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Introduction To Statistics Resampling Methods And Microsoft Office Excel


Introduction To Statistics Resampling Methods And Microsoft Office Excel
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Introduction To Statistics Through Resampling Methods And Microsoft Office Excel


Introduction To Statistics Through Resampling Methods And Microsoft Office Excel
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Author : Phillip I. Good
language : en
Publisher: John Wiley & Sons
Release Date : 2005-07-22

Introduction To Statistics Through Resampling Methods And Microsoft Office Excel written by Phillip I. Good 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 2005-07-22 with Mathematics categories.


Learn statistical methods quickly and easily with the discovery method With its emphasis on the discovery method, this publication encourages readers to discover solutions on their own rather than simply copy answers or apply a formula by rote. Readers quickly master and learn to apply statistical methods, such as bootstrap, decision trees, t-test, and permutations to better characterize, report, test, and classify their research findings. In addition to traditional methods, specialized methods are covered, allowing readers to select and apply the most effective method for their research, including: * Tests and estimation procedures for one, two, and multiple samples * Model building * Multivariate analysis * Complex experimental design Throughout the text, Microsoft Office Excel(r) is used to illustrate new concepts and assist readers in completing exercises. An Excel Primer is included as an Appendix for readers who need to learn or brush up on their Excel skills. Written in an informal, highly accessible style, this text is an excellent guide to descriptive statistics, estimation, testing hypotheses, and model building. All the pedagogical tools needed to facilitate quick learning are provided: * More than 100 exercises scattered throughout the text stimulate readers' thinking and actively engage them in applying their newfound skills * Companion FTP site provides access to all data sets discussed in the text * An Instructor's Manual is available upon request from the publisher * Dozens of thought-provoking questions in the final chapter assist readers in applying statistics to solve real-life problems * Helpful appendices include an index to Excel and Excel add-in functions This text serves as an excellent introduction to statistics for students in all disciplines. The accessible style and focus on real-life problem solving are perfectly suited to both students and practitioners.



Introduction To Statistics Through Resampling Methods And R S Plus


Introduction To Statistics Through Resampling Methods And R S Plus
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Author : Phillip I. Good
language : en
Publisher: John Wiley & Sons
Release Date : 2012-01-20

Introduction To Statistics Through Resampling Methods And R S Plus written by Phillip I. Good 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 2012-01-20 with Mathematics categories.


Stimulate learning through discovery With its emphasis on the discovery method, this book allows readers to discover solutions on their own rather than simply copy answers or apply a formula by rote. Readers will quickly master and learn to apply statistical methods, such as bootstrap, decision trees, and permutations, to better characterize, report, test, and classify their research findings. In addition to traditional methods, specialized methods are covered, allowing readers to select and apply the most effective method for their research, including: Tests and estimation procedures for one, two, and multiple samples Model building Multivariate analysis Complex experimental design Throughout the text, the R programming language is used to illustrate new concepts and assist readers in completing exercises. Readers may download the freely available R programming language from the Internet or take advantage of the menu-driven S-PLUS® program. Written in an informal, highly accessible style, this text is an excellent guide to descriptive statistics, estimation, testing hypotheses, and model building. All the pedagogical tools needed to facilitate quick learning are provided: More than two hundred exercises scattered throughout the text stimulate readers' thinking and actively engage them in applying their newfound skills Companion FTP site provides access to all data sets and programs discussed in the text Dozens of thought-provoking questions in the final chapter, Problem Solving, assist readers in applying statistics to address real-life problems Instructor's manual provides answers to exercises Helpful appendices include an introduction to S-PLUS® features This text serves as an excellent introduction to statistics for students in all disciplines. The accessible style and focus on real-life problem solving are perfectly suited for both students and practitioners.



Introduction To Statistics Through Resampling Methods And R


Introduction To Statistics Through Resampling Methods And R
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Author : Phillip I. Good
language : en
Publisher: John Wiley & Sons
Release Date : 2013-02-11

Introduction To Statistics Through Resampling Methods And R written by Phillip I. Good 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 2013-02-11 with Mathematics categories.


