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Statistical Process Control in Manufacturing Practice
by Fred W. KearEmphasizing the importance of understanding and reducing process variation to achieve quality manufacturing performance, this work establishes how statistical process control (SPC) provides powerful tools for measuring and regulating manufacturing processes. It presents information derived from time-tested applications of SPC techniques at on-site process situations in manufacturing. It is designed to assist manufacturing organizations in explaining and implementing successful SPC programmes.
Statistical Process Control: A Pragmatic Approach (Continuous Improvement Series)
by Stephen MundwillerPeople with minimal math skills, and even those with advanced math skills, have difficulty grasping the intuitive concepts behind Statistical Process Control (SPC). Many practitioners do not understand the concepts behind Control Charts, the differences of out of control and out of specification, and the process variation on Control Charts. This book will explain these concepts by using a simple methodology that will bring a much greater level of understanding to those that use it by providing a detailed description of the method, using common language, real-world examples to illustrate the concept, and instructions on easy implementation.
Statistical Process Monitoring and Optimization (Statistics: A Series of Textbooks and Monographs)
by G. Geoffrey Vining Sung H. ParkDemonstrates ways to track industrial processes and performance, integrating related areas such as engineering process control, statistical reasoning in TQM, robust parameter design, control charts, multivariate process monitoring, capability indices, experimental design, empirical model building, and process optimization. The book covers a range o
Statistical Programming in SAS
by A. John BailerStatistical Programming in SAS Second Edition provides a foundation for programming to implement statistical solutions using SAS, a system that has been used to solve data analytic problems for more than 40 years. The author includes motivating examples to inspire readers to generate programming solutions. Upper-level undergraduates, beginning graduate students, and professionals involved in generating programming solutions for data-analytic problems will benefit from this book. The ideal background for a reader is some background in regression modeling and introductory experience with computer programming. The coverage of statistical programming in the second edition includes Getting data into the SAS system, engineering new features, and formatting variables Writing readable and well-documented code Structuring, implementing, and debugging programs that are well documented Creating solutions to novel problems Combining data sources, extracting parts of data sets, and reshaping data sets as needed for other analyses Generating general solutions using macros Customizing output Producing insight-inspiring data visualizations Parsing, processing, and analyzing text Programming solutions using matrices and connecting to R Processing text Programming with matrices Connecting SAS with R Covering topics that are part of both base and certification exams.
Statistical Properties in Firms’ Large-scale Data (Evolutionary Economics and Social Complexity Science #26)
by Atushi IshikawaThis is the first book to provide a systematic description of statistical properties of large-scale financial data. Specifically, the power-law and log-normal distributions observed at a given time and their changes using time-reversal symmetry, quasi-time-reversal symmetry, Gibrat's law, and the non-Gibrat's property observed in a short-term period are derived here. The statistical properties observed over a long-term period, such as power-law and exponential growth, are also derived. These subjects have not been thoroughly discussed in the field of economics in the past, and this book is a compilation of the author's series of studies by reconstructing the data analyses published in 15 academic journals with new data. This book provides readers with a theoretical and empirical understanding of how the statistical properties observed in firms’ large-scale data are related along the time axis. It is possible to expand this discussion to understand theoretically and empirically how the statistical properties observed among differing large-scale financial data are related. This possibility provides readers with an approach to microfoundations, an important issue that has been studied in economics for many years.
Statistical Quality Control
by M. Jeya ChandraIt has recently become apparent that "quality" is quickly becoming the single most important factor for success and growth in business. Companies achieving higher quality in their products through effective quality improvement programs enjoy a significant competitive advantage. It is, therefore, essential for engineers responsible for design, devel
Statistical Quality Control Methods: Statistics: A Series of Textbooks and Monographs
by Irving W. BurrStatistical Quality Control Methods by Irving W. Burr.
Statistical Quality Control for Process Improvement
by Roger E. BohnDescribes systematic methods for process debugging and improvement, based on statistical quality control. Examples are from manufacturing settings, but techniques are also useful for services and sales, and to quantity improvement as well as quality improvement.
