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Research methods using R : advanced data analysis in the behavioural and biological sciences / Daniel H. Baker.

By: Material type: TextTextPublisher: Oxford, United Kingdom : Oxford University Press, 2022Description: xiii, 338 pges : illustrations (some color) ; 25 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9780192896599
Subject(s): DDC classification:
  • 300.285555 B171r 23
LOC classification:
  • H62.A5 .B35 2022
Contents:
Introduction -- Introduction to the R environment -- Cleaning and preparing data for analysis -- Statistical tests as linear models P-- Power analysis -- Meta-analysis -- Mixed-effects models -- Stochastic methods -- Non-linear curve fitting -- Fourier analysis -- Multivariate t-tests -- Structural equation modeling -- Multidimensional scaling and k-means clustering -- Multivariate pattern analysis -- Correcting for multiple comparison -- Signal detection theory -- Bayesian statistics -- Plotting graphs and data visualization -- Reproducible data analysis.
Summary: "Providing complete coverage of advanced research methods for undergraduates, Daniel H. Baker supports students in their mastery of more advanced research methods and their application in R. This brand new title brings together coverage of a variety of topics for readers with basic statistical knowledge. It begins with material on the fundamental tools - nonlinear curve fitting and function optimization, stochastic methods, and Fourier (frequency) analysis - before leading readers on to more specialist content - bivariate and multivariate statistics, Bayesian statistics, and machine learning methods. Several chapters also discuss methods that can be used to improve research practises, including power analysis, meta-analysis, reproducible data analysis. Written to build a student's confidence with using R in a step-by-step way, early chapters present the essentials, ensuring that the content is accessible to those that have never programmed before. By giving them a feel for how the software works in practice, students are gradually introduced to simple examples of techniques before building up to more detailed implementations demonstrated in worked examples. Readers are also presented with opportunities to try analysis techniques for themselves. Practice questions are presented at the end of each chapter with answer guidance supplied in the book, while multiple-choice-questions with instant feedback can be accessed online. The author also provides datasets online which students can use to practise their new skills. Digital formats and resources This book is available for students and institutions to purchase in a variety of formats, and is supported by online resources. - The e-book offers a mobile experience and convenient access along with functionality, navigation features, and links that offer extra learning support. This book is accompanied by online resources including multiple-choice-questions with instant feedback, example code, and data files allowing students to run examples independently"-- Amazon.com.
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Holdings
Item type Current library Shelving location Call number Copy number Status Date due Barcode
Books Books CAMPUS CSPC Circulation Section CIR 300.285555 B171r 2022 (Browse shelf(Opens below)) 1-1 Available 001779-BUH
Books Books Main Library Circulation Section CIR 300.285555 B171r 2022 (Browse shelf(Opens below)) 1-1 Available 031257

Includes bibliographical references and index.

Introduction -- Introduction to the R environment -- Cleaning and preparing data for analysis -- Statistical tests as linear models P-- Power analysis -- Meta-analysis -- Mixed-effects models -- Stochastic methods -- Non-linear curve fitting -- Fourier analysis -- Multivariate t-tests -- Structural equation modeling -- Multidimensional scaling and k-means clustering -- Multivariate pattern analysis -- Correcting for multiple comparison -- Signal detection theory -- Bayesian statistics -- Plotting graphs and data visualization -- Reproducible data analysis.

"Providing complete coverage of advanced research methods for undergraduates, Daniel H. Baker supports students in their mastery of more advanced research methods and their application in R. This brand new title brings together coverage of a variety of topics for readers with basic statistical knowledge. It begins with material on the fundamental tools - nonlinear curve fitting and function optimization, stochastic methods, and Fourier (frequency) analysis - before leading readers on to more specialist content - bivariate and multivariate statistics, Bayesian statistics, and machine learning methods. Several chapters also discuss methods that can be used to improve research practises, including power analysis, meta-analysis, reproducible data analysis. Written to build a student's confidence with using R in a step-by-step way, early chapters present the essentials, ensuring that the content is accessible to those that have never programmed before. By giving them a feel for how the software works in practice, students are gradually introduced to simple examples of techniques before building up to more detailed implementations demonstrated in worked examples. Readers are also presented with opportunities to try analysis techniques for themselves. Practice questions are presented at the end of each chapter with answer guidance supplied in the book, while multiple-choice-questions with instant feedback can be accessed online. The author also provides datasets online which students can use to practise their new skills. Digital formats and resources This book is available for students and institutions to purchase in a variety of formats, and is supported by online resources. - The e-book offers a mobile experience and convenient access along with functionality, navigation features, and links that offer extra learning support. This book is accompanied by online resources including multiple-choice-questions with instant feedback, example code, and data files allowing students to run examples independently"-- Amazon.com.

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