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Statistics Essentials For Dummies
by Deborah J. RumseyStatistics Essentials For Dummies (9781119590309) was previously published as Statistics Essentials For Dummies (9780470618394). While this version features a new Dummies cover and design, the content is the same as the prior release and should not be considered a new or updated product. Statistics Essentials For Dummies not only provides students enrolled in Statistics I with an excellent high-level overview of key concepts, but it also serves as a reference or refresher for students in upper-level statistics courses. Free of review and ramp-up material, Statistics Essentials For Dummies sticks to the point, with content focused on key course topics only. It provides discrete explanations of essential concepts taught in a typical first semester college-level statistics course, from odds and error margins to confidence intervals and conclusions. This guide is also a perfect reference for parents who need to review critical statistics concepts as they help high school students with homework assignments, as well as for adult learners headed back into the classroom who just need a refresher of the core concepts. The Essentials For Dummies SeriesDummies is proud to present our new series, The Essentials For Dummies. Now students who are prepping for exams, preparing to study new material, or who just need a refresher can have a concise, easy-to-understand review guide that covers an entire course by concentrating solely on the most important concepts. From algebra and chemistry to grammar and Spanish, our expert authors focus on the skills students most need to succeed in a subject.
Statistics Explained
by Perry R. HintonStatistics Explained is an accessible introduction to statistical concepts and ideas. It makes few assumptions about the reader’s statistical knowledge, carefully explaining each step of the analysis and the logic behind it. The book: provides a clear explanation of statistical analysis and the key statistical tests employed in analysing research data gives accessible explanations of how and why statistical tests are used includes a wide range of practical, easy-to-understand worked examples. Building on the international success of earlier editions, this fully updated revision includes developments in statistical analysis, with new sections explaining concepts such as bootstrapping and structural equation modelling. A new chapter - ‘Samples and Statistical Inference’ - explains how data can be analysed in detail to examine its suitability for certain statistical tests. The friendly and straightforward style of the text makes it accessible to all those new to statistics, as well as more experienced students requiring a concise guide. It is suitable for students and new researchers in disciplines including Psychology, Education, Sociology, Sports Science, Nursing, Communication, and Media and Business Studies. Presented in full colour and with an updated, reader-friendly layout, this new edition also comes with a companion website featuring supplementary resources for students. Unobtrusive cross-referencing makes it the ideal companion to Perry R. Hinton’s SPSS Explained, also published by Routledge. Perry R. Hinton has many years of experience in teaching statistics to students from a wide range of disciplines and his understanding of the problems students face forms the basis of this book.
Statistics Explained
by Perry R. HintonStatistics Explained, now in its fully revised Fourth Edition, is for students and researchers who wish to understand the statistical analyses used to analyse quantitative (numerical) research data in a wide range of academic disciplines, in particular, the behavioural, human and social sciences.This book explains, in a clear and informative manner, the logic of statistical analysis, in particular the null hypothesis significance testing (NHST) method. Using this method, a range of statistical tests have been devised for different types of data. Each of these tests is explained in the book by adopting a step-by-step (“walkthrough”) approach with a specific illustrative example. Crucially, these explanations do not require the reader to have an advanced knowledge of mathematics or statistics, but only assumes the basic mathematics which most readers will have learnt at high school. The book also examines the criticisms of NHST and introduces the reader to Bayesian statistics. As a result the reader will be able to critically evaluate the outcomes of statistical analysis both of their own work and that of others.Statistics Explains will be of great interest to students and researchers in the behavioural, human and social sciences in understanding both the outcomes of their own research and also the research reports in the academic journals.
Statistics Explained
by Steve MckillupAn understanding of statistics and experimental design is essential for life science studies, but many students lack a mathematical background and some even dread taking an introductory statistics course. Using a refreshingly clear and encouraging reader-friendly approach, this book helps students understand how to choose, carry out, interpret and report the results of complex statistical analyses, critically evaluate the design of experiments and proceed to more advanced material. Taking a straightforward conceptual approach, it is specifically designed to foster understanding, demystify difficult concepts and encourage the unsure. Even complex topics are explained clearly, using a pictorial approach with a minimum of formulae and terminology. Examples of tests included throughout are kept simple by using small data sets. In addition, end-of-chapter exercises, new to this edition, allow self-testing. Handy diagnostic tables help students choose the right test for their work and remain a useful refresher tool for postgraduates.
