Statistics (the Easier Way) with R, 3rd Ed

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Statistics (the Easier Way) with R, 3rd Ed

Statistics (the Easier Way) with R, 3rd Ed

An Informal Text on Applied Statistics and Data Science

  • Author: N. M. Radziwill,Morgan Benton
  • Publisher: Lapis Lucera
  • ISBN: 9780996916035
  • Category:
  • Page: 540
  • View: 2411
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Covers exploratory data analysis using R, ggplot, and the tidyverse, with 10 parametric statistical inference tests, and examples using a 7 Step method blending estimation, analytical and computational solutions.

Statistics (the Easier Way) with R

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Statistics (the Easier Way) with R

Statistics (the Easier Way) with R

An Informal Text on Applied Statistics

  • Author: Nicole M. Radziwill
  • Publisher: N.A
  • ISBN: 9780692339428
  • Category: Regression analysis
  • Page: 536
  • View: 8228
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"Designed for beginning and intermediate data scientists, graduate students starting research, undergraduate students taking a first or second applied statistics class, quality improvement professionals, and consultants, this unique book provides an integrated treatment of statistical inference techniques in data analysis. Each example is solved analytically (using equations), and then also in the R software so that readers can see exactly how the computations are performed. Each technique is framed within an easy-to-apply 12-step methodology that will make planning and presenting research a breeze. If you're new to statistics, data science, or R, this book will help get you started. If you have some experience already, this book will make you more productive and enhance your understanding of foundational statistical concepts."--Back cover

Statistics (The Easier Way) With R

This ebook list for those who looking for to read Statistics (The Easier Way) With R, you can read or download in PDF, ePub or Mobi. May some of ebooks not available on your country and only available for those who subscribe and depend to the source of library websites.

Statistics (The Easier Way) With R

Statistics (The Easier Way) With R

An Informal Text on Applied Statistics and Data Science

  • Author: N. M. Radziwill
  • Publisher: Nicole M Radziwill
  • ISBN: 9780996916059
  • Category: Mathematics
  • Page: 538
  • View: 9271
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Covers exploratory data analysis, 10 parametric statistical inference tests, and examples using a 7 Step method that blends estimation, analytical solutions, and computational solutions.

Statistical Analysis with R For Dummies

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Statistical Analysis with R For Dummies

Statistical Analysis with R For Dummies

  • Author: Joseph Schmuller
  • Publisher: John Wiley & Sons
  • ISBN: 1119337267
  • Category: Computers
  • Page: 456
  • View: 7284
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Understanding the world of R programming and analysis has never been easier Most guides to R, whether books or online, focus on R functions and procedures. But now, thanks to Statistical Analysis with R For Dummies, you have access to a trusted, easy-to-follow guide that focuses on the foundational statistical concepts that R addresses—as well as step-by-step guidance that shows you exactly how to implement them using R programming. People are becoming more aware of R every day as major institutions are adopting it as a standard. Part of its appeal is that it's a free tool that's taking the place of costly statistical software packages that sometimes take an inordinate amount of time to learn. Plus, R enables a user to carry out complex statistical analyses by simply entering a few commands, making sophisticated analyses available and understandable to a wide audience. Statistical Analysis with R For Dummies enables you to perform these analyses and to fully understand their implications and results. Gets you up to speed on the #1 analytics/data science software tool Demonstrates how to easily find, download, and use cutting-edge community-reviewed methods in statistics and predictive modeling Shows you how R offers intel from leading researchers in data science, free of charge Provides information on using R Studio to work with R Get ready to use R to crunch and analyze your data—the fast and easy way!

Learning Statistics with R

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Learning Statistics with R

Learning Statistics with R

  • Author: Daniel Navarro
  • Publisher: Lulu.com
  • ISBN: 1326189727
  • Category:
  • Page: N.A
  • View: 2258
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Statistics with R

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Statistics with R

Statistics with R

A Beginner's Guide

  • Author: Robert Stinerock
  • Publisher: SAGE
  • ISBN: 1526421496
  • Category: Computers
  • Page: 392
  • View: 5349
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The dynamic, student focused textbook provides step-by-step instruction in the use of R and of statistical language as a general research tool. It is ideal for anyone hoping to: Complete an introductory course in statistics Prepare for more advanced statistical courses Gain the transferable analytical skills needed to interpret research from across the social sciences Learn the technical skills needed to present data visually Acquire a basic competence in the use of R. The book provides readers with the conceptual foundation to use applied statistical methods in everyday research. Each statistical method is developed within the context of practical, real-world examples and is supported by carefully developed pedagogy and jargon-free definitions. Theory is introduced as an accessible and adaptable tool and is always contextualized within the pragmatic context of real research projects and definable research questions.

