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Statistics for Engineers: An Introduction with Examples from Practice
by Hartmut Schiefer Felix SchieferThis book describes how statistical methods can be effectively applied in the work of an engineer in terms that can be readily understood. Application of these methods enables the effort involved in experiments to be reduced, the results of these experiments to be fully evaluated, and statistically sound statements to be made as a result. Products can be developed more efficiently and manufactured more cost-effectively, not to mention with greater process reliability. The overarching aim is to save time, money, and materials. From the examples provided, the nature of the practical application can be clearly grasped in each case.This book is a translation of the original German 1st edition Statistik für Ingenieure by Hartmut Schiefer and Felix Schiefer, published by Springer Fachmedien Wiesbaden GmbH, part of Springer Nature in 2018. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). The present version has been revised technically and linguistically by the authors in collaboration with a professional translator. Springer Nature works continuously to further the development of tools for the production of books and on the related technologies to support the authors.
Statistics for Environmental Biology and Toxicology (Interdisciplinary Statistics Ser. #4)
by A.John BailerStatistics for Environmental Biology and Toxicology presents and illustrates statistical methods appropriate for the analysis of environmental data obtained in biological or toxicological experiments. Beginning with basic probability and statistical inferences, this text progresses through non-linear and generalized linear models, trend testing, time-to-event data and analysis of cross-classified tabular and categorical data. For the more complex analyses, extensive examples including SAS and S-PLUS programming code are provided to assist the reader when implementing the methods in practice.
Statistics for Epidemiology (Chapman & Hall/CRC Texts in Statistical Science)
by Nicholas P. JewellStatistical ideas have been integral to the development of epidemiology and continue to provide the tools needed to interpret epidemiological studies. Although epidemiologists do not need a highly mathematical background in statistical theory to conduct and interpret such studies, they do need more than an encyclopedia of "recipes."Statistics for E
Statistics For Evidence-Based Practice And Evaluation
by Allen RubinBoth practical and easy to read, Rubin's STATISTICS FOR EVIDENCE-BASED PRACTICE AND EVALUATION provides you with a step-by-step guide that will help you succeed in your course. Practice illustrations and exercises support your ability to study and retain core concepts, while practical examples provide you with the opportunity to see how and when data analysis and statistics are used by helping professionals in the real world.
Statistics for Exercise Science and Health with Microsoft Office Excel
by J. P. VermaThis book introduces the use of statistics to solve a variety of problems in exercise science and health and provides readers with a solid foundation for future research and data analysis. Statistics for Exercise Science and Health with Microsoft Office Excel: Aids readers in analyzing their own data using the presented statistical techniques combined with Excel Features comprehensive coverage of hypothesis testing and regression models to facilitate modeling in sports science Utilizes Excel to enhance reader competency in data analysis and experimental designs Includes coverage of both binomial and poison distributions with applications in exercise science and health Provides solved examples and plentiful practice exercises throughout in addition to case studies to illustrate the discussed analytical techniques Contains all needed definitions and formulas to aid readers in understanding different statistical concepts and developing the needed skills to solve research problems
Statistics for Experimenters: Design, Innovation, and Discovery (Second Edition)
by George E.P. Box J. Stuart Hunter William G. HunterThe book intends to make available to experimenters scientific and statistical tools that can greatly catalyze innovation, problem solving, and discovery and illustrate how these tools may be used by and with subject matter specialists as their investigations proceed.
Statistics for Finance (Chapman & Hall/CRC Texts in Statistical Science)
by Erik Lindström Henrik Madsen Jan Nygaard NielsenStatistics for Finance develops students’ professional skills in statistics with applications in finance. Developed from the authors’ courses at the Technical University of Denmark and Lund University, the text bridges the gap between classical, rigorous treatments of financial mathematics that rarely connect concepts to data and books on econometrics and time series analysis that do not cover specific problems related to option valuation. The book discusses applications of financial derivatives pertaining to risk assessment and elimination. The authors cover various statistical and mathematical techniques, including linear and nonlinear time series analysis, stochastic calculus models, stochastic differential equations, Itō’s formula, the Black–Scholes model, the generalized method-of-moments, and the Kalman filter. They explain how these tools are used to price financial derivatives, identify interest rate models, value bonds, estimate parameters, and much more. This textbook will help students understand and manage empirical research in financial engineering. It includes examples of how the statistical tools can be used to improve value-at-risk calculations and other issues. In addition, end-of-chapter exercises develop students’ financial reasoning skills.