Statistics for Anthropology
by Lorena MadrigalAnthropology as a discipline is rapidly becoming more quantitative, and anthropology students are now required to develop sophisticated statistical skills. This book provides students of anthropology with a clear, step-by-step guide to univariate statistical methods, demystifying the aspects that are often seen as difficult or impenetrable. Explaining the central role of statistical methods in anthropology and using only anthropological examples, the book provides a solid footing in statistical techniques. Beginning with basic descriptive statistics, this new edition also covers more advanced methods such as analyses of frequencies and variance, simple and multiple regression analysis with dummy and continuous variables. It addresses commonly encountered problems such as small samples and non-normality. Each statistical technique is accompanied by clearly worked examples and the chapters end with practice problem sets. Many of the datasets are available for download at www.cambridge.org/9780521147088.
Statistics for Archaeologists
by Robert D. DrennanIn the decade since its publication, the first edition of Statistics for Archaeologists has become a staple in the classroom. Taking a jargon-free approach, this teaching tool introduces the basic principles of statistics to archaeologists. The author covers the necessary techniques for analyzing data collected in the field and laboratory as well as for evaluating the significance of the relationships between variables. In addition, chapters discuss the special concerns of working with samples. This well-illustrated guide features several practice problems making it an ideal text for students in archaeology and anthropology. Using feedback from students and teachers who have been using the first edition, as well as another ten years of personal experience with the text, the author has provided an updated and revised second edition with a number of important changes. New topics covered include: -Proportions and Densities -Error Ranges for Medians -Resampling Approaches -Residuals from Regression -Point Sampling -Multivariate Analysis -Similarity Measures -Multidimensional Scaling -Principal Components Analysis -Cluster Analysis Those already familiar with the clear and useful format of Statistics for Archaeologists will find this new edition a welcome update, and the new sections will make this seminal textbook an indispensible resource for a whole new group of students, professors, and practitioners.
Statistics for Biotechnology Process Development
by Todd Coffey Harry YangWritten specifically for biotechnology scientists, engineers, and quality professionals, this book describes and demonstrates the proper application of statistical methods throughout Chemistry, Manufacturing, and Controls (CMC). Filled with case studies, examples, and easy-to-follow explanations of how to perform statistics in modern software, it is the first book on CMC statistics written primarily for practitioners. While statisticians will also benefit from this book, it is written particularly for industry professionals who don’t have access to a CMC statistician or who want to be more independent in the design and analysis of their experiments. Provides an introduction to the statistical concepts important in the biotechnology industry Focuses on concepts with theoretical details kept to a minimum Includes lots of real examples and case studies to illustrate the methods Uses JMP software for implementation of the methods Offers a text suitable for scientists in the industry with some quantitative training Written and edited by seasoned veterans of the biotechnology industry, this book will prove useful to a wide variety of biotechnology professionals. The book brings together individual chapters that showcase the use of statistics in the most salient areas of CMC.
Statistics for Business
by Perumal MariappanStatistics for Business is meant as a textbook for students in business, computer science, bioengineering, environmental technology, and mathematics. In recent years, business statistics is used widely for decision making in business endeavours. It emphasizes statistical applications, statistical model building, and determining the manual solution methods. Special Features: This text is prepared based on "self-taught" method. For most of the methods, the required algorithm is clearly explained using flow-charting methodology. More than 200 solved problems provided. More than 175 end-of-chapter exercises with answers are provided. This allows teachers ample flexibility in adopting the textbook to their individual class plans. This textbook is meant to for beginners and advanced learners as a text in Statistics for Business or Applied Statistics for undergraduate and graduate students.
Statistics For Business: Decision Making And Analysis
by Robert A. Stine Dean P. FosterUnderstand Business. Understand Data. The 3rd Edition of Statistics for Business: Decision Making and Analysis emphasizes an application-based approach, in which readers learn how to work with data to make decisions. In this contemporary presentation of business statistics, readers learn how to approach business decisions through a 4M Analytics decision making strategy—motivation, method, mechanics and message—to better understand how a business context motivates the statistical process and how the results inform a course of action. Each chapter includes hints on using Excel, Minitab Express, and JMP for calculations, pointing the reader in the right direction to get started with analysis of data.