Mathematical Statistics with Resampling and R

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Mathematical Statistics with Resampling and R

Mathematical Statistics with Resampling and R

  • Author: Laura M. Chihara,Tim C. Hesterberg
  • Publisher: John Wiley & Sons
  • ISBN: 1119416523
  • Category: Mathematics
  • Page: 560
  • View: 9770
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This thoroughly updated second edition combines the latest software applications with the benefits of modern resampling techniques Resampling helps students understand the meaning of sampling distributions, sampling variability, P-values, hypothesis tests, and confidence intervals. The second edition of Mathematical Statistics with Resampling and R combines modern resampling techniques and mathematical statistics. This book has been classroom-tested to ensure an accessible presentation, uses the powerful and flexible computer language R for data analysis and explores the benefits of modern resampling techniques. This book offers an introduction to permutation tests and bootstrap methods that can serve to motivate classical inference methods. The book strikes a balance between theory, computing, and applications, and the new edition explores additional topics including consulting, paired t test, ANOVA and Google Interview Questions. Throughout the book, new and updated case studies are included representing a diverse range of subjects such as flight delays, birth weights of babies, and telephone company repair times. These illustrate the relevance of the real-world applications of the material. This new edition: • Puts the focus on statistical consulting that emphasizes giving a client an understanding of data and goes beyond typical expectations • Presents new material on topics such as the paired t test, Fisher's Exact Test and the EM algorithm • Offers a new section on "Google Interview Questions" that illustrates statistical thinking • Provides a new chapter on ANOVA • Contains more exercises and updated case studies, data sets, and R code Written for undergraduate students in a mathematical statistics course as well as practitioners and researchers, the second edition of Mathematical Statistics with Resampling and R presents a revised and updated guide for applying the most current resampling techniques to mathematical statistics.

The New Statistics with R

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The New Statistics with R

The New Statistics with R

An Introduction for Biologists

  • Author: Andy Hector
  • Publisher: OUP Oxford
  • ISBN: 0191045268
  • Category: Science
  • Page: 208
  • View: 9747
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Statistical methods are a key tool for all scientists working with data, but learning the basic mathematical skills can be one of the most challenging components of a biologist's training. This accessible book provides a contemporary introduction to the classical techniques and modern extensions of linear model analysis: one of the most useful approaches in the analysis of scientific data in the life and environmental sciences. It emphasizes an estimation-based approach that accounts for recent criticisms of the over-use of probability values, and introduces alternative approaches using information criteria. Statistics are introduced through worked analyses performed in R, the free open source programming language for statistics and graphics, which is rapidly becoming the standard software in many areas of science and technology. These analyses use real data sets from ecology, evolutionary biology and environmental science, and the data sets and R scripts are available as support material. The book's structure and user friendly style stem from the author's 20 years of experience teaching statistics to life and environmental scientists at both the undergraduate and graduate levels. The New Statistics with R is suitable for senior undergraduate and graduate students, professional researchers, and practitioners in the fields of ecology, evolution, environmental studies, and computational biology. Supporting material for the book is available at the author's website: www.plantecol.org/contemporary-analysis-for-ecology/

Simulation for Data Science with R

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Simulation for Data Science with R

Simulation for Data Science with R

  • Author: Matthias Templ
  • Publisher: Packt Publishing
  • ISBN: 9781785881169
  • Category: Computers
  • Page: 398
  • View: 2394
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Data Analysis with R

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Data Analysis with R

Data Analysis with R

  • Author: Tony Fischetti
  • Publisher: Packt Publishing Ltd
  • ISBN: 1785286447
  • Category: Computers
  • Page: 388
  • View: 7403
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Load, wrangle, and analyze your data using the world's most powerful statistical programming language About This Book Load, manipulate and analyze data from different sources Gain a deeper understanding of fundamentals of applied statistics A practical guide to performing data analysis in practice Who This Book Is For Whether you are learning data analysis for the first time, or you want to deepen the understanding you already have, this book will prove to an invaluable resource. If you are looking for a book to bring you all the way through the fundamentals to the application of advanced and effective analytics methodologies, and have some prior programming experience and a mathematical background, then this is for you. What You Will Learn Navigate the R environment Describe and visualize the behavior of data and relationships between data Gain a thorough understanding of statistical reasoning and sampling Employ hypothesis tests to draw inferences from your data Learn Bayesian methods for estimating parameters Perform regression to predict continuous variables Apply powerful classification methods to predict categorical data Handle missing data gracefully using multiple imputation Identify and manage problematic data points Employ parallelization and Rcpp to scale your analyses to larger data Put best practices into effect to make your job easier and facilitate reproducibility In Detail Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. With over 7,000 user contributed packages, it's easy to find support for the latest and greatest algorithms and techniques. Starting with the basics of R and statistical reasoning, Data Analysis with R dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples. Packed with engaging problems and exercises, this book begins with a review of R and its syntax. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with “messy data”, large data, communicating results, and facilitating reproducibility. This book is engineered to be an invaluable resource through many stages of anyone's career as a data analyst. Style and approach Learn data analysis using engaging examples and fun exercises, and with a gentle and friendly but comprehensive "learn-by-doing" approach.