Best Practices in Logistic Regression

Author: Jason W. Osborne
Publisher: SAGE Publications
ISBN: 1483323137
Format: PDF, ePub
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Jason W. Osborne’s Best Practices in Logistic Regression provides students with an accessible, applied approach that communicates logistic regression in clear and concise terms. The book effectively leverages readers’ basic intuitive understanding of simple and multiple regression to guide them into a sophisticated mastery of logistic regression. Osborne’s applied approach offers students and instructors a clear perspective, elucidated through practical and engaging tools that encourage student comprehension.

Regression Linear Modeling

Author: Jason W. Osborne
Publisher: SAGE Publications
ISBN: 1506302785
Format: PDF, Mobi
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In a conversational tone, Regression & Linear Modeling provides conceptual, user-friendly coverage of the generalized linear model (GLM). Readers will become familiar with applications of ordinary least squares (OLS) regression, binary and multinomial logistic regression, ordinal regression, Poisson regression, and loglinear models. Author Jason W. Osborne returns to certain themes throughout the text, such as testing assumptions, examining data quality, and, where appropriate, nonlinear and non-additive effects modeled within different types of linear models.

Best Practices in Quantitative Methods

Author: Jason W. Osborne
Publisher: SAGE
ISBN: 1412940656
Format: PDF, ePub
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The contributors to Best Practices in Quantitative Methods envision quantitative methods in the 21st century, identify the best practices, and, where possible, demonstrate the superiority of their recommendations empirically. Editor Jason W. Osborne designed this book with the goal of providing readers with the most effective, evidence-based, modern quantitative methods and quantitative data analysis across the social and behavioral sciences. The text is divided into five main sections covering select best practices in Measurement, Research Design, Basics of Data Analysis, Quantitative Methods, and Advanced Quantitative Methods. Each chapter contains a current and expansive review of the literature, a case for best practices in terms of method, outcomes, inferences, etc., and broad-ranging examples along with any empirical evidence to show why certain techniques are better. Key Features: Describes important implicit knowledge to readers: The chapters in this volume explain the important details of seemingly mundane aspects of quantitative research, making them accessible to readers and demonstrating why it is important to pay attention to these details. Compares and contrasts analytic techniques: The book examines instances where there are multiple options for doing things, and make recommendations as to what is the "best" choice—or choices, as what is best often depends on the circumstances. Offers new procedures to update and explicate traditional techniques: The featured scholars present and explain new options for data analysis, discussing the advantages and disadvantages of the new procedures in depth, describing how to perform them, and demonstrating their use. Intended Audience: Representing the vanguard of research methods for the 21st century, this book is an invaluable resource for graduate students and researchers who want a comprehensive, authoritative resource for practical and sound advice from leading experts in quantitative methods.

Regression

Author: Ludwig Fahrmeir
Publisher: Springer-Verlag
ISBN: 3642018378
Format: PDF, Kindle
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In dem Band beschreiben die Autoren erstmals klassische Regressionsansätze und moderne nicht- und semiparametrische Methoden in einer integrierten und anwendungsorientierten Form. Um Lesern die Analyse eigener Fragestellungen zu ermöglichen, demonstrieren sie die praktische Anwendung der Konzepte und Methoden anhand ausführlicher Fallstudien. Geeignet für Studierende der Statistik sowie für Wissenschaftler und Praktiker, zum Beispiel in den Wirtschafts- und Sozialwissenschaften, der Bioinformatik und -statistik, Ökonometrie und Epidemiologie.

Best Practices in Data Cleaning

Author: Jason W. Osborne
Publisher: SAGE
ISBN: 1412988012
Format: PDF, ePub, Docs
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Many researchers jump from data collection directly into testing hypothesis without realizing these tests can go profoundly wrong without clean data. This book provides a clear, accessible, step-by-step process of important best practices in preparing for data collection, testing assumptions, and examining and cleaning data in order to decrease error rates and increase both the power and replicability of results. Jason W. Osborne, author of the handbook Best Practices in Quantitative Methods (SAGE, 2008) provides easily-implemented suggestions that are evidence-based and will motivate change in practice by empirically demonstrating—for each topic—the benefits of following best practices and the potential consequences of not following these guidelines.

