Practical Guide To Cluster Analysis In R

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Practical Guide to Cluster Analysis in R

Author : Alboukadel Kassambara
Publisher : STHDA
ISBN 13 : 1542462703
Total Pages : 168 pages
Book Rating : 4.5/5 (424 download)

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Book Synopsis Practical Guide to Cluster Analysis in R by : Alboukadel Kassambara

Download or read book Practical Guide to Cluster Analysis in R written by Alboukadel Kassambara and published by STHDA. This book was released on 2017-08-23 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although there are several good books on unsupervised machine learning, we felt that many of them are too theoretical. This book provides practical guide to cluster analysis, elegant visualization and interpretation. It contains 5 parts. Part I provides a quick introduction to R and presents required R packages, as well as, data formats and dissimilarity measures for cluster analysis and visualization. Part II covers partitioning clustering methods, which subdivide the data sets into a set of k groups, where k is the number of groups pre-specified by the analyst. Partitioning clustering approaches include: K-means, K-Medoids (PAM) and CLARA algorithms. In Part III, we consider hierarchical clustering method, which is an alternative approach to partitioning clustering. The result of hierarchical clustering is a tree-based representation of the objects called dendrogram. In this part, we describe how to compute, visualize, interpret and compare dendrograms. Part IV describes clustering validation and evaluation strategies, which consists of measuring the goodness of clustering results. Among the chapters covered here, there are: Assessing clustering tendency, Determining the optimal number of clusters, Cluster validation statistics, Choosing the best clustering algorithms and Computing p-value for hierarchical clustering. Part V presents advanced clustering methods, including: Hierarchical k-means clustering, Fuzzy clustering, Model-based clustering and Density-based clustering.

An Introduction to Clustering with R

Author : Paolo Giordani
Publisher : Springer Nature
ISBN 13 : 9811305536
Total Pages : 340 pages
Book Rating : 4.8/5 (113 download)

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Book Synopsis An Introduction to Clustering with R by : Paolo Giordani

Download or read book An Introduction to Clustering with R written by Paolo Giordani and published by Springer Nature. This book was released on 2020-08-27 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this book is to thoroughly prepare the reader for applied research in clustering. Cluster analysis comprises a class of statistical techniques for classifying multivariate data into groups or clusters based on their similar features. Clustering is nowadays widely used in several domains of research, such as social sciences, psychology, and marketing, highlighting its multidisciplinary nature. This book provides an accessible and comprehensive introduction to clustering and offers practical guidelines for applying clustering tools by carefully chosen real-life datasets and extensive data analyses. The procedures addressed in this book include traditional hard clustering methods and up-to-date developments in soft clustering. Attention is paid to practical examples and applications through the open source statistical software R. Commented R code and output for conducting, step by step, complete cluster analyses are available. The book is intended for researchers interested in applying clustering methods. Basic notions on theoretical issues and on R are provided so that professionals as well as novices with little or no background in the subject will benefit from the book.

Practical Guide To Principal Component Methods in R

Author : Alboukadel KASSAMBARA
Publisher : STHDA
ISBN 13 : 1975721136
Total Pages : 171 pages
Book Rating : 4.9/5 (757 download)

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Book Synopsis Practical Guide To Principal Component Methods in R by : Alboukadel KASSAMBARA

Download or read book Practical Guide To Principal Component Methods in R written by Alboukadel KASSAMBARA and published by STHDA. This book was released on 2017-08-23 with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although there are several good books on principal component methods (PCMs) and related topics, we felt that many of them are either too theoretical or too advanced. This book provides a solid practical guidance to summarize, visualize and interpret the most important information in a large multivariate data sets, using principal component methods in R. The visualization is based on the factoextra R package that we developed for creating easily beautiful ggplot2-based graphs from the output of PCMs. This book contains 4 parts. Part I provides a quick introduction to R and presents the key features of FactoMineR and factoextra. Part II describes classical principal component methods to analyze data sets containing, predominantly, either continuous or categorical variables. These methods include: Principal Component Analysis (PCA, for continuous variables), simple correspondence analysis (CA, for large contingency tables formed by two categorical variables) and Multiple CA (MCA, for a data set with more than 2 categorical variables). In Part III, you'll learn advanced methods for analyzing a data set containing a mix of variables (continuous and categorical) structured or not into groups: Factor Analysis of Mixed Data (FAMD) and Multiple Factor Analysis (MFA). Part IV covers hierarchical clustering on principal components (HCPC), which is useful for performing clustering with a data set containing only categorical variables or with a mixed data of categorical and continuous variables.

