovating, controlling costs; and smoothly managing declining demand. Throughout, they offer invaluable insights and tools for negotiating, measuring performance, anticipating change, improving agility and flexibility, meeting commitments to social responsibility and the law; and much more. Based on the authors' up-to-the minute supply chain experience and pioneering academic research, Reinventing the Supply Chain Life Cycle contains many real-world examples and interviews with executives from some of the world’s top organizations. It integrates content related to key certifications and offers valuable material that can be incorporated directly into existing supply chain practices, procedures, and policies.

An Introduction to Statistical Methods and Data Analysis

Author: R. Lyman Ott,Micheal T. Longnecker

Publisher: Cengage Learning

ISBN: 1305465520

Category: Mathematics

Page: 1296

View: 4909

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Ott and Longnecker's AN INTRODUCTION TO STATISTICAL METHODS AND DATA ANALYSIS, Seventh Edition, provides a broad overview of statistical methods for advanced undergraduate and graduate students from a variety of disciplines who have little or no prior course work in statistics. The authors teach students to solve problems encountered in research projects, to make decisions based on data in general settings both within and beyond the university setting, and to become critical readers of statistical analyses in research papers and news reports. The first eleven chapters present material typically covered in an introductory statistics course, as well as case studies and examples that are often encountered in undergraduate capstone courses. The remaining chapters cover regression modeling and design of experiments. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

Enterprise Analytics

Optimize Performance, Process, and Decisions Through Big Data

Author: Thomas H. Davenport

Publisher: Pearson Education

ISBN: 0133039439

Category: Business & Economics

Page: 268

View: 9373

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"International Institute for Analytics"--Dust jacket.

Marketing Data Science

Modeling Techniques in Predictive Analytics with R and Python

Author: Thomas W. Miller

Publisher: FT Press

ISBN: 0133887340

Category: Business & Economics

Page: 225

View: 3752

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Now , a leader of Northwestern University's prestigious analytics program presents a fully-integrated treatment of both the business and academic elements of marketing applications in predictive analytics. Writing for both managers and students, Thomas W. Miller explains essential concepts, principles, and theory in the context of real-world applications. Building on Miller's pioneering program, Marketing Data Science thoroughly addresses segmentation, target marketing, brand and product positioning, new product development, choice modeling, recommender systems, pricing research, retail site selection, demand estimation, sales forecasting, customer retention, and lifetime value analysis. Starting where Miller's widely-praised Modeling Techniques in Predictive Analytics left off, he integrates crucial information and insights that were previously segregated in texts on web analytics, network science, information technology, and programming. Coverage includes: The role of analytics in delivering effective messages on the web Understanding the web by understanding its hidden structures Being recognized on the web – and watching your own competitors Visualizing networks and understanding communities within them Measuring sentiment and making recommendations Leveraging key data science methods: databases/data preparation, classical/Bayesian statistics, regression/classification, machine learning, and text analytics Six complete case studies address exceptionally relevant issues such as: separating legitimate email from spam; identifying legally-relevant information for lawsuit discovery; gleaning insights from anonymous web surfing data, and more. This text's extensive set of web and network problems draw on rich public-domain data sources; many are accompanied by solutions in Python and/or R. Marketing Data Science will be an invaluable resource for all students, faculty, and professional marketers who want to use business analytics to improve marketing performance.