Best Practice Guide on the Control of Arsenic in Drinking Water

Author: Prosun Bhattacharya
Publisher: IWA Publishing
ISBN: 1843393859
Format: PDF, Mobi
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Arsenic in drinking water derived from groundwater is arguably the biggest environmental chemical human health risk known at the present time, with well over 100,000,000 people around the world being exposed. Monitoring the hazard, assessing exposure and health risks and implementing effective remediation are therefore key tasks for organisations and individuals with responsibilities related to the supply of safe, clean drinking water. Best Practice Guide on the Control of Arsenic in Drinking Water, covering aspects of hazard distribution, exposure, health impacts, biomonitoring and remediation, including social and economic issues, is therefore a very timely contribution to disseminating useful knowledge in this area. The volume contains 10 short reviews of key aspects of this issue, supplemented by a further 14 case studies, each of which focusses on a particular area or technological or other practice, and written by leading experts in the field. Detailed selective reference lists provide pointers to more detailed guidance on relevant practice. The volume includes coverage of (i) arsenic hazard in groundwater and exposure routes to humans, including case studies in USA, SE Asia and UK; (ii) health impacts arising from exposure to arsenic in drinking water and biomonitoring approaches; (iii) developments in the nature of regulation of arsenic in drinking water; (iv) sampling and monitoring of arsenic, including novel methodologies; (v) approaches to remediation, particularly in the context of water safety planning, and including case studies from the USA, Italy, Poland and Bangladesh; and (vi) socio-economic aspects of remediation, including non-market valuation methods and local community engagement.

Treasure Chest of Six Sigma Growth Methods Tools and Best Practices Adobe Reader

Author: Lynne Hambleton
Publisher: Pearson Education
ISBN: 9780132797290
Format: PDF, ePub, Docs
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This reference is the first comprehensive how-to collection of Six Sigma tools, methodologies, and best practices. Leading implementer Lynne Hambleton covers the entire Six Sigma toolset, including more than 70 different tools–ranging from rigorous statistical and quantitative tools, to “softer” techniques. The toolset is organized in an easy-to-use, alphabetical encyclopedia and helps professionals quickly select the right tool, at the right time for every business challenge. Hambleton systematically discusses which questions each tool is designed to answer; how the tool compares with similar tools; when to use it; how to use it step-by-step; how to analyze and apply the output; and which other tool to use with it. To further illustrate and clarify tool usage, she presents hundreds of figures, along with never-before-published hints, tips, and real-world, “out-of-the-box” examples. Coverage includes · Real-world guidance to help practitioners raise the most important questions and determine the best resolution · Statistical techniques, including ANOVA, multi-vari charts, Monte Carlo simulations, normal probability plots, and regression analysis · Benchmarks, capability and cost/benefit analyses, Porter’s Five Forces, scorecards, stakeholder analysis, and brainstorming techniques · CPM, CTQ, FMEA, HOQ, and GOSPA · GANTT, PERT chart, and other Six Sigma project management tools · 7QC: cause and effect diagrams, checklists, control charts, fishbone diagram, flowchart, histogram, Pareto chart, process maps, run chart, scatter diagram, and the stratification tool · 7M: AND, affinity diagrams, interrelationship diagrams, matrix diagrams, prioritization matrices, PDPC, and tree diagrams · Crystal Ball, Minitab, and Quality Companion 2 software to facilitate the use of statistical and analytical tools and more to help you become a more effective Six Sigma practitioner · This book is also available in a highly-searchable eBook format at www.prenhallprofessional.com/title/0136007376 and other online booksellers,. From start to finish, this bookdelivers fast, thorough and reliable answers–knowledge you’ll rely on in every Six Sigma project, for years to come.