Bayesian Statistics the Fun Way

Author : Will Kurt
Publisher : No Starch Press
ISBN 13 : 1593279566
Total Pages : 258 pages
Book Rating : 4.5/5 (932 download)

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Book Synopsis Bayesian Statistics the Fun Way by : Will Kurt

Download or read book Bayesian Statistics the Fun Way written by Will Kurt and published by No Starch Press. This book was released on 2019-07-09 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fun guide to learning Bayesian statistics and probability through unusual and illustrative examples. Probability and statistics are increasingly important in a huge range of professions. But many people use data in ways they don't even understand, meaning they aren't getting the most from it. Bayesian Statistics the Fun Way will change that. This book will give you a complete understanding of Bayesian statistics through simple explanations and un-boring examples. Find out the probability of UFOs landing in your garden, how likely Han Solo is to survive a flight through an asteroid shower, how to win an argument about conspiracy theories, and whether a burglary really was a burglary, to name a few examples. By using these off-the-beaten-track examples, the author actually makes learning statistics fun. And you'll learn real skills, like how to: - How to measure your own level of uncertainty in a conclusion or belief - Calculate Bayes theorem and understand what it's useful for - Find the posterior, likelihood, and prior to check the accuracy of your conclusions - Calculate distributions to see the range of your data - Compare hypotheses and draw reliable conclusions from them Next time you find yourself with a sheaf of survey results and no idea what to do with them, turn to Bayesian Statistics the Fun Way to get the most value from your data.

R in Action

Author : Robert Kabacoff
Publisher : Manning Publications
ISBN 13 : 9781617291388
Total Pages : 475 pages
Book Rating : 4.2/5 (913 download)

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Book Synopsis R in Action by : Robert Kabacoff

Download or read book R in Action written by Robert Kabacoff and published by Manning Publications. This book was released on 2015-03-03 with total page 475 pages. Available in PDF, EPUB and Kindle. Book excerpt: R is a powerful language for statistical computing and graphics that can handle virtually any data-crunching task. It runs on all important platforms and provides thousands of useful specialized modules and utilities. This makes R a great way to get meaningful information from mountains of raw data. R in Action, Second Edition is a language tutorial focused on practical problems. Written by a research methodologist, it takes a direct and modular approach to quickly give readers the information they need to produce useful results. Focusing on realistic data analyses and a comprehensive integration of graphics, it follows the steps that real data analysts use to acquire their data, get it into shape, analyze it, and produce meaningful results that they can provide to clients. Purchase of the print book comes with an offer of a free PDF eBook from Manning. Also available is all code from the book.

Machine Learning Essentials

Author : Alboukadel Kassambara
Publisher : STHDA
ISBN 13 : 1986406857
Total Pages : 211 pages
Book Rating : 4.9/5 (864 download)

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Book Synopsis Machine Learning Essentials by : Alboukadel Kassambara