Statistical Quality Control: Using MINITAB, R, JMP and Python
by Bhisham C. GuptaSTATISTICAL QUALITY CONTROL Provides a basic understanding of statistical quality control (SQC) and demonstrates how to apply the techniques of SQC to improve the quality of products in various sectorsThis book introduces Statistical Quality Control and the elements of Six Sigma Methodology, illustrating the widespread applications that both have for a multitude of areas, including manufacturing, finance, transportation, and more. It places emphasis on both the theory and application of various SQC techniques and offers a large number of examples using data encountered in real life situations to support each theoretical concept.Statistical Quality Control: Using MINITAB, R, JMP and Python begins with a brief discussion of the different types of data encountered in various fields of statistical applications and introduces graphical and numerical tools needed to conduct preliminary analysis of the data. It then discusses the basic concept of statistical quality control (SQC) and Six Sigma Methodology and examines the different types of sampling methods encountered when sampling schemes are used to study certain populations. The book also covers Phase 1 Control Charts for variables and attributes; Phase II Control Charts to detect small shifts; the various types of Process Capability Indices (CPI); certain aspects of Measurement System Analysis (MSA); various aspects of PRE-control; and more. This helpful guide alsoFocuses on the learning and understanding of statistical quality control for second and third year undergraduates and practitioners in the fieldDiscusses aspects of Six Sigma MethodologyTeaches readers to use MINITAB, R, JMP and Python to create and analyze chartsRequires no previous knowledge of statistical theoryIs supplemented by an instructor-only book companion site featuring data sets and a solutions manual to all problems, as well as a student book companion site that includes data sets and a solutions manual to all odd-numbered problemsStatistical Quality Control: Using MINITAB, R, JMP and Python is an excellent book for students studying engineering, statistics, management studies, and other related fields and who are interested in learning various techniques of statistical quality control. It also serves as a desk reference for practitioners who work to improve quality in various sectors, such as manufacturing, service, transportation, medical, oil, and financial institutions. It‘s also useful for those who use Six Sigma techniques to improve the quality of products in such areas.
Statistical Quality Technologies: Theory and Practice (ICSA Book Series in Statistics)
by Hon Keung Tony Ng Ding-Geng Chen Yuhlong Lio Tzong-Ru TsaiThis book explores different statistical quality technologies including recent advances and applications. Statistical process control, acceptance sample plans and reliability assessment are some of the essential statistical techniques in quality technologies to ensure high quality products and to reduce consumer and producer risks. Numerous statistical techniques and methodologies for quality control and improvement have been developed in recent years to help resolve current product quality issues in today’s fast changing environment. Featuring contributions from top experts in the field, this book covers three major topics: statistical process control, acceptance sampling plans, and reliability testing and designs. The topics covered in the book are timely and have a high potential impact and influence to academics, scholars, students and professionals in statistics, engineering, manufacturing and health.
Statistical Quantitative Methods in Finance: From Theory to Quantitative Portfolio Management
by Samit AhlawatStatistical quantitative methods are vital for financial valuation models and benchmarking machine learning models in finance. This book explores the theoretical foundations of statistical models, from ordinary least squares (OLS) to the generalized method of moments (GMM) used in econometrics. It enriches your understanding through practical examples drawn from applied finance, demonstrating the real-world applications of these concepts. Additionally, the book delves into non-linear methods and Bayesian approaches, which are becoming increasingly popular among practitioners thanks to advancements in computational resources. By mastering these topics, you will be equipped to build foundational models crucial for applied data science, a skill highly sought after by software engineering and asset management firms. The book also offers valuable insights into quantitative portfolio management, showcasing how traditional data science tools can be enhanced with machine learning models. These enhancements are illustrated through real-world examples from finance and econometrics, accompanied by Python code. This practical approach ensures that you can apply what you learn, gaining proficiency in the statsmodels library and becoming adept at designing, implementing, and calibrating your models. By understanding and applying these statistical models, you enhance your data science skills and effectively tackle financial challenges. What You Will Learn Understand the fundamentals of linear regression and its applications in financial data analysis and prediction Apply generalized linear models for handling various types of data distributions and enhancing model flexibility Gain insights into regime switching models to capture different market conditions and improve financial forecasting Benchmark machine learning models against traditional statistical methods to ensure robustness and reliability in financial applications Who This Book Is For Data scientists, machine learning engineers, finance professionals, and software engineers
Statistical Reasoning For Everyday Life (Fourth Edition)
by Jeffrey O. Bennett William L. Briggs Mario F. Triola Bill F. BriggsStatistical Reasoning for Everyday Life, Fourth Edition, provides students with a clear understanding of statistical concepts and ideas so they can become better critical thinkers and decision makers, whether they decide to start a business, plan for their financial future, or just watch the news. The authors bring statistics to life by applying statistical concepts to the real world situations, taken from news sources, the internet, and individual experiences.