Statistics for Business
by Derek L. WallerStatistics for Business explains the fundamentals of statistical analysis in a lucid, pragmatic way. A thorough knowledge of statistics is essential for decision making in all corners of business and management. By collecting, organizing and analyzing statistical data you can express what you know, benchmark your current situation, and estimate future outcomes. Based entirely on Microsoft Excel, this book covers a spectrum of statistic fundamentals from basic principles, to probability, sampling, hypothesis testing, forecasting, statistical process control and six-sigma management. This second edition is packed with features to aid understanding and help ensure that every aspect of your knowledge of statistics is applicable to practice, including: Icebreakers introducing each chapter that relate statistics to the real world, drawn from management and hospitality situations Detailed worked examples in each chapter Over 140 case-exercises complete with objective, situation, requirements, and answers A complete glossary of key terminology and formulas, mathematical relationships, and Excel relationships and functions A brand new companion website containing slides, worked-out-solutions to the case-exercises, and a test bank With a clear and accessible style this book makes statistics easier to understand. It is ideal for business, management, tourism and hospitality students who want to learn how to apply statistics to the real world.
Statistics for Business and Economics
by David R. Anderson Dennis J. Sweeney Thomas A. Williams Jeffrey D. Camm James J. CochranGet more out of your statistics course than simply solving equations. Discover how statistical information enables strong decisions in today's business world with STATISTICS FOR BUSINESS AND ECONOMICS, REVISED 13E. Sound methodology combines with a proven problem-scenario approach, and meaningful applications for the most powerful approach to mastering critical business statistics. This edition's prestigious author team brings together more than 25 years of unmatched experience to this thoroughly updated text. More than 350 real business examples, timely cases, and memorable exercises present the latest statistical data and business information with unwavering accuracy. To ensure the most relevant coverage, this edition includes coverage of popular commercial statistical software Minitab 17 and Excel 2016. Optional chapter appendices, coordinating data sets, and online learning tools, such as CengageNOW, create a customizable, efficient solution.
Statistics for Business and Economics
by David R. Anderson Dennis J. Sweeney Thomas A. Williams Jeffrey D. Camm James J. Cochran Michael J. Fry Jeffrey W. OhlmannDiscover how statistical information impacts decisions in today’s business world as Anderson/Sweeney/Williams/Camm/Cochran/Fry/Ohlmann's leading STATISTICS FOR BUSINESS AND ECONOMICS, 14E connects concepts in each chapter to real-world practice. This edition delivers sound statistical methodology, a proven problem-scenario approach and meaningful applications that reflect the latest developments in business and statistics today. More than 350 new and proven real business examples, a wealth of practical cases and meaningful hands-on exercises highlight statistics in action. You gain practice using leading professional statistical software with exercises and appendices that walk you through using JMP Student Edition 14 and Excel 2016. WebAssign's online course management systems further strengthens this business statistics approach and helps you maximize your course success.
Statistics For Business And Economics
by Terry Sincich P. George Benson James McClaveThirteenth Edition, Statistics for Business and Economics introduces statistics in the context of contemporary business. Emphasizing statistical literacy in thinking, the text applies its concepts with real data and uses technology to develop a deeper conceptual understanding. Examples, activities, and case studies foster active learning while emphasizing intuitive concepts of probability and teaching readers to make informed business decisions. The Thirteenth Edition continues to highlight the importance of ethical behavior in collecting, interpreting, and reporting on data, while also providing a wealth of new and updated exercises and case studies.
Statistics for Business and Economics (Special Edition)
by David R. Anderson Dennis J. Sweeney Thomas A. Williams Jeffrey D. CammStatistics for Business and Economics by Jeffrey D. Camm, Thomas A. Williams, Dennis J. Sweeney, and David R. Anderson.
Statistics for Business and Financial Economics
by Alice C. Lee John C. Lee Cheng-Few LeeStatistics for Business and Financial Economics, 3rd edition is the definitive Business Statistics book to use Finance, Economics, and Accounting data throughout the entire book. Therefore, this book gives students an understanding of how to apply the methodology of statistics to real world situations. In particular, this book shows how descriptive statistics, probability, statistical distributions, statistical inference, regression methods, and statistical decision theory can be used to analyze individual stock price, stock index, stock rate of return, market rate of return, and decision making. In addition, this book also shows how time-series analysis and the statistical decision theory method can be used to analyze accounting and financial data. In this fully-revised edition, the real world examples have been reconfigured and sections have been edited for better understanding of the topics.
