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Best Practices In Logistic Regression

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Best Practices In Logistic Regression

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Author : Jason W. Osborne
language : en
Publisher: SAGE Publications
Release Date : 2014-02-26

Best Practices In Logistic Regression written by Jason W. Osborne and has been published by SAGE Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-02-26 with Social Science categories.

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.

Best Practices In Quantitative Methods

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Author : Jason W. Osborne
language : en
Publisher: SAGE
Release Date : 2008

Best Practices In Quantitative Methods written by Jason W. Osborne and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Language Arts & Disciplines categories.

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.

Secondary Analysis Of Electronic Health Records

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Author : MIT Critical Data
language : en
Publisher: Springer
Release Date : 2016-09-09

Secondary Analysis Of Electronic Health Records written by MIT Critical Data and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-09-09 with Medical categories.

This book trains the next generation of scientists representing different disciplines to leverage the data generated during routine patient care. It formulates a more complete lexicon of evidence-based recommendations and support shared, ethical decision making by doctors with their patients. Diagnostic and therapeutic technologies continue to evolve rapidly, and both individual practitioners and clinical teams face increasingly complex ethical decisions. Unfortunately, the current state of medical knowledge does not provide the guidance to make the majority of clinical decisions on the basis of evidence. The present research infrastructure is inefficient and frequently produces unreliable results that cannot be replicated. Even randomized controlled trials (RCTs), the traditional gold standards of the research reliability hierarchy, are not without limitations. They can be costly, labor intensive, and slow, and can return results that are seldom generalizable to every patient population. Furthermore, many pertinent but unresolved clinical and medical systems issues do not seem to have attracted the interest of the research enterprise, which has come to focus instead on cellular and molecular investigations and single-agent (e.g., a drug or device) effects. For clinicians, the end result is a bit of a “data desert” when it comes to making decisions. The new research infrastructure proposed in this book will help the medical profession to make ethically sound and well informed decisions for their patients.

Applied Logistic Regression

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Author : David W. Hosmer, Jr.
language : en
Publisher: John Wiley & Sons
Release Date : 2004-10-28

Applied Logistic Regression written by David W. Hosmer, Jr. and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-10-28 with Mathematics categories.

From the reviews of the First Edition. "An interesting, useful, and well-written book on logistic regression models . . . Hosmer and Lemeshow have used very little mathematics, have presented difficult concepts heuristically and through illustrative examples, and have included references." —Choice "Well written, clearly organized, and comprehensive . . . the authors carefully walk the reader through the estimation of interpretation of coefficients from a wide variety of logistic regression models . . . their careful explication of the quantitative re-expression of coefficients from these various models is excellent." —Contemporary Sociology "An extremely well-written book that will certainly prove an invaluable acquisition to the practicing statistician who finds other literature on analysis of discrete data hard to follow or heavily theoretical." —The Statistician In this revised and updated edition of their popular book, David Hosmer and Stanley Lemeshow continue to provide an amazingly accessible introduction to the logistic regression model while incorporating advances of the last decade, including a variety of software packages for the analysis of data sets. Hosmer and Lemeshow extend the discussion from biostatistics and epidemiology to cutting-edge applications in data mining and machine learning, guiding readers step-by-step through the use of modeling techniques for dichotomous data in diverse fields. Ample new topics and expanded discussions of existing material are accompanied by a wealth of real-world examples-with extensive data sets available over the Internet.

Best Practices In Logistic Regression

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Author : Jason W. Osborne
language : en
Publisher: SAGE Publications
Release Date : 2014-02-26

A I Machine Learning When You Don T Know Sh T

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Author : Lyron Foster
language : en
Publisher: Career Kick Start Books, LLC
Release Date : 2023-04-05

A I Machine Learning When You Don T Know Sh T written by Lyron Foster and has been published by Career Kick Start Books, LLC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-05 with Computers categories.

This is a comprehensive book that provides a step-by-step guide to learning machine learning concepts and techniques, from basic terminology and concepts to advanced machine learning concepts and techniques. The book is ideal for beginners and intermediate learners who want to gain a strong foundation in machine learning, and it is suitable for anyone interested in learning machine learning regardless of their technical background. The book covers the different types of machine learning, including supervised, unsupervised, and reinforcement learning, and provides an in-depth overview of commonly used machine learning algorithms and techniques. The book also covers advanced machine learning concepts and techniques, including building machine learning pipelines, using machine learning for natural language processing and computer vision tasks, and best practices for advanced machine learning systems. The book provides hands-on examples and exercises throughout, making it easy for readers to apply what they have learned to real-world problems. In addition, the book covers best practices for model evaluation and hyperparameter tuning, feature engineering and data preprocessing, and using machine learning tools and libraries. The book also includes an explanation of the development environment setup, making it easy for readers to get started with machine learning.

