04821cam a2200517 i 4500 22289359 CSPC 20260214101912.0 211028t20222022enka b 001 0 eng d 2021949628 GBC217614 bnb 020480740 Uk 9780192896599 22289359 (OCoLC)1289734683 YDX eng rda YDX UKUOY OCLCO UKMGB OCLCF OCLCO NRC DLC CSPC lccopycat H62.A5 .B35 2022 300.285555 B171r 23 Baker, Daniel H., author. Research methods using R : advanced data analysis in the behavioural and biological sciences / Daniel H. Baker. Oxford, United Kingdom : Oxford University Press, 2022. xiii, 338 pges : illustrations (some color) ; 25 cm. text rdacontent unmediated rdamedia volume rdacarrier 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. Social sciences Research Statistical methods. Life sciences Research Statistical methods. Research Statistical methods Data processing. R (Computer program language) Sciences sociales Recherche Méthodes statistiques. Recherche Méthodes statistiques Informatique. R (Langage de programmation) Sciences de la vie Recherche Méthodes statistiques. R (Computer program language) fast (OCoLC)fst01086207 Research Statistical methods Data processing. fast (OCoLC)fst01095243 Social sciences Research Statistical methods. fast (OCoLC)fst01122971 7 cbc copycat 2 ncip 20 y-gencatlg ddc BK 23 300.285555 B171r CIR 2022 0 0 0 ddc 0 0 BUHI BUHI CIR 2026-01-08 GAA 0 CIR 300.285555 B171r 2022 001779-BUH 2026-01-08 00:00:00 1-1 2026-01-08 BK 0 0 ddc 0 0 CSPC CSPC CIR 2026-02-13 0 CIR 300.285555 B171r 2022 031257 2026-02-13 17:19:45 1-1 2026-02-13 BK 31399 31399