An Introduction to Statistical Concepts

Author: Debbie L Hahs-Vaughn
Publisher: Routledge
ISBN: 1136490124
Format: PDF, ePub, Mobi
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This comprehensive, flexible text is used in both one- and two-semester courses to review introductory through intermediate statistics. Instructors select the topics that are most appropriate for their course. Its conceptual approach helps students more easily understand the concepts and interpret SPSS and research results. Key concepts are simply stated and occasionally reintroduced and related to one another for reinforcement. Numerous examples demonstrate their relevance. This edition features more explanation to increase understanding of the concepts. Only crucial equations are included. In addition to updating throughout, the new edition features: New co-author, Debbie L. Hahs-Vaughn, the 2007 recipient of the University of Central Florida's College of Education Excellence in Graduate Teaching Award. A new chapter on logistic regression models for today's more complex methodologies. More on computing confidence intervals and conducting power analyses using G*Power. Many more SPSS screenshots to assist with understanding how to navigate SPSS and annotated SPSS output to assist in the interpretation of results. Extended sections on how to write-up statistical results in APA format. New learning tools including chapter-opening vignettes, outlines, and a list of key concepts, many more examples, tables, and figures, boxes, and chapter summaries. More tables of assumptions and the effects of their violation including how to test them in SPSS. 33% new conceptual, computational, and all new interpretative problems. A website that features PowerPoint slides, answers to the even-numbered problems, and test items for instructors, and for students the chapter outlines, key concepts, and datasets that can be used in SPSS and other packages, and more. Each chapter begins with an outline, a list of key concepts, and a vignette related to those concepts. Realistic examples from education and the behavioral sciences illustrate those concepts. Each example examines the procedures and assumptions and provides instructions for how to run SPSS, including annotated output, and tips to develop an APA style write-up. Useful tables of assumptions and the effects of their violation are included, along with how to test assumptions in SPSS. 'Stop and Think' boxes provide helpful tips for better understanding the concepts. Each chapter includes computational, conceptual, and interpretive problems. The data sets used in the examples and problems are provided on the web. Answers to the odd-numbered problems are given in the book. The first five chapters review descriptive statistics including ways of representing data graphically, statistical measures, the normal distribution, and probability and sampling. The remainder of the text covers inferential statistics involving means, proportions, variances, and correlations, basic and advanced analysis of variance and regression models. Topics not dealt with in other texts such as robust methods, multiple comparison and nonparametric procedures, and advanced ANOVA and multiple and logistic regression models are also reviewed. Intended for one- or two-semester courses in statistics taught in education and/or the behavioral sciences at the graduate and/or advanced undergraduate level, knowledge of statistics is not a prerequisite. A rudimentary knowledge of algebra is required.

Logistic Regression Second Edition

Author: Gerardus Blokdyk
Publisher: 5starcooks
ISBN: 9780655308010
Format: PDF, Mobi
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Is there any existing Logistic regression governance structure? To what extent does management recognize Logistic regression as a tool to increase the results? What other areas of the organization might benefit from the Logistic regression team's improvements, knowledge, and learning? Who are the people involved in developing and implementing Logistic regression? Have all basic functions of Logistic regression been defined? Defining, designing, creating, and implementing a process to solve a challenge or meet an objective is the most valuable role... In EVERY group, company, organization and department. Unless you are talking a one-time, single-use project, there should be a process. Whether that process is managed and implemented by humans, AI, or a combination of the two, it needs to be designed by someone with a complex enough perspective to ask the right questions. Someone capable of asking the right questions and step back and say, 'What are we really trying to accomplish here? And is there a different way to look at it?' This Self-Assessment empowers people to do just that - whether their title is entrepreneur, manager, consultant, (Vice-)President, CxO etc... - they are the people who rule the future. They are the person who asks the right questions to make Logistic regression investments work better. This Logistic regression All-Inclusive Self-Assessment enables You to be that person. All the tools you need to an in-depth Logistic regression Self-Assessment. Featuring 682 new and updated case-based questions, organized into seven core areas of process design, this Self-Assessment will help you identify areas in which Logistic regression improvements can be made. In using the questions you will be better able to: - diagnose Logistic regression projects, initiatives, organizations, businesses and processes using accepted diagnostic standards and practices - implement evidence-based best practice strategies aligned with overall goals - integrate recent advances in Logistic regression and process design strategies into practice according to best practice guidelines Using a Self-Assessment tool known as the Logistic regression Scorecard, you will develop a clear picture of which Logistic regression areas need attention. Your purchase includes access details to the Logistic regression self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows your organization exactly what to do next. Your exclusive instant access details can be found in your book.