People Analytics in the Era of Big Data

Changing the Way You Attract, Acquire, Develop, and Retain Talent

Author: Jean Paul Isson,Jesse S. Harriott

Publisher: John Wiley & Sons

ISBN: 111923316X

Category: Business & Economics

Page: 416

View: 6972

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Apply predictive analytics throughout all stages of workforce management People Analytics in the Era of Big Data provides a blueprint for leveraging your talent pool through the use of data analytics. Written by the Global Vice President of Business Intelligence and Predictive Analytics at Monster Worldwide, this book is packed full of actionable insights to help you source, recruit, acquire, engage, retain, promote, and manage the exceptional talent your organization needs. With a unique approach that applies analytics to every stage of the hiring process and the entire workforce planning and management cycle, this informative guide provides the key perspective that brings analytics into HR in a truly useful way. You're already inundated with disparate employee data, so why not mine that data for insights that add value to your organization and strengthen your workforce? This book presents a practical framework for real-world talent analytics, backed by groundbreaking examples of workforce analytics in action across the U.S., Canada, Europe, Asia, and Australia. Leverage predictive analytics throughout the hiring process Utilize analytics techniques for more effective workforce management Learn how people analytics benefits organizations of all sizes in various industries Integrate analytics into HR practices seamlessly and thoroughly Corporate executives need fact-based insights into what will happen with their talent. Who should you hire? Who should you promote? Who are the top or bottom performers, and why? Who is at risk to quit, and why? Analytics can provide these answers, and give you insights based on quantifiable data instead of gut feeling and subjective assessment. People Analytics in the Era of Big Data is the essential guide to optimizing your workforce with the tools already at your disposal.

Learn Business Analytics in Six Steps Using SAS and R

A Practical, Step-by-Step Guide to Learning Business Analytics

Author: Subhashini Sharma Tripathi

Publisher: Apress

ISBN: 1484210018

Category: Computers

Page: 219

View: 9911

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Apply analytics to business problems using two very popular software tools, SAS and R. No matter your industry, this book will provide you with the knowledge and insights you and your business partners need to make better decisions faster. Learn Business Analytics in Six Steps Using SAS and R teaches you how to solve problems and execute projects through the "DCOVA and I" (Define, Collect, Organize, Visualize, Analyze, and Insights) process. You no longer need to choose between the two most popular software tools. This book puts the best of both worlds—SAS and R—at your fingertips to solve a myriad of problems, whether relating to data science, finance, web usage, product development, or any other business discipline. What You'll Learn Use the DCOVA and I process: Define, Collect, Organize, Visualize, Analyze and Insights. Harness both SAS and R, the star analytics technologies in the industry Use various tools to solve significant business challenges Understand how the tools relate to business analytics See seven case studies for hands-on practice Who This Book Is For This book is for all IT professionals, especially data analysts, as well as anyone who Likes to solve business problems and is good with logical thinking and numbers Wants to enter the analytics world and is looking for a structured book to reach that goal Is currently working on SAS , R, or any other analytics software and strives to use its full power

Big Data, Mining, and Analytics

Components of Strategic Decision Making

Author: Stephan Kudyba

Publisher: CRC Press

ISBN: 1466568712

Category: Computers

Page: 325

View: 8763

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There is an ongoing data explosion transpiring that will make previous creations, collections, and storage of data look trivial. Big Data, Mining, and Analytics: Components of Strategic Decision Making ties together big data, data mining, and analytics to explain how readers can leverage them to extract valuable insights from their data. Facilitating a clear understanding of big data, it supplies authoritative insights from expert contributors into leveraging data resources, including big data, to improve decision making. Illustrating basic approaches of business intelligence to the more complex methods of data and text mining, the book guides readers through the process of extracting valuable knowledge from the varieties of data currently being generated in the brick and mortar and internet environments. It considers the broad spectrum of analytics approaches for decision making, including dashboards, OLAP cubes, data mining, and text mining. Includes a foreword by Thomas H. Davenport, Distinguished Professor, Babson College; Fellow, MIT Center for Digital Business; and Co-Founder, International Institute for Analytics Introduces text mining and the transforming of unstructured data into useful information Examines real time wireless medical data acquisition for today’s healthcare and data mining challenges Presents the contributions of big data experts from academia and industry, including SAS Highlights the most exciting emerging technologies for big data—Hadoop is just the beginning Filled with examples that illustrate the value of analytics throughout, the book outlines a conceptual framework for data modeling that can help you immediately improve your own analytics and decision-making processes. It also provides in-depth coverage of analyzing unstructured data with text mining methods to supply you with the well-rounded understanding required to leverage your information assets into improved strategic decision making.