Download or read book Machine Learning Essentials written by Alboukadel Kassambara and published by STHDA. This book was released on 2018-03-10 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discovering knowledge from big multivariate data, recorded every days, requires specialized machine learning techniques. This book presents an easy to use practical guide in R to compute the most popular machine learning methods for exploring real word data sets, as well as, for building predictive models. The main parts of the book include: A) Unsupervised learning methods, to explore and discover knowledge from a large multivariate data set using clustering and principal component methods. You will learn hierarchical clustering, k-means, principal component analysis and correspondence analysis methods. B) Regression analysis, to predict a quantitative outcome value using linear regression and non-linear regression strategies. C) Classification techniques, to predict a qualitative outcome value using logistic regression, discriminant analysis, naive bayes classifier and support vector machines. D) Advanced machine learning methods, to build robust regression and classification models using k-nearest neighbors methods, decision tree models, ensemble methods (bagging, random forest and boosting). E) Model selection methods, to select automatically the best combination of predictor variables for building an optimal predictive model. These include, best subsets selection methods, stepwise regression and penalized regression (ridge, lasso and elastic net regression models). We also present principal component-based regression methods, which are useful when the data contain multiple correlated predictor variables. F) Model validation and evaluation techniques for measuring the performance of a predictive model. G) Model diagnostics for detecting and fixing a potential problems in a predictive model. The book presents the basic principles of these tasks and provide many examples in R. This book offers solid guidance in data mining for students and researchers. Key features: - Covers machine learning algorithm and implementation - Key mathematical concepts are presented - Short, self-contained chapters with practical examples.

R for Political Data Science

Author : Francisco Urdinez
Publisher : CRC Press
ISBN 13 : 1000204510
Total Pages : 469 pages
Book Rating : 4.0/5 (2 download)

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Book Synopsis R for Political Data Science by : Francisco Urdinez

Download or read book R for Political Data Science written by Francisco Urdinez and published by CRC Press. This book was released on 2020-11-18 with total page 469 pages. Available in PDF, EPUB and Kindle. Book excerpt: R for Political Data Science: A Practical Guide is a handbook for political scientists new to R who want to learn the most useful and common ways to interpret and analyze political data. It was written by political scientists, thinking about the many real-world problems faced in their work. The book has 16 chapters and is organized in three sections. The first, on the use of R, is for those users who are learning R or are migrating from another software. The second section, on econometric models, covers OLS, binary and survival models, panel data, and causal inference. The third section is a data science toolbox of some the most useful tools in the discipline: data imputation, fuzzy merge of large datasets, web mining, quantitative text analysis, network analysis, mapping, spatial cluster analysis, and principal component analysis. Key features: Each chapter has the most up-to-date and simple option available for each task, assuming minimal prerequisites and no previous experience in R Makes extensive use of the Tidyverse, the group of packages that has revolutionized the use of R Provides a step-by-step guide that you can replicate using your own data Includes exercises in every chapter for course use or self-study Focuses on practical-based approaches to statistical inference rather than mathematical formulae Supplemented by an R package, including all data As the title suggests, this book is highly applied in nature, and is designed as a toolbox for the reader. It can be used in methods and data science courses, at both the undergraduate and graduate levels. It will be equally useful for a university student pursuing a PhD, political consultants, or a public official, all of whom need to transform their datasets into substantive and easily interpretable conclusions.

Practical Machine Learning in R

Author : Fred Nwanganga
Publisher : John Wiley & Sons
ISBN 13 : 1119591511
Total Pages : 464 pages
Book Rating : 4.1/5 (195 download)

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Book Synopsis Practical Machine Learning in R by : Fred Nwanganga

Download or read book Practical Machine Learning in R written by Fred Nwanganga and published by John Wiley & Sons. This book was released on 2020-05-27 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: Guides professionals and students through the rapidly growing field of machine learning with hands-on examples in the popular R programming language Machine learning—a branch of Artificial Intelligence (AI) which enables computers to improve their results and learn new approaches without explicit instructions—allows organizations to reveal patterns in their data and incorporate predictive analytics into their decision-making process. Practical Machine Learning in R provides a hands-on approach to solving business problems with intelligent, self-learning computer algorithms. Bestselling author and data analytics experts Fred Nwanganga and Mike Chapple explain what machine learning is, demonstrate its organizational benefits, and provide hands-on examples created in the R programming language. A perfect guide for professional self-taught learners or students in an introductory machine learning course, this reader-friendly book illustrates the numerous real-world business uses of machine learning approaches. Clear and detailed chapters cover data wrangling, R programming with the popular RStudio tool, classification and regression techniques, performance evaluation, and more. Explores data management techniques, including data collection, exploration and dimensionality reduction Covers unsupervised learning, where readers identify and summarize patterns using approaches such as apriori, eclat and clustering Describes the principles behind the Nearest Neighbor, Decision Tree and Naive Bayes classification techniques Explains how to evaluate and choose the right model, as well as how to improve model performance using ensemble methods such as Random Forest and XGBoost Practical Machine Learning in R is a must-have guide for business analysts, data scientists, and other professionals interested in leveraging the power of AI to solve business problems, as well as students and independent learners seeking to enter the field.

