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Author: Scott Pardo Publisher: Springer Nature ISBN: 3030433285 Category : Mathematics Languages : en Pages : 278
Book Description
Researchers and students who use empirical investigation in their work must go through the process of selecting statistical methods for analyses, and they are often challenged to justify these selections. This book is designed for readers with limited background in statistical methodology who seek guidance in defending their statistical decision-making in the worlds of research and practice. It is devoted to helping students and scholars find the information they need to select data analytic methods, and to speak knowledgeably about their statistical research processes. Each chapter opens with a conundrum relating to the selection of an analysis, or to explaining the nature of an analysis. Throughout the chapter, the analysis is described, along with some guidance in justifying the choices of that particular method. Designed to offer statistical knowledge to the non-specialist, this volume can be used in courses on research methods, or for courses on statistical applications to biological, medical, life, social, or physical sciences. It will also be useful to academic and industrial researchers in engineering and in the physical sciences who will benefit from a stronger understanding of how to analyze empirical data. The book is written for those with foundational education in calculus. However, a brief review of fundamental concepts of probability and statistics, together with a primer on some concepts in elementary calculus and matrix algebra, is included. R code and sample datasets are provided.
Author: Scott Pardo Publisher: Springer Nature ISBN: 3030433285 Category : Mathematics Languages : en Pages : 278
Book Description
Researchers and students who use empirical investigation in their work must go through the process of selecting statistical methods for analyses, and they are often challenged to justify these selections. This book is designed for readers with limited background in statistical methodology who seek guidance in defending their statistical decision-making in the worlds of research and practice. It is devoted to helping students and scholars find the information they need to select data analytic methods, and to speak knowledgeably about their statistical research processes. Each chapter opens with a conundrum relating to the selection of an analysis, or to explaining the nature of an analysis. Throughout the chapter, the analysis is described, along with some guidance in justifying the choices of that particular method. Designed to offer statistical knowledge to the non-specialist, this volume can be used in courses on research methods, or for courses on statistical applications to biological, medical, life, social, or physical sciences. It will also be useful to academic and industrial researchers in engineering and in the physical sciences who will benefit from a stronger understanding of how to analyze empirical data. The book is written for those with foundational education in calculus. However, a brief review of fundamental concepts of probability and statistics, together with a primer on some concepts in elementary calculus and matrix algebra, is included. R code and sample datasets are provided.
Author: Scott A. Pardo Publisher: Springer ISBN: 3319327682 Category : Mathematics Languages : en Pages : 247
Book Description
This textbook teaches advanced undergraduate and first-year graduate students in Engineering and Applied Sciences to gather and analyze empirical observations (data) in order to aid in making design decisions. While science is about discovery, the primary paradigm of engineering and "applied science" is design. Scientists are in the discovery business and want, in general, to understand the natural world rather than to alter it. In contrast, engineers and applied scientists design products, processes, and solutions to problems. That said, statistics, as a discipline, is mostly oriented toward the discovery paradigm. Young engineers come out of their degree programs having taken courses such as "Statistics for Engineers and Scientists" without any clear idea as to how they can use statistical methods to help them design products or processes. Many seem to think that statistics is only useful for demonstrating that a device or process actually does what it was designed to do. Statistics courses emphasize creating predictive or classification models - predicting nature or classifying individuals, and statistics is often used to prove or disprove phenomena as opposed to aiding in the design of a product or process. In industry however, Chemical Engineers use designed experiments to optimize petroleum extraction; Manufacturing Engineers use experimental data to optimize machine operation; Industrial Engineers might use data to determine the optimal number of operators required in a manual assembly process. This text teaches engineering and applied science students to incorporate empirical investigation into such design processes. Much of the discussion in this book is about models, not whether the models truly represent reality but whether they adequately represent reality with respect to the problems at hand; many ideas focus on how to gather data in the most efficient way possible to construct adequate models. Includes chapters on subjects not often seen together in a single text (e.g., measurement systems, mixture experiments, logistic regression, Taguchi methods, simulation) Techniques and concepts introduced present a wide variety of design situations familiar to engineers and applied scientists and inspire incorporation of experimentation and empirical investigation into the design process. Software is integrally linked to statistical analyses with fully worked examples in each chapter; fully worked using several packages: SAS, R, JMP, Minitab, and MS Excel - also including discussion questions at the end of each chapter. The fundamental learning objective of this textbook is for the reader to understand how experimental data can be used to make design decisions and to be familiar with the most common types of experimental designs and analysis methods.