Statistics for Health Data Science: An Organic Approach (Springer Texts in Statistics)
by Ruth Etzioni Micha Mandel Roman GulatiStudents and researchers in the health sciences are faced with greater opportunity and challenge than ever before. The opportunity stems from the explosion in publicly available data that simultaneously informs and inspires new avenues of investigation. The challenge is that the analytic tools required go far beyond the standard methods and models of basic statistics. This textbook aims to equip health care researchers with the most important elements of a modern health analytics toolkit, drawing from the fields of statistics, health econometrics, and data science. This textbook is designed to overcome students’ anxiety about data and statistics and to help them to become confident users of appropriate analytic methods for health care research studies. Methods are presented organically, with new material building naturally on what has come before. Each technique is motivated by a topical research question, explained in non-technical terms, and accompanied by engaging explanations and examples. In this way, the authors cultivate a deep (“organic”) understanding of a range of analytic techniques, their assumptions and data requirements, and their advantages and limitations. They illustrate all lessons via analyses of real data from a variety of publicly available databases, addressing relevant research questions and comparing findings to those of published studies. Ultimately, this textbook is designed to cultivate health services researchers that are thoughtful and well informed about health data science, rather than data analysts. This textbook differs from the competition in its unique blend of methods and its determination to ensure that readers gain an understanding of how, when, and why to apply them. It provides the public health researcher with a way to think analytically about scientific questions, and it offers well-founded guidance for pairing data with methods for valid analysis. Readers should feel emboldened to tackle analysis of real public datasets using traditional statistical models, health econometrics methods, and even predictive algorithms. Accompanying code and data sets are provided in an author site: https://roman-gulati.github.io/statistics-for-health-data-science/
Statistics for Imaging, Optics, and Photonics
by Peter BajorskiA vivid, hands-on discussion of the statistical methods in imaging, optics, and photonics applications In the field of imaging science, there is a growing need for students and practitioners to be equipped with the necessary knowledge and tools to carry out quantitative analysis of data. Providing a self-contained approach that is not too heavily statistical in nature, Statistics for Imaging, Optics, and Photonics presents necessary analytical techniques in the context of real examples from various areas within the field, including remote sensing, color science, printing, and astronomy. Bridging the gap between imaging, optics, photonics, and statistical data analysis, the author uniquely concentrates on statistical inference, providing a wide range of relevant methods. Brief introductions to key probabilistic terms are provided at the beginning of the book in order to present the notation used, followed by discussions on multivariate techniques such as: Linear regression models, vector and matrix algebra, and random vectors and matrices Multivariate statistical inference, including inferences about both mean vectors and covariance matrices Principal components analysis Canonical correlation analysis Discrimination and classification analysis for two or more populations and spatial smoothing Cluster analysis, including similarity and dissimilarity measures and hierarchical and nonhierarchical clustering methodsIntuitive and geometric understanding of concepts is emphasized, and all examples are relatively simple and include background explanations. Computational results and graphs are presented using the freely available R software, and can be replicated by using a variety of software packages. Throughout the book, problem sets and solutions contain partial numerical results, allowing readers to confirm the accuracy of their approach; and a related website features additional resources including the book's datasets and figures.Statistics for Imaging, Optics, and Photonics is an excellent book for courses on multivariate statistics for imaging science, optics, and photonics at the upper-undergraduate and graduate levels. The book also serves as a valuable reference for professionals working in imaging, optics, and photonics who carry out data analyses in their everyday work.
Statistics for K-8 Educators
by Robert RosenfeldThis book offers an introduction to descriptive and inferential statistics tailored to the teaching and research needs of K-8 educators. Using statistics to tell a story, veteran teacher educator Robert Rosenfeld pushes readers away from simply performing a calculation to truly understanding the statistical concepts themselves. In addition to helping educators develop this statistical habit of mind, Rosenfeld also focuses on developing an understanding of the statistics in published research and on interpreting school data, which can be applied in school assessment and educational research. Features of this must-read resource include: Numerous exercises and activities throughout that are related specifically to the world of educators and are designed to foster conversation and small group discussion. Connections drawn between statistics and the regular mathematics curriculum to aid teachers who do classroom-based action research. A section covering the basic concepts of standardized tests, such as summative versus formative assessment, and standards-based versus norm-referenced tests. Accessibly written and conversational in tone, Statistics for K-8 Eductors provides the technical foundation to help teachers make good sense of quantitative information connected to their classrooms and to their schools.