A highly accessible alternative approach to basic statistics Praise for the First Edition: "Certainly one of the most impressive little paperback 200-page introductory statistics books that I will ever see . . . it would make a good nightstand book for every statistician."—Technometrics Written in a highly accessible style, Introduction to Statistics through Resampling Methods and R, Second Edition guides students in the understanding of descriptive statistics, estimation, hypothesis testing, and model building. The book emphasizes the discovery method, enabling readers to ascertain solutions on their own rather than simply copy answers or apply a formula by rote. The Second Edition utilizes the R programming language to simplify tedious computations, illustrate new concepts, and assist readers in completing exercises. The text facilitates quick learning through the use of: More than 250 exercises—with selected "hints"—scattered throughout to stimulate readers' thinking and to actively engage them in applying their newfound skills An increased focus on why a method is introduced Multiple explanations of basic concepts Real-life applications in a variety of disciplines Dozens of thought-provoking, problem-solving questions in the final chapter to assist readers in applying statistics to real-life applications Introduction to Statistics through Resampling Methods and R, Second Edition is an excellent resource for students and practitioners in the fields of agriculture, astrophysics, bacteriology, biology, botany, business, climatology, clinical trials, economics, education, epidemiology, genetics, geology, growth processes, hospital administration, law, manufacturing, marketing, medicine, mycology, physics, political science, psychology, social welfare, sports, and toxicology who want to master and learn to apply statistical methods.



Introduction To Statistics Resampling Methods And Microsoft Office Excel


Introduction To Statistics Resampling Methods And Microsoft Office Excel
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Author : Phillip I. Good
language : en
Publisher:
Release Date : 2005

Introduction To Statistics Resampling Methods And Microsoft Office Excel written by Phillip I. Good and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with categories.


Learn statistical methods quickly and easily with the discovery method. With its emphasis on the discovery method, this publication encourages readers to discover solutions on their own rather than simply copy answers or apply a formula by rote. Readers quickly master and learn to apply statistical methods, such as bootstrap, decision trees, t-test, and permutations to better characterize, report, test, and classify their research findings. In addition to traditional methods, specialized methods are covered, allowing readers to select and apply the most effective method for their research, including: tests and estimation procedures for one, two, and multiple samples; model building; multivariate analysis; and complex experimental design. Throughout the text, Microsoft Office Excel(r) is used to illustrate new concepts and assist readers in completing exercises. An Excel Primer is included as an Appendix for readers who need to learn or brush up on their Excel skills. Written in an informal, highly accessible style, this text is an excellent guide to descriptive statistics, estimation, testing hypotheses, and model building. All the pedagogical tools needed to facilitate quick learn



Politics And The Ruling Group In Putin S Russia


Politics And The Ruling Group In Putin S Russia
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Author : S. White
language : en
Publisher: Springer
Release Date : 2008-06-11

Politics And The Ruling Group In Putin S Russia written by S. White and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-06-11 with Political Science categories.


There is little consensus about the nature of the political system that has emerged during the Putin presidency. This collection considers the issues arising in this connection, focusing more closely on institutions such as the presidency and the security police, and on the socioeconomic dimensions of political power.



Data Mining For Business Analytics


Data Mining For Business Analytics
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Author : Galit Shmueli
language : en
Publisher: John Wiley & Sons
Release Date : 2019-10-14

Data Mining For Business Analytics written by Galit Shmueli 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 2019-10-14 with Mathematics categories.


Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities. This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process A new section on ethical issues in data mining Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students More than a dozen case studies demonstrating applications for the data mining techniques described End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. “This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.” —Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R



Handbook Of Motivation At School


Handbook Of Motivation At School
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Author : Kathryn R. Wentzel
language : en
Publisher: Routledge
Release Date : 2009-09-10

Handbook Of Motivation At School written by Kathryn R. Wentzel and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-09-10 with Education categories.


The Handbook of Motivation at School presents the first comprehensive and integrated compilation of theory and research on children’s motivation at school. It covers the major theoretical perspectives in the field as well as their application to instruction, learning, and social adjustment at school. Key Features: Comprehensive – no other book provides such a comprehensive overview of theory and research on children’s motivation at school. Theoretical & Applied – the book provides a review of current motivation theories by the developers of those theories as well as attention to the application of motivation theory and research in classrooms and schools. Chapter Structure – chapters within each section follow a similar structure so that there is uniformity across chapters. Commentaries – each section ends with a commentary that provides clear directions for future research.