Statistics for Censored Environmental Data Using Minitab and R
by Dennis R. HelselPraise for the First Edition" . . . an excellent addition to an upper-level undergraduate course on environmental statistics, and . . . a 'must-have' desk reference for environmental practitioners dealing with censored datasets." -Vadose Zone JournalStatistical Methods for Censored Environmental Data Using Minitab® and R, Second Edition introduces and explains methods for analyzing and interpreting censored data in the environmental sciences. Adapting survival analysis techniques from other fields, the book translates well-established methods from other disciplines into new solutions for environmental studies. This new edition applies methods of survival analysis, including methods for interval-censored data to the interpretation of low-level contaminants in environmental sciences and occupational health. Now incorporating the freely available R software as well as Minitab® into the discussed analyses, the book features newly developed and updated material including: A new chapter on multivariate methods for censored data Use of interval-censored methods for treating true nondetects as lower than and separate from values between the detection and quantitation limits ("remarked data") A section on summing data with nondetects A newly written introduction that discusses invasive data, showing why substitution methods fail Expanded coverage of graphical methods for censored data The author writes in a style that focuses on applications rather than derivations, with chapters organized by key objectives such as computing intervals, comparing groups, and correlation. Examples accompany each procedure, utilizing real-world data that can be analyzed using the Minitab® and R software macros available on the book's related website, and extensive references direct readers to authoritative literature from the environmental sciences.Statistics for Censored Environmental Data Using Minitab® and R, Second Edition is an excellent book for courses on environmental statistics at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for?environmental professionals, biologists, and ecologists who focus on the water sciences, air quality, and soil science.
Statistics for Chemical and Process Engineers: A Modern Approach
by Yuri A.W. ShardtA coherent, concise, and comprehensive course in the statistics needed for a modern career in chemical engineering covers all of the concepts required for the American Fundamentals of Engineering Examination.Statistics for Chemical and Process Engineers (second edition) shows the reader how to develop and test models, design experiments and analyze data in ways easily applicable through readily available software tools like MS Excel® and MATLAB® and is updated for the most recent versions of both. Generalized methods that can be applied irrespective of the tool at hand are a key feature of the text, and it now contains an introduction to the use of state-space methods.The reader is given a detailed framework for statistical procedures covering: data visualization;probability;linear and nonlinear regression; experimental design (including factorial and fractional factorial designs); and dynamic process identification. Main concepts are illustrated with chemical- and process-engineering-relevant examples that can also serve as the bases for checking any subsequent real implementations. Questions are provided (with solutions available for instructors) to confirm the correct use of numerical techniques, and templates for use in MS Excel and MATLAB are also available for download. With its integrative approach to system identification, regression, and statistical theory, this book provides an excellent means of revision and self-study for chemical and process engineers working in experimental analysis and design in petrochemicals, ceramics, oil and gas, automotive and similar industries, and invaluable instruction to advanced undergraduate and graduate students looking to begin a career in the process industries.
Statistics for Clinicians: How Much Should a Doctor Know?
by Ahmed HassounaHow much statistics does a clinician, surgeon or nurse need to know?This book provides an essential handbook to help appraise evidence in a scientific paper, to design and interpret the results of research correctly, to guide our students and to review the work of our colleagues. This title is written by a clinician exclusively for fellow clinicians, in their own language and not in statistical or epidemiological terms.When clinicians discuss probability, it is focussed on how it applies to the management of patients in the flesh and how they are managed in a clinical setting. Statistics for Clinicians does not overlook the basis of statistics, but reviews techniques specific to medicine with an emphasis on their application. It ensures that readers have the correct tools to hand, including worked examples, guides and links to online calculators and free software, enabling readers to execute most statistical calculations. This book will therefore be enormously helpful for many working across all fields of medicine at any stage of their career.