Data Analytics and Decision Support for Cybersecurity

Trends, Methodologies and Applications

Author: Iván Palomares Carrascosa,Harsha Kumara Kalutarage,Yan Huang

Publisher: Springer

ISBN: 3319594397

Category: Computers

Page: 270

View: 3621

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The book illustrates the inter-relationship between several data management, analytics and decision support techniques and methods commonly adopted in Cybersecurity-oriented frameworks. The recent advent of Big Data paradigms and the use of data science methods, has resulted in a higher demand for effective data-driven models that support decision-making at a strategic level. This motivates the need for defining novel data analytics and decision support approaches in a myriad of real-life scenarios and problems, with Cybersecurity-related domains being no exception. This contributed volume comprises nine chapters, written by leading international researchers, covering a compilation of recent advances in Cybersecurity-related applications of data analytics and decision support approaches. In addition to theoretical studies and overviews of existing relevant literature, this book comprises a selection of application-oriented research contributions. The investigations undertaken across these chapters focus on diverse and critical Cybersecurity problems, such as Intrusion Detection, Insider Threats, Insider Threats, Collusion Detection, Run-Time Malware Detection, Intrusion Detection, E-Learning, Online Examinations, Cybersecurity noisy data removal, Secure Smart Power Systems, Security Visualization and Monitoring. Researchers and professionals alike will find the chapters an essential read for further research on the topic.

Structural Steel Design

A Practice Oriented Approach

Author: Abi O. Aghayere,Jason Vigil

Publisher: Pearson Education

ISBN: 9780135064160

Category: Technology & Engineering

Page: 692

View: 5102

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This book is a comprehensive, stand alone reference for structural steel design. Giving the audience a thorough introduction to steel structures, this book contains all of the need to know information on practical design considerations in the design of steel buildings. It includes complete coverage of design methods, load combinations, gravity loads, lateral loads and systems in steel buildings, and much more.

Data Mining and Business Analytics with R

Author: Johannes Ledolter

Publisher: John Wiley & Sons

ISBN: 1118572157

Category: Computers

Page: 368

View: 7432

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Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification. Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents: • A thorough discussion and extensive demonstration of the theory behind the most useful data mining tools • Illustrations of how to use the outlined concepts in real-world situations • Readily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materials • Numerous exercises to help readers with computing skills and deepen their understanding of the material Data Mining and Business Analytics with R is an excellent graduate-level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences.

The Data Science Handbook

Author: Field Cady

Publisher: John Wiley & Sons

ISBN: 1119092949

Category: Mathematics

Page: 416

View: 2939

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A comprehensive overview of data science covering the analytics, programming, and business skills necessary to master the discipline Finding a good data scientist has been likened to hunting for a unicorn: the required combination of technical skills is simply very hard to find in one person. In addition, good data science is not just rote application of trainable skill sets; it requires the ability to think flexibly about all these areas and understand the connections between them. This book provides a crash course in data science, combining all the necessary skills into a unified discipline. Unlike many analytics books, computer science and software engineering are given extensive coverage since they play such a central role in the daily work of a data scientist. The author also describes classic machine learning algorithms, from their mathematical foundations to real-world applications. Visualization tools are reviewed, and their central importance in data science is highlighted. Classical statistics is addressed to help readers think critically about the interpretation of data and its common pitfalls. The clear communication of technical results, which is perhaps the most undertrained of data science skills, is given its own chapter, and all topics are explained in the context of solving real-world data problems. The book also features: • Extensive sample code and tutorials using Python™ along with its technical libraries • Core technologies of “Big Data,” including their strengths and limitations and how they can be used to solve real-world problems • Coverage of the practical realities of the tools, keeping theory to a minimum; however, when theory is presented, it is done in an intuitive way to encourage critical thinking and creativity • A wide variety of case studies from industry • Practical advice on the realities of being a data scientist today, including the overall workflow, where time is spent, the types of datasets worked on, and the skill sets needed The Data Science Handbook is an ideal resource for data analysis methodology and big data software tools. The book is appropriate for people who want to practice data science, but lack the required skill sets. This includes software professionals who need to better understand analytics and statisticians who need to understand software. Modern data science is a unified discipline, and it is presented as such. This book is also an appropriate reference for researchers and entry-level graduate students who need to learn real-world analytics and expand their skill set. FIELD CADY is the data scientist at the Allen Institute for Artificial Intelligence, where he develops tools that use machine learning to mine scientific literature. He has also worked at Google and several Big Data startups. He has a BS in physics and math from Stanford University, and an MS in computer science from Carnegie Mellon.