Statistical Regression and Classification: From Linear Models to Machine Learning (Chapman & Hall/CRC Texts in Statistical Science)
by Norman Matloff<p>Statistical Regression and Classification: From Linear Models to Machine Learning takes an innovative look at the traditional statistical regression course, presenting a contemporary treatment in line with today's applications and users. <p>The book treats classical regression methods in an innovative, contemporary manner. Though some statistical learning methods are introduced, the primary methodology used is linear and generalized linear parametric models, covering both the Description and Prediction goals of regression methods. The author is just as interested in Description applications of regression, such as measuring the gender wage gap in Silicon Valley, as in forecasting tomorrow's demand for bike rentals. An entire chapter is devoted to measuring such effects, including discussion of Simpson's Paradox, multiple inference, and causation issues. Similarly, there is an entire chapter of parametric model fit, making use of both residual analysis and assessment via nonparametric analysis.</p>
Statistical Reinforcement Learning: Modern Machine Learning Approaches (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
by Masashi SugiyamaReinforcement learning (RL) is a framework for decision making in unknown environments based on a large amount of data. Several practical RL applications for business intelligence, plant control, and gaming have been successfully explored in recent years. Providing an accessible introduction to the field, this book covers model-based and model-free approaches, policy iteration, and policy search methods. It presents illustrative examples and state-of-the-art results, including dimensionality reduction in RL and risk-sensitive RL. The book provides a bridge between RL and data mining and machine learning research.
Statistical Sampling and Risk Analysis in Auditing
by Peter JonesThis book is aimed at those with responsibilities for audit, risk and control - auditors of course - but also finance directors, audit committee members, project accountants, systems designers and other professionals too. Working under pressure, these people often need to take account of theory and best practice but strike a balance with the practical demands of their workplace. This book’s practical emphasis on meeting the ever-changing needs of clients and auditees will benefit a wide audience by helping readers to: ¢ select a suitable, practical sampling approach ¢ appreciate the statistical implications ¢ evaluate the results of audit testing ¢ take account of risk and control evaluation in targeting valuable audit resources. It does this by laying out the principles behind a concept and then grounding them in ’real life’ cases for the reader to work through. These are accompanied by suggested solutions which, while not definitive answers, do provide valuable advice and guidance. Finally the range of appendices, including a complete copy of the statement of auditing standards, SAS 430, make this book an essential resource for everyone concerned about modern auditing.
Statistical Tableau: How to Use Statistical Models and Decision Science in Tableau
by Ethan LangIn today's data-driven world, understanding statistical models is crucial for effective analysis and decision making. Whether you're a beginner or an experienced user, this book equips you with the foundational knowledge to grasp and implement statistical models within Tableau. Gain the confidence to speak fluently about the models you employ, driving adoption of your insights and analysis across your organization.As AI continues to revolutionize industries, possessing the skills to leverage statistical models is no longer optional—it's a necessity. Stay ahead of the curve and harness the full potential of your data by mastering the ability to interpret and utilize the insights generated by these models.Whether you're a data enthusiast, analyst, or business professional, this book empowers you to navigate the ever-evolving landscape of data analytics with confidence and proficiency. Start your journey toward data mastery today.In this book, you will learn:The basics of foundational statistical modeling with TableauHow to prove your analysis is statistically significantHow to calculate and interpret confidence intervalsBest practices for incorporating statistics into data visualizationsHow to connect external analytics resources from Tableau using R and Python
Statistical Techniques for Project Control (Systems Innovation Book Series)
by Adedeji B. Badiru Tina AgustiadyWinner of the IIE Book of the Month for June 2012A project can be simple or complex. In each case, proven project management processes must be followed. In all cases of project management implementation, control must be exercised in order to assure that project objectives are achieved. Statistical Techniques for Project Control seamlessly integrate
Statistical Thinking
by Ron D. Snee Roger HoerlHow statistical thinking and methodology can help you make crucial business decisionsStraightforward and insightful, Statistical Thinking: Improving Business Performance, Second Edition, prepares you for business leadership by developing your capacity to apply statistical thinking to improve business processes. Unique and compelling, this book shows you how to derive actionable conclusions from data analysis, solve real problems, and improve real processes. Here, you'll discover how to implement statistical thinking and methodology in your work to improve business performance.Explores why statistical thinking is necessary and helpfulProvides case studies that illustrate how to integrate several statistical tools into the decision-making processFacilitates and encourages an experiential learning environment to enable you to apply material to actual problemsWith an in-depth discussion of JMP® software, the new edition of this important book focuses on skills to improve business processes, including collecting data appropriate for a specified purpose, recognizing limitations in existing data, and understanding the limitations of statistical analyses.