Statistics for Kids: Model Eliciting Activities to Investigate Concepts in Statistics (Grades 4-6)
by Scott ChamberlinPerhaps the most useful and neglected content area of mathematics is statistics, especially for students in Grades 4-6. Couple that fact with the notion that mathematical modeling is an increasing emphasis in many standards, such as the Common Core State Standards for Mathematics and the NCTM standards, and the necessity for this topic is overdue. In this book, teachers will facilitate learning using model-eliciting activities (MEAs), problem-solving tasks created by mathematics educators to encourage students to investigate concepts in mathematics through the creation of mathematical models. Students will explore statistical concepts including trends, spread of data, standard deviation, variability, correlation, sampling, and more—all of which are designed around topics of interest to students. Grades 4-6
Statistics for Lawyers
by Michael O. Finkelstein Bruce LevinThis classic text, first published in 1990, is designed to introduce law students, law teachers, practitioners, and judges to the basic ideas of mathematical probability and statistics as they have been applied in the law. The third edition includes over twenty new sections, including the addition of timely topics, like New York City police stops, exonerations in death-sentence cases, projecting airline costs, and new material on various statistical techniques such as the randomized response survey technique, rare-events meta-analysis, competing risks, and negative binomial regression. The book consists of sections of exposition followed by real-world cases and case studies in which statistical data have played a role. The reader is asked to apply the theory to the facts, to calculate results (a hand calculator is sufficient), and to explore legal issues raised by quantitative findings. The authors' calculations and comments are given in the back of the book. As with previous editions, the cases and case studies reflect a broad variety of legal subjects, including antidiscrimination, mass torts, taxation, school finance, identification evidence, preventive detention, handwriting disputes, voting, environmental protection, antitrust, sampling for insurance audits, and the death penalty. A chapter on epidemiology was added in the second edition. In 1991, the first edition was selected by the University of Michigan Law Review as one of the important law books of the year.
Statistics for Linguists: An Introduction Using R
by Bodo WinterStatistics for Linguists: An Introduction Using R is the first statistics textbook on linear models for linguistics. The book covers simple uses of linear models through generalized models to more advanced approaches, maintaining its focus on conceptual issues and avoiding excessive mathematical details. It contains many applied examples using the R statistical programming environment. Written in an accessible tone and style, this text is the ideal main resource for graduate and advanced undergraduate students of Linguistics statistics courses as well as those in other fields, including Psychology, Cognitive Science, and Data Science.
Statistics for Long-Memory Processes (Chapman And Hall/crc Monographs On Statistics And Applied Probability Ser. #61)
by Jan BeranStatistical Methods for Long Term Memory Processes covers the diverse statistical methods and applications for data with long-range dependence. Presenting material that previously appeared only in journals, the author provides a concise and effective overview of probabilistic foundations, statistical methods, and applications. The material emphasizes basic principles and practical applications and provides an integrated perspective of both theory and practice. This book explores data sets from a wide range of disciplines, such as hydrology, climatology, telecommunications engineering, and high-precision physical measurement. The data sets are conveniently compiled in the index, and this allows readers to view statistical approaches in a practical context. Statistical Methods for Long Term Memory Processes also supplies S-PLUS programs for the major methods discussed. This feature allows the practitioner to apply long memory processes in daily data analysis. For newcomers to the area, the first three chapters provide the basic knowledge necessary for understanding the remainder of the material. To promote selective reading, the author presents the chapters independently. Combining essential methodologies with real-life applications, this outstanding volume is and indispensable reference for statisticians and scientists who analyze data with long-range dependence.
Statistics for Machine Learning
by Pratap DangetiBuild Machine Learning models with a sound statistical understanding. About This Book • Learn about the statistics behind powerful predictive models with p-value, ANOVA, and F- statistics. • Implement statistical computations programmatically for supervised and unsupervised learning through K-means clustering. • Master the statistical aspect of Machine Learning with the help of this example-rich guide to R and Python. Who This Book Is For This book is intended for developers with little to no background in statistics, who want to implement Machine Learning in their systems. Some programming knowledge in R or Python will be useful. What You Will Learn • Understand the Statistical and Machine Learning fundamentals necessary to build models • Understand the major differences and parallels between the statistical way and the Machine Learning way to solve problems • Learn how to prepare data and feed models by using the appropriate Machine Learning algorithms from the more-than-adequate R and Python packages • Analyze the results and tune the model appropriately to your own predictive goals • Understand the concepts of required statistics for Machine Learning • Introduce yourself to necessary fundamentals required for building supervised & unsupervised deep learning models • Learn reinforcement learning and its application in the field of artificial intelligence domain In Detail Complex statistics in Machine Learning worry a lot of developers. Knowing statistics helps you build strong Machine Learning models that are optimized for a given problem statement. This book will teach you all it takes to perform complex statistical computations required for Machine Learning. You will gain information on statistics behind supervised learning, unsupervised learning, reinforcement learning, and more. Understand the real-world examples that discuss the statistical side of Machine Learning and familiarize yourself with it. You will also design programs for performing tasks such as model, parameter fitting, regression, classification, density collection, and more. By the end of the book, you will have mastered the required statistics for Machine Learning and will be able to apply your new skills to any sort of industry problem. Style and approach This practical, step-by-step guide will give you an understanding of the Statistical and Machine Learning fundamentals you'll need to build models.