Treasure Chest Of Six Sigma Growth Methods Tools And Best Practices Adobe Reader

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Author : Lynne Hambleton
language : en
Publisher: Pearson Education
Release Date : 2007-07-06

Treasure Chest Of Six Sigma Growth Methods Tools And Best Practices Adobe Reader written by Lynne Hambleton and has been published by Pearson Education this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-07-06 with Technology & Engineering categories.

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.

Quantitative Historical Linguistics

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Author : Gard B. Jenset
language : en
Publisher: Oxford University Press
Release Date : 2017-09-22

Quantitative Historical Linguistics written by Gard B. Jenset and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-22 with Language Arts & Disciplines categories.

This book is an innovative guide to quantitative, corpus-based research in historical and diachronic linguistics. Gard B. Jenset and Barbara McGillivray argue that, although historical linguistics has been successful in using the comparative method, the field lags behind other branches of linguistics with respect to adopting quantitative methods. Here they provide a theoretically agnostic description of a new framework for quantitatively assessing models and hypotheses in historical linguistics, based on corpus data and using case studies to illustrate how this framework can answer research questions in historical linguistics. The authors offer an in-depth explanation and discussion of the benefits of working with quantitative methods, corpus data, and corpus annotation, and the advantages of open and reproducible research. The book will be a valuable resource for graduate students and researchers in historical linguistics, as well as for all those working with linguistic corpora.

Regression Linear Modeling

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Author : Jason W. Osborne
language : en
Publisher: SAGE Publications
Release Date : 2016-03-24

Regression Linear Modeling written by Jason W. Osborne and has been published by SAGE Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-24 with Psychology categories.

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.

Python Data Analytics And Visualization

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Author : Phuong Vo.T.H
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-03-31

Python Data Analytics And Visualization written by Phuong Vo.T.H and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-03-31 with Computers categories.

Understand, evaluate, and visualize data About This Book Learn basic steps of data analysis and how to use Python and its packages A step-by-step guide to predictive modeling including tips, tricks, and best practices Effectively visualize a broad set of analyzed data and generate effective results Who This Book Is For This book is for Python Developers who are keen to get into data analysis and wish to visualize their analyzed data in a more efficient and insightful manner. What You Will Learn Get acquainted with NumPy and use arrays and array-oriented computing in data analysis Process and analyze data using the time-series capabilities of Pandas Understand the statistical and mathematical concepts behind predictive analytics algorithms Data visualization with Matplotlib Interactive plotting with NumPy, Scipy, and MKL functions Build financial models using Monte-Carlo simulations Create directed graphs and multi-graphs Advanced visualization with D3 In Detail You will start the course with an introduction to the principles of data analysis and supported libraries, along with NumPy basics for statistics and data processing. Next, you will overview the Pandas package and use its powerful features to solve data-processing problems. Moving on, you will get a brief overview of the Matplotlib API .Next, you will learn to manipulate time and data structures, and load and store data in a file or database using Python packages. You will learn how to apply powerful packages in Python to process raw data into pure and helpful data using examples. You will also get a brief overview of machine learning algorithms, that is, applying data analysis results to make decisions or building helpful products such as recommendations and predictions using Scikit-learn. After this, you will move on to a data analytics specialization—predictive analytics. Social media and IOT have resulted in an avalanche of data. You will get started with predictive analytics using Python. You will see how to create predictive models from data. You will get balanced information on statistical and mathematical concepts, and implement them in Python using libraries such as Pandas, scikit-learn, and NumPy. You'll learn more about the best predictive modeling algorithms such as Linear Regression, Decision Tree, and Logistic Regression. Finally, you will master best practices in predictive modeling. After this, you will get all the practical guidance you need to help you on the journey to effective data visualization. Starting with a chapter on data frameworks, which explains the transformation of data into information and eventually knowledge, this path subsequently cover the complete visualization process using the most popular Python libraries with working examples This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products: Getting Started with Python Data Analysis, Phuong Vo.T.H &Martin Czygan Learning Predictive Analytics with Python, Ashish Kumar Mastering Python Data Visualization, Kirthi Raman Style and approach The course acts as a step-by-step guide to get you familiar with data analysis and the libraries supported by Python with the help of real-world examples and datasets. It also helps you gain practical insights into predictive modeling by implementing predictive-analytics algorithms on public datasets with Python. The course offers a wealth of practical guidance to help you on this journey to data visualization

Best Practices In Logistic Regression

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