Statistical Thinking in Business
by J. A. John D. WhitakerBusiness students need the ability to think statistically about how to deal with uncertainty and its effect on decision-making in business and management. Traditional statistics courses and textbooks tend to focus on probability, mathematical detail, and heavy computation, and thus fail to meet the needs of future managers. Statistical Thinking in Business, Second Edition responds to the growing recognition that we must change the way business statistics is taught. It shows how statistics is important in all aspects of business and equips students with the skills they need to make sensible use of data and other information. The authors take an interactive, scenario-based approach and use almost no mathematical formulas, opting to use Excel for the technical work. This allows them to focus on using statistics to aid decision-making rather than how to perform routine calculations.New in the Second Edition A completely revised chapter on forecasting Re-arrangement of the material on data presentation with the inclusion of histograms and cumulative line plots A more thorough discussion of the analysis of attribute data Coverage of variable selection and model building in multiple regression End-of-chapter summaries More end-of-chapter problems A variety of case studies throughout the book The second edition also comes with a wealth of ancillary materials provided on downloadable resources packaged with the book. These include automatically-marked multiple-choice questions, answers to questions in the text, data sets, Excel experiments and demonstrations, an introduction to Excel, and the StiBstat Add-In for stem and leaf plots, box plots, distribution plots, control charts and summary statistics.
Statistical Thinking: Improving Business Performance (Wiley and SAS Business Series #58)
by Roger W. Hoerl Ronald D. SneeApply statistics in business to achieve performance improvement Statistical Thinking: Improving Business Performance, 3rd Edition helps managers understand the role of statistics in implementing business improvements. It guides professionals who are learning statistics in order to improve performance in business and industry. It also helps graduate and undergraduate students understand the strategic value of data and statistics in arriving at real business solutions. Instruction in the book is based on principles of effective learning, established by educational and behavioral research. The authors cover both practical examples and underlying theory, both the big picture and necessary details. Readers gain a conceptual understanding and the ability to perform actionable analyses. They are introduced to data skills to improve business processes, including collecting the appropriate data, identifying existing data limitations, and analyzing data graphically. The authors also provide an in-depth look at JMP software, including its purpose, capabilities, and techniques for use. Updates to this edition include: A new chapter on data, assessing data pedigree (quality), and acquisition tools Discussion of the relationship between statistical thinking and data science Explanation of the proper role and interpretation of p-values (understanding of the dangers of “p-hacking”) Differentiation between practical and statistical significance Introduction of the emerging discipline of statistical engineering Explanation of the proper role of subject matter theory in order to identify causal relationships A holistic framework for variation that includes outliers, in addition to systematic and random variation Revised chapters based on significant teaching experience Content enhancements based on student input This book helps readers understand the role of statistics in business before they embark on learning statistical techniques.