Statistical Techniques for Forensic Accounting

Understanding the Theory and Application of Data Analysis

Author: Saurav K. Dutta

Publisher: Pearson Education

ISBN: 0133133818

Category: Business & Economics

Page: 262

View: 8546

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Master powerful statistical techniques for uncovering fraud or misrepresentation in complex financial data. The discipline of statistics has developed sophisticated, well-accepted approaches for identifying financial fraud and demonstrating that it is deliberate. Statistical Techniques for Forensic Accounting is the first comprehensive guide to these tools and techniques. Leading expert Dr. Saurav Dutta explains their mathematical underpinnings, shows how to use them properly, and guides you in communicating your findings to other interested and knowledgeable parties, or assessing others' analyses. Dutta is singularly well-qualified to write this book: he has been engaged as an expert in many of the world's highest-profile financial fraud cases, including Worldcom, Global Crossing, Cendant, and HealthSouth. Here, he covers everything professionals need to know to construct and conduct valid and defensible statistical tests, perform analyses, and interpret others' analyses. Coverage includes: exploratory data analysis to identify the "Fraud Triangle" and other red flags... data mining tools, usage, and limitations... statistical terms and methods applicable to forensic accounting... relevant uncertainty and probability concepts... Bayesian analysis and networks... statistical inference, sampling, sample size, estimation, regression, correlation, classification, prediction, and much more. For all forensic accountants, auditors, investigators, and litigators involved with corporate financial reporting; and for all students interested in forensic accounting and related fields.

Probability and Statistics for Computer Scientists, Second Edition

Author: Michael Baron

Publisher: CRC Press

ISBN: 1498760600

Category: Mathematics

Page: 449

View: 9701

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Student-Friendly Coverage of Probability, Statistical Methods, Simulation, and Modeling Tools Incorporating feedback from instructors and researchers who used the previous edition, Probability and Statistics for Computer Scientists, Second Edition helps students understand general methods of stochastic modeling, simulation, and data analysis; make optimal decisions under uncertainty; model and evaluate computer systems and networks; and prepare for advanced probability-based courses. Written in a lively style with simple language, this classroom-tested book can now be used in both one- and two-semester courses. New to the Second Edition Axiomatic introduction of probability Expanded coverage of statistical inference, including standard errors of estimates and their estimation, inference about variances, chi-square tests for independence and goodness of fit, nonparametric statistics, and bootstrap More exercises at the end of each chapter Additional MATLAB® codes, particularly new commands of the Statistics Toolbox In-Depth yet Accessible Treatment of Computer Science-Related Topics Starting with the fundamentals of probability, the text takes students through topics heavily featured in modern computer science, computer engineering, software engineering, and associated fields, such as computer simulations, Monte Carlo methods, stochastic processes, Markov chains, queuing theory, statistical inference, and regression. It also meets the requirements of the Accreditation Board for Engineering and Technology (ABET). Encourages Practical Implementation of Skills Using simple MATLAB commands (easily translatable to other computer languages), the book provides short programs for implementing the methods of probability and statistics as well as for visualizing randomness, the behavior of random variables and stochastic processes, convergence results, and Monte Carlo simulations. Preliminary knowledge of MATLAB is not required. Along with numerous computer science applications and worked examples, the text presents interesting facts and paradoxical statements. Each chapter concludes with a short summary and many exercises.