Author: Andreas Eichler Publisher: Springer ISBN: 3319389688 Category : Education Languages : en Pages : 37
Book Description
This ICME-13 Topical Survey provides a review of recent research into statistics education, with a focus on empirical research published in established educational journals and on the proceedings of important conferences on statistics education. It identifies and addresses six key research topics, namely: teachers’ knowledge; teachers’ role in statistics education; teacher preparation; students’ knowledge; students’ role in statistics education; and how students learn statistics with the help of technology. For each topic, the survey builds upon existing reviews, complementing them with the latest research.
Author: Manfred Schwaiger Publisher: Springer Science & Business Media ISBN: 364255721X Category : Computers Languages : en Pages : 547
Book Description
This volume presents a selection of new methods and approaches in the field of Exploratory Data Analysis. The reader will find numerous ideas and examples for cross disciplinary applications of classification and data analysis methods in fields such as data and web mining, medicine and biological sciences as well as marketing, finance and management sciences.
Author: Wolfgang Wiedermann Publisher: John Wiley & Sons ISBN: 1118947061 Category : Social Science Languages : en Pages : 624
Book Description
b”STATISTICS AND CAUSALITYA one-of-a-kind guide to identifying and dealing with modern statistical developments in causality Written by a group of well-known experts, Statistics and Causality: Methods for Applied Empirical Research focuses on the most up-to-date developments in statistical methods in respect to causality. Illustrating the properties of statistical methods to theories of causality, the book features a summary of the latest developments in methods for statistical analysis of causality hypotheses. The book is divided into five accessible and independent parts. The first part introduces the foundations of causal structures and discusses issues associated with standard mechanistic and difference-making theories of causality. The second part features novel generalizations of methods designed to make statements concerning the direction of effects. The third part illustrates advances in Granger-causality testing and related issues. The fourth part focuses on counterfactual approaches and propensity score analysis. Finally, the fifth part presents designs for causal inference with an overview of the research designs commonly used in epidemiology. Statistics and Causality: Methods for Applied Empirical Research also includes: New statistical methodologies and approaches to causal analysis in the context of the continuing development of philosophical theories End-of-chapter bibliographies that provide references for further discussions and additional research topics Discussions on the use and applicability of software when appropriate Statistics and Causality: Methods for Applied Empirical Research is an ideal reference for practicing statisticians, applied mathematicians, psychologists, sociologists, logicians, medical professionals, epidemiologists, and educators who want to learn more about new methodologies in causal analysis. The book is also an excellent textbook for graduate-level courses in causality and qualitative logic.
Author: Zealure C Holcomb Publisher: Routledge ISBN: 1351867296 Category : Psychology Languages : en Pages : 169
Book Description
• In Real Data, students predict the answers to interesting questions. Then they analyze data supplied by leading researchers to see if there is empirical support for their predictions. • Students get practice in computing all the major statistics usually covered in an introductory statistics course. • Because each of the 35 exercises in Part A deals with only a limited number of statistics, the workbook is easily coordinated with all introductory statistics textbooks. • Part A emphasizes small data sets that are useful whether students are using calculators or computers. The exercises in this part are highly structured so students know exactly what is required of them. • Part B provides larger data sets for comprehensive analysis by computer users. Loosely structured, the data sets allow you to specify which statistics should be computed. • Sample topics: Kissing and Sexual Harassment; Basic Trust of Rape Survivors; Gambling and Stealing; Pregnancy Risk Among Adolescents Who Had Been Sexually Abused; Boys Interacting with Their Fathers; Racial Differences in Seeking Medical Assistance; Instructors’ Clothing and Student Evaluations; Students’ Attitudes Toward Math; and Physician-Assisted Suicide. • Using real data for analysis makes the traditional statistics class come alive.