Statistics for Making Decisions
by Nicholas T. LongfordMaking decisions is a ubiquitous mental activity in our private and professional or public lives. It entails choosing one course of action from an available shortlist of options. Statistics for Making Decisions places decision making at the centre of statistical inference, proposing its theory as a new paradigm for statistical practice. The analysis in this paradigm is earnest about prior information and the consequences of the various kinds of errors that may be committed. Its conclusion is a course of action tailored to the perspective of the specific client or sponsor of the analysis. The author’s intention is a wholesale replacement of hypothesis testing, indicting it with the argument that it has no means of incorporating the consequences of errors which self-evidently matter to the client. The volume appeals to the analyst who deals with the simplest statistical problems of comparing two samples (which one has a greater mean or variance), or deciding whether a parameter is positive or negative. It combines highlighting the deficiencies of hypothesis testing with promoting a principled solution based on the idea of a currency for error, of which we want to spend as little as possible. This is implemented by selecting the option for which the expected loss is smallest (the Bayes rule). The price to pay is the need for a more detailed description of the options, and eliciting and quantifying the consequences (ramifications) of the errors. This is what our clients do informally and often inexpertly after receiving outputs of the analysis in an established format, such as the verdict of a hypothesis test or an estimate and its standard error. As a scientific discipline and profession, statistics has a potential to do this much better and deliver to the client a more complete and more relevant product. Nicholas T. Longford is a senior statistician at Imperial College, London, specialising in statistical methods for neonatal medicine. His interests include causal analysis of observational studies, decision theory, and the contest of modelling and design in data analysis. His longer-term appointments in the past include Educational Testing Service, Princeton, NJ, USA, de Montfort University, Leicester, England, and directorship of SNTL, a statistics research and consulting company. He is the author of over 100 journal articles and six other monographs on a variety of topics in applied statistics.
Statistics for Managers Using Microsoft Excel
by David Levine David Stephan Kathryn Szabat<p>For undergraduate business statistics courses. Analyzing the Data Applicable to Business. This text is the gold standard for learning how to use Microsoft Excel® in business statistics, helping students gain the understanding they need to be successful in their careers. The authors present statistics in the context of specific business fields; full chapters on business analytics further prepare students for success in their professions. Current data throughout the text lets students practice analyzing the types of data they will see in their professions. The friendly writing style include tips throughout to encourage learning. <p>The book also integrates PHStat, an add-in that bolsters the statistical functions of Excel.</p>
Statistics for Mathematicians
by Victor M. PanaretosThis textbook provides a coherent introduction to the main concepts andmethods of one-parameter statistical inference. Intended for students ofMathematics taking their first course in Statistics, the focus is on Statisticsfor Mathematicians rather than on Mathematical Statistics. The goal is notto focus on the mathematical/theoretical aspects of the subject, but rather toprovide an introduction to the subject tailored to the mindset and tastes ofMathematics students, who are sometimes turned off by the informal nature ofStatistics courses. This book can be used as the basis for an elementary semester-longfirst course on Statistics with a firm sense of direction that does notsacrifice rigor. The deeper goal of the text is to attract the attention ofpromising Mathematics students.
Statistics for Non-Statisticians
by Birger MadsenThis book was written for those who need to know how to collect, analyze and present data. It is meant to be a first course for practitioners, a book for private study or brush-up on statistics, and supplementary reading for general statistics classes. The book is untraditional, both with respect to the choice of topics and the presentation. The topics were determined by what is most useful for practical statistical work: even experienced statisticians will find new topics or new approaches to traditional topics. The presentation is as non-mathematical as possible. Mathematical formulae are presented only if they are necessary for calculations and/or add to readers' understanding. A sample survey is developed as a realistic example throughout the book, and many further examples are presented, which also use data spreadsheets from a supplementary website.
Statistics for Non-Statisticians
by Birger Stjernholm MadsenThisbook was written for those who need to know how to collect, analyze and presentdata. It is meant to be a first course for practitioners, a book for privatestudy or brush-up on statistics, and supplementary reading for generalstatistics classes. The book is untraditional, both with respect to the choiceof topics and the presentation: Topics were determined by what is most usefulfor practical statistical work, and the presentation is as non-mathematical aspossible. The bookcontains many examples using statistical functions in spreadsheets. In thissecond edition, new topics have been included e. g. within the area ofstatistical quality control, in order to make the book even more useful for practitionersworking in industry.