Introductory Statistics And Analytics


Introductory Statistics And Analytics
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Author : Peter C. Bruce
language : en
Publisher: John Wiley & Sons
Release Date : 2014-12-03

Introductory Statistics And Analytics written by Peter C. Bruce 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 2014-12-03 with Mathematics categories.


Concise, thoroughly class-tested primer that features basicstatistical concepts in the concepts in the context of analytics,resampling, and the bootstrap A uniquely developed presentation of key statistical topics,Introductory Statistics and Analytics: A ResamplingPerspective provides an accessible approach to statisticalanalytics, resampling, and the bootstrap for readers with variouslevels of exposure to basic probability and statistics. Originallyclass-tested at one of the first online learning companies in thediscipline, www.statistics.com, the book primarily focuses onapplications of statistical concepts developed via resampling, witha background discussion of mathematical theory. This featurestresses statistical literacy and understanding, which demonstratesthe fundamental basis for statistical inference and demystifiestraditional formulas. The book begins with illustrations that have the essentialstatistical topics interwoven throughout before moving on todemonstrate the proper design of studies. Meeting all of theGuidelines for Assessment and Instruction in Statistics Education(GAISE) requirements for an introductory statistics course,Introductory Statistics and Analytics: A ResamplingPerspective also includes: Over 300 “Try It Yourself” exercises andintermittent practice questions, which challenge readers atmultiple levels to investigate and explore key statisticalconcepts Numerous interactive links designed to provide solutions toexercises and further information on crucial concepts Linkages that connect statistics to the rapidly growing fieldof data science Multiple discussions of various software systems, such asMicrosoft Office Excel®, StatCrunch, and R, to develop andanalyze data Areas of concern and/or contrasting points-of-view indicatedthrough the use of “Caution” icons Introductory Statistics and Analytics: A ResamplingPerspective is an excellent primary textbook for courses inpreliminary statistics as well as a supplement for courses inupper-level statistics and related fields, such as biostatisticsand econometrics. The book is also a general reference for readersinterested in revisiting the value of statistics.



Introductory Econometrics


Introductory Econometrics
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Author : Humberto Barreto
language : en
Publisher: Cambridge University Press
Release Date : 2006

Introductory Econometrics written by Humberto Barreto and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006 with Business & Economics categories.


This highly accessible and innovative text with supporting web site uses Excel (R) to teach the core concepts of econometrics without advanced mathematics. It enables students to use Monte Carlo simulations in order to understand the data generating process and sampling distribution. Intelligent repetition of concrete examples effectively conveys the properties of the ordinary least squares (OLS) estimator and the nature of heteroskedasticity and autocorrelation. Coverage includes omitted variables, binary response models, basic time series, and simultaneous equations. The authors teach students how to construct their own real-world data sets drawn from the internet, which they can analyze with Excel (R) or with other econometric software. The accompanying web site with text support can be found at www.wabash.edu/econometrics.



Computer Aided Numerical Methods In Psychology


Computer Aided Numerical Methods In Psychology
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Author : PressGrup Academician Team
language : en
Publisher: Prof. Dr. Bilal Semih Bozdemir
Release Date :

Computer Aided Numerical Methods In Psychology written by PressGrup Academician Team and has been published by Prof. Dr. Bilal Semih Bozdemir this book supported file pdf, txt, epub, kindle and other format this book has been release on with Education categories.


Psychology: Computer-Aided Numerical Methods Introduction to Numerical Methods in Psychology Advantages of Computer-Aided Numerical Analysis Data Collection and Preprocessing Linear Regression and Correlation Analysis Logistic Regression and Classification Principal Component Analysis (PCA) Cluster Analysis Time Series Analysis Bayesian Methods and Inference Monte Carlo Simulation Techniques Optimization Algorithms in Psychological Research Visualization and Interpretation of Results Practical Applications and Case Studies