Author: Akkelies van Nes Publisher: Springer Nature ISBN: 3030591409 Category : Political Science Languages : en Pages : 265
Book Description
This open access textbook is a comprehensive introduction to space syntax method and theory for graduate students and researchers. It provides a step-by-step approach for its application in urban planning and design. This textbook aims to increase the accessibility of the space syntax method for the first time to all graduate students and researchers who are dealing with the built environment, such as those in the field of architecture, urban design and planning, urban sociology, urban geography, archaeology, road engineering, and environmental psychology. Taking a didactical approach, the authors have structured each chapter to explain key concepts and show practical examples followed by underlying theory and provided exercises to facilitate learning in each chapter. The textbook gradually eases the reader into the fundamental concepts and leads them towards complex theories and applications. In summary, the general competencies gain after reading this book are: – to understand, explain, and discuss space syntax as a method and theory; – be capable of undertaking various space syntax analyses such as axial analysis, segment analysis, point depth analysis, or visibility analysis; – be able to apply space syntax for urban research and design practice; – be able to interpret and evaluate space syntax analysis results and embed these in a wider context; – be capable of producing new original work using space syntax. This holistic textbook functions as compulsory literature for spatial analysis courses where space syntax is part of the methods taught. Likewise, this space syntax book is useful for graduate students and researchers who want to do self-study. Furthermore, the book provides readers with the fundamental knowledge to understand and critically reflect on existing literature using space syntax.
Author: Ian Peers Publisher: Routledge ISBN: 1135717516 Category : Education Languages : en Pages : 436
Book Description
Basic statistical concepts such as probability, estimation and inference, and their role in research design and analysis are presented in this volume. The author demonstrates which statistical test to use in given circumstances and how to use it, drawing on data from psychology and education.; Written for those without a strong mathematical background, the book's examples can be worked using a pocket calculator. "Real life" data are analyzed using statistical software (SAS), output is interpreted, and a decision chart is presented which summarizes considerations when choosing a statistical test.
Author: J. V. Deshpande Publisher: Taylor & Francis ISBN: 9788122407075 Category : Mathematics Languages : en Pages : 256
Book Description
Statistical Analysis Of Nonnormal Data Has Successfully Made Available In One Place Nonparametric Methods And Methods Of Discrete Data-Analysis. It Has Attempted To Introduce The Reader To Methods Appropriate For Simple, Continuous, Nonnormal Distribution Of Interest In The Newly Emerging Area Of Survival Analysis And Reliability. The Book Also Provides Computer Programmes For Ready Use.It Can Be Used By Anyone Familiar With Standard Statistical Principles And The Tools In The Framework Of Normal Distribution. Computer Programmes Are In Theready To Use Format. Therefore, Familiarity With Operations Of A Personal Computer And A Dos Environment Is The Only Prerequisite.The Book Would Make An Excellent Text For A Second Course In Statistical Methods For Biologists, Social Scientists, Engineers, Etc. Researchers In Various Disciplines Should Be Able To Use The Methods Described In The Book Without The Benefit Of A Formal Course.
Author: Ding-Geng Chen Publisher: Springer ISBN: 9811025940 Category : Mathematics Languages : en Pages : 222
Book Description
This book gathers invited presentations from the 2nd Symposium of the ICSA- CANADA Chapter held at the University of Calgary from August 4-6, 2015. The aim of this Symposium was to promote advanced statistical methods in big-data sciences and to allow researchers to exchange ideas on statistics and data science and to embraces the challenges and opportunities of statistics and data science in the modern world. It addresses diverse themes in advanced statistical analysis in big-data sciences, including methods for administrative data analysis, survival data analysis, missing data analysis, high-dimensional and genetic data analysis, longitudinal and functional data analysis, the design and analysis of studies with response-dependent and multi-phase designs, time series and robust statistics, statistical inference based on likelihood, empirical likelihood and estimating functions. The editorial group selected 14 high-quality presentations from this successful symposium and invited the presenters to prepare a full chapter for this book in order to disseminate the findings and promote further research collaborations in this area. This timely book offers new methods that impact advanced statistical model development in big-data sciences.
Author: Scott Pardo
Author: Scott Pardo
Author: Scott A. Pardo
Author: Andreas Eichler
Author: Manfred Schwaiger
Author: Wolfgang Wiedermann
Author: Zealure C Holcomb
Author: Akkelies van Nes
Author: Ian Peers
Author: J. V. Deshpande
Author: Ding-Geng Chen