Statistical Tools for Program Evaluation: Methods and Applications to Economic Policy, Public Health, and Education
by Jean-Michel Josselin Benoît Le MauxThis book provides a self-contained presentation of the statistical tools required for evaluating public programs, as advocated by many governments, the World Bank, the European Union, and the Organization for Economic Cooperation and Development. After introducing the methodological framework of program evaluation, the first chapters are devoted to the collection, elementary description and multivariate analysis of data as well as the estimation of welfare changes. The book then successively presents the tools of ex-ante methods (financial analysis, budget planning, cost-benefit, cost-effectiveness and multi-criteria evaluation) and ex-post methods (benchmarking, experimental and quasi-experimental evaluation). The step-by-step approach and the systematic use of numerical illustrations equip readers to handle the statistics of program evaluation. It not only offers practitioners from public administrations, consultancy firms and nongovernmental organizations the basic tools and advanced techniques used in program assessment, it is also suitable for executive management training, upper undergraduate and graduate courses, as well as for self-study.
Statistical Tragedy in Africa?: Evaluating the Database for African Economic Development
by Morten Jerven and Deborah JohnstonWhat do we know about economic development in Africa? The answer is that we know much less than we would like to think. This collection assesses the knowledge problem present in statistics on poverty, agriculture, labour, education, health, and economic growth. While diverse in origin, the contributors to this book are unified in two conclusions: the quality and quantity of data needs to be improved; and this is a concern not just for statisticians. Weaknesses in statistical methodology and practice can misinform policy makers, international agencies, donors, the private sector, and the citizens of African countries themselves. This is also a problem for academics from various disciplines, from history and economics to social epidemiology and education policy. Not only does academic work on Africa regularly use flawed data, but many problems encountered in surveys challenge common academic abstractions. By exploring these flaws, this book will provide a guide for scholars, policy makers, and all those using and commissioning surveys in Africa. This book was originally published as a special issue of The Journal of Development Studies.
Statistical and Probabilistic Methods in Actuarial Science (Chapman & Hall/CRC Series in Actuarial Science)
by Philip J. BolandStatistical and Probabilistic Methods in Actuarial Science covers many of the diverse methods in applied probability and statistics for students aspiring to careers in insurance, actuarial science, and finance. The book builds on students' existing knowledge of probability and statistics by establishing a solid and thorough understanding of
Statistical: Ten Easy Ways to Avoid Being Misled By Numbers
by Anthony Reuben'Refreshingly clear and engaging' Tim Harford'Delightful . . . full of unique insights' Prof Sir David SpiegelhalterThere's no getting away from statistics. We encounter them every day. We are all users of statistics whether we like it or not.Do missed appointments really cost the NHS £1bn per year?What's the difference between the mean gender pay gap and the median gender pay gap?How can we work out if a claim that we use 42 billion single-use plastic straws per year in the UK is accurate?What did the Vote Leave campaign's £350m bus really mean?How can we tell if the headline 'Public pensions cost you £4,000 a year' is correct?Does snow really cost the UK economy £1bn per day?But how do we distinguish statistical fact from fiction? What can we do to decide whether a number, claim or news story is accurate? Without an understanding of data, we cannot truly understand what is going on in the world around us.Written by Anthony Reuben, the BBC's first head of statistics, Statistical is an accessible and empowering guide to challenging the numbers all around us.
Statistics 101: From Data Analysis and Predictive Modeling to Measuring Distribution and Determining Probability, Your Essential Guide to Statistics (Adams 101)
by David BormanA comprehensive guide to statistics—with information on collecting, measuring, analyzing, and presenting statistical data—continuing the popular 101 series. Data is everywhere. In the age of the internet and social media, we’re responsible for consuming, evaluating, and analyzing data on a daily basis. From understanding the percentage probability that it will rain later today, to evaluating your risk of a health problem, or the fluctuations in the stock market, statistics impact our lives in a variety of ways, and are vital to a variety of careers and fields of practice. Unfortunately, most statistics text books just make us want to take a snooze, but with Statistics 101, you’ll learn the basics of statistics in a way that is both easy-to-understand and apply. From learning the theory of probability and different kinds of distribution concepts, to identifying data patterns and graphing and presenting precise findings, this essential guide can help turn statistical math from scary and complicated, to easy and fun. Whether you are a student looking to supplement your learning, a worker hoping to better understand how statistics works for your job, or a lifelong learner looking to improve your grasp of the world, Statistics 101 has you covered.