Statistics for Clinicians
by Andrew OwenThis book provides clinical medicine readers with a detailed explanation of statistical concepts using non-technical terms. This allows clinicians and others without specialist statistical knowledge to understand the medical literature where such concepts are used. Many examples from the medical literature are used to exemplify how these concepts are used in practice. Current books written for clinicians fall into two broad categories. Simple texts that are not designed to cover many important statistical concepts used in the medical literature. Comprehensive texts which cover many statistical principles in detail, including statistical theory, but which are more challenging to read and do not always cover many important statistical techniques used in the medical literature. This book assists in the understanding of these techniques.Statistics for Clinicians covers such topics in a robust non-technical manner accessible to clinicians and is intended for hospital consultants, junior doctors and general practitioners. Undergraduates in biomedical sciences and medicine may also find some sections valuable.
Statistics for Compensation
by John H. DavisAn insightful, hands-on focus on the statistical methods used by compensation and human resources professionals in their everyday work Across various industries, compensation professionals work to organize and analyze aspects of employment that deal with elements of pay, such as deciding base salary, bonus, and commission provided by an employer to its employees for work performed. Acknowledging the numerous quantitative analyses of data that are a part of this everyday work, Statistics for Compensation provides a comprehensive guide to the key statistical tools and techniques needed to perform those analyses and to help organizations make fully informed compensation decisions. This self-contained book is the first of its kind to explore the use of various quantitative methods--from basic notions about percents to multiple linear regression--that are used in the management, design, and implementation of powerful compensation strategies. Drawing upon his extensive experience as a consultant, practitioner, and teacher of both statistics and compensation, the author focuses on the usefulness of the techniques and their immediate application to everyday compensation work, thoroughly explaining major areas such as: Frequency distributions and histograms Measures of location and variability Model building Linear models Exponential curve models Maturity curve models Power models Market models and salary survey analysis Linear and exponential integrated market models Job pricing market models Throughout the book, rigorous definitions and step-by-step procedures clearly explain and demonstrate how to apply the presented statistical techniques. Each chapter concludes with a set of exercises, and various case studies showcase the topic's real-world relevance. The book also features an extensive glossary of key statistical terms and an appendix with technical details. Data for the examples and practice problems are available in the book and on a related FTP site. Statistics for Compensation is an excellent reference for compensation professionals, human resources professionals, and other practitioners responsible for any aspect of base pay, incentive pay, sales compensation, and executive compensation in their organizations. It can also serve as a supplement for compensation courses at the upper-undergraduate and graduate levels.
Statistics for Data Science
by James D. MillerGet your statistics basics right before diving into the world of data science About This Book • No need to take a degree in statistics, read this book and get a strong statistics base for data science and real-world programs; • Implement statistics in data science tasks such as data cleaning, mining, and analysis • Learn all about probability, statistics, numerical computations, and more with the help of R programs Who This Book Is For This book is intended for those developers who are willing to enter the field of data science and are looking for concise information of statistics with the help of insightful programs and simple explanation. Some basic hands on R will be useful. What You Will Learn • Analyze the transition from a data developer to a data scientist mindset • Get acquainted with the R programs and the logic used for statistical computations • Understand mathematical concepts such as variance, standard deviation, probability, matrix calculations, and more • Learn to implement statistics in data science tasks such as data cleaning, mining, and analysis • Learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks • Get comfortable with performing various statistical computations for data science programmatically In Detail Data science is an ever-evolving field, which is growing in popularity at an exponential rate. Data science includes techniques and theories extracted from the fields of statistics; computer science, and, most importantly, machine learning, databases, data visualization, and so on. This book takes you through an entire journey of statistics, from knowing very little to becoming comfortable in using various statistical methods for data science tasks. It starts off with simple statistics and then move on to statistical methods that are used in data science algorithms. The R programs for statistical computation are clearly explained along with logic. You will come across various mathematical concepts, such as variance, standard deviation, probability, matrix calculations, and more. You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis. You will learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks. By the end of the book, you will be comfortable with performing various statistical computations for data science programmatically. Style and approach Step by step comprehensive guide with real world examples
Statistics for Data Science and Analytics