Statistics for nuclear and particle physicists
by Louis LyonsThis book, written by a non-statistician for non-statisticians, emphasises the practical approach to those problems in statistics which arise regularly in data analysis situations in nuclear and high-energy physics experiments. Rather than concentrating on formal proofs of theorems, an abundant use of simple examples illustrates the general ideas which are presented, showing the reader how to obtain the maximum information from the data in the simplest manner. Possible difficulties with the various techniques, and pitfalls to be avoided, are also discussed. Based on a series of lectures given by the author to both students and staff at Oxford, this common-sense approach to statistics will enable nuclear physicists to understand better how to do justice to their data in both analysis and interpretation.
Statistics For Nursing: A Practical Approach
by Elizabeth HeaveyEach new print copy includes Navigate 2 Advantage Access that unlocks a comprehensive and interactive eBook, student practice activities and assessments, a full suite of instructor resources, and learning analytics reporting tools. Statistics for Nursing: A Practical Approach, Third Edition is designed in accordance with the Conversation Theory of Gordon Pask and presents the complicated topic of statistics in an understandable manner for entry level nurses. The underlying principle of this design is to give students the opportunity to practice statistics while they learn statistics. The text accomplishes this through the inclusion of relevant clinical examples followed by end of chapter application exercises. The Third Edition has been updated with practice activities which enable students to apply the content they have learned. In addition, the authors have included new research articles to develop and reinforce literature critiquing skills. These new features provide more opportunities for students to apply the concepts learned while the explanations are beneficial to clinical experts interested in further developing evidence-based skills. This text meets the needs of both undergraduate nursing research students who need to learn how to critically analyze literature as well as graduate DNP students who must also be familiar with statistics for nursing in accordance with the rigor of the DNP program. New to the Third Edition: • Additional review questions • New and updated graphs and figures • Updated lesson content for computer application exercises
Statistics for People Who (Think They) Hate Statistics
by Bruce B. Frey Dr. Neil J. SalkindNow in its Seventh Edition, Neil J. Salkind’s bestselling Statistics for People Who (Think They) Hate Statistics with new co-author Bruce B. Frey teaches an often intimidating subject with a humorous, personable, and informative approach that reduces statistics anxiety. With instruction in SPSS®, the authors guide students through basic and advanced statistical procedures, from correlation and graph creation to analysis of variance, regression, non-parametric tests, and more. The Seventh Edition includes new real-world examples, additional coverage on multiple regression and power and effect size, and a robust interactive eBook with video tutorials and animations of key concepts. In the end, students who (think they) hate statistics will understand how to explain the results of many statistical analyses and won’t be intimidated by basic statistical tasks.
Statistics for People Who (Think They) Hate Statistics
by Bruce B. Frey Dr. Neil J. SalkindNow in its Seventh Edition, Neil J. Salkind’s bestselling Statistics for People Who (Think They) Hate Statistics with new co-author Bruce B. Frey teaches an often intimidating subject with a humorous, personable, and informative approach that reduces statistics anxiety. With instruction in SPSS®, the authors guide students through basic and advanced statistical procedures, from correlation and graph creation to analysis of variance, regression, non-parametric tests, and more. The Seventh Edition includes new real-world examples, additional coverage on multiple regression and power and effect size, and a robust interactive eBook with video tutorials and animations of key concepts. In the end, students who (think they) hate statistics will understand how to explain the results of many statistical analyses and won’t be intimidated by basic statistical tasks.
Statistics For People Who (Think They) Hate Statistics: Excel 2010 Edition
by Neil J. SalkindThe bestselling text Statistics for People Who (Think They) Hate Statistics is the basis for this completely adapted Excel 2010 version. Author Neil J. Salkind presents an often intimidating and difficult subject in a way that is informative, personable, and clear. Researchers and students who find themselves uncomfortable with the analysis portion of their work will appreciate this book′s unhurried pace and thorough, friendly presentation. Salkind begins the Excel version with a complete introduction to the software, and shows the students how to install the Excel Analysis ToolPak option (free) to earn access to a host of new and very useful analytical techniques. He then walks students through various statistical procedures, beginning with correlations and graphical representation of data and ending with inferential techniques and analysis of variance. Pedagogical features include sidebars offering additional technical information about the topic and set-off points that reinforce major themes. Finally, questions to chapter exercises, a complete glossary, and extensive Excel functionality are located at the back of the book. This Third Edition is updated for use with Excel 2010.