by Peter C. Bruce Peter Gedeck Janet DobbinsIntroductory statistics textbook with a focus on data science topics such as prediction, correlation, and data exploration Statistics for Data Science and Analytics is a comprehensive guide to statistical analysis using Python, presenting important topics useful for data science such as prediction, correlation, and data exploration. The authors provide an introduction to statistical science and big data, as well as an overview of Python data structures and operations. A range of statistical techniques are presented with their implementation in Python, including hypothesis testing, probability, exploratory data analysis, categorical variables, surveys and sampling, A/B testing, and correlation. The text introduces binary classification, a foundational element of machine learning, validation of statistical models by applying them to holdout data, and probability and inference via the easy-to-understand method of resampling and the bootstrap instead of using a myriad of “kitchen sink” formulas. Regression is taught both as a tool for explanation and for prediction. This book is informed by the authors’ experience designing and teaching both introductory statistics and machine learning at Statistics.com. Each chapter includes practical examples, explanations of the underlying concepts, and Python code snippets to help readers apply the techniques themselves. Statistics for Data Science and Analytics includes information on sample topics such as: Int, float, and string data types, numerical operations, manipulating strings, converting data types, and advanced data structures like lists, dictionaries, and setsExperiment design via randomizing, blinding, and before-after pairing, as well as proportions and percents when handling binary dataSpecialized Python packages like numpy, scipy, pandas, scikit-learn and statsmodels—the workhorses of data science—and how to get the most value from themStatistical versus practical significance, random number generators, functions for code reuse, and binomial and normal probability distributions Written by and for data science instructors, Statistics for Data Science and Analytics is an excellent learning resource for data science instructors prescribing a required intro stats course for their programs, as well as other students and professionals seeking to transition to the data science field.
Statistics for Data Science and Policy Analysis
by Azizur RahmanThis book brings together the best contributions of the Applied Statistics and Policy Analysis Conference 2019. Written by leading international experts in the field of statistics, data science and policy evaluation. This book explores the theme of effective policy methods through the use of big data, accurate estimates and modern computing tools and statistical modelling.
Statistics For Dummies
by Deborah J. RumseyThe fun and easy way to get down to business with statisticsStymied by statistics? No fear ? this friendly guide offers clear, practical explanations of statistical ideas, techniques, formulas, and calculations, with lots of examples that show you how these concepts apply to your everyday life.Statistics For Dummies shows you how to interpret and critique graphs and charts, determine the odds with probability, guesstimate with confidence using confidence intervals, set up and carry out a hypothesis test, compute statistical formulas, and more.Tracks to a typical first semester statistics courseUpdated examples resonate with today's studentsExplanations mirror teaching methods and classroom protocolPacked with practical advice and real-world problems, Statistics For Dummies gives you everything you need to analyze and interpret data for improved classroom or on-the-job performance.
Statistics for Earth and Environmental Scientists
by Larry Drew John SchuenemeyerA comprehensive treatment of statistical applications for solving real-world environmental problemsA host of complex problems face today's earth science community, such as evaluating the supply of remaining non-renewable energy resources, assessing the impact of people on the environment, understanding climate change, and managing the use of water. Proper collection and analysis of data using statistical techniques contributes significantly toward the solution of these problems. Statistics for Earth and Environmental Scientists presents important statistical concepts through data analytic tools and shows readers how to apply them to real-world problems.The authors present several different statistical approaches to the environmental sciences, including Bayesian and nonparametric methodologies. The book begins with an introduction to types of data, evaluation of data, modeling and estimation, random variation, and sampling--all of which are explored through case studies that use real data from earth science applications. Subsequent chapters focus on principles of modeling and the key methods and techniques for analyzing scientific data, including:Interval estimation and Methods for analyzinghypothesis testing of means time series dataSpatial statisticsMultivariate analysisDiscrete distributionsExperimental designMost statistical models are introduced by concept and application, given as equations, and then accompanied by heuristic justification rather than a formal proof. Data analysis, model building, and statistical inference are stressed throughout, and readers are encouraged to collect their own data to incorporate into the exercises at the end of each chapter. Most data sets, graphs, and analyses are computed using R, but can be worked with using any statistical computing software. A related website features additional data sets, answers to selected exercises, and R code for the book's examples.Statistics for Earth and Environmental Scientists is an excellent book for courses on quantitative methods in geology, geography, natural resources, and environmental sciences at the upper-undergraduate and graduate levels. It is also a valuable reference for earth scientists, geologists, hydrologists, and environmental statisticians who collect and analyze data in their everyday work.