Cluster Analysis for Applications

Author : Michael R. Anderberg
Publisher : Academic Press
ISBN 13 : 1483191397
Total Pages : 376 pages
Book Rating : 4.4/5 (831 download)

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Book Synopsis Cluster Analysis for Applications by : Michael R. Anderberg

Download or read book Cluster Analysis for Applications written by Michael R. Anderberg and published by Academic Press. This book was released on 2014-05-10 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cluster Analysis for Applications deals with methods and various applications of cluster analysis. Topics covered range from variables and scales to measures of association among variables and among data units. Conceptual problems in cluster analysis are discussed, along with hierarchical and non-hierarchical clustering methods. The necessary elements of data analysis, statistics, cluster analysis, and computer implementation are integrated vertically to cover the complete path from raw data to a finished analysis. Comprised of 10 chapters, this book begins with an introduction to the subject of cluster analysis and its uses as well as category sorting problems and the need for cluster analysis algorithms. The next three chapters give a detailed account of variables and association measures, with emphasis on strategies for dealing with problems containing variables of mixed types. Subsequent chapters focus on the central techniques of cluster analysis with particular reference to computational considerations; interpretation of clustering results; and techniques and strategies for making the most effective use of cluster analysis. The final chapter suggests an approach for the evaluation of alternative clustering methods. The presentation is capped with a complete set of implementing computer programs listed in the Appendices to make the use of cluster analysis as painless and free of mechanical error as is possible. This monograph is intended for students and workers who have encountered the notion of cluster analysis.

R Graphics Essentials for Great Data Visualization

Author : Alboukadel Kassambara
Publisher : STHDA
ISBN 13 : 1979748101
Total Pages : 155 pages
Book Rating : 4.9/5 (797 download)

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Book Synopsis R Graphics Essentials for Great Data Visualization by : Alboukadel Kassambara

Download or read book R Graphics Essentials for Great Data Visualization written by Alboukadel Kassambara and published by STHDA. This book was released on 2017-11-14 with total page 155 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data visualization is one of the most important part of data science. Many books and courses present a catalogue of graphics but they don't teach you which charts to use according to the type of the data. In this book, we start by presenting the key graphic systems and packages available in R, including R base graphs, lattice and ggplot2 plotting systems. Next, we provide more than 200 practical examples to create great graphics for the right data using either the ggplot2 package and extensions or the traditional R graphics. With this book, you 'll learn: - How to quickly create beautiful graphics using ggplot2 packages - How to properly customize and annotate the plots - Type of graphics for visualizing categorical and continuous variables - How to add automatically p-values to box plots, bar plots and alternatives - How to add marginal density plots and correlation coefficients to scatter plots - Key methods for analyzing and visualizing multivariate data - R functions and packages for plotting time series data - How to combine multiple plots on one page to create production-quality figures.

Numerical Ecology with R

Author : Daniel Borcard
Publisher : Springer
ISBN 13 : 331971404X
Total Pages : 444 pages
Book Rating : 4.3/5 (197 download)

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Book Synopsis Numerical Ecology with R by : Daniel Borcard

Download or read book Numerical Ecology with R written by Daniel Borcard and published by Springer. This book was released on 2018-03-19 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: This new edition of Numerical Ecology with R guides readers through an applied exploration of the major methods of multivariate data analysis, as seen through the eyes of three ecologists. It provides a bridge between a textbook of numerical ecology and the implementation of this discipline in the R language. The book begins by examining some exploratory approaches. It proceeds logically with the construction of the key building blocks of most methods, i.e. association measures and matrices, and then submits example data to three families of approaches: clustering, ordination and canonical ordination. The last two chapters make use of these methods to explore important and contemporary issues in ecology: the analysis of spatial structures and of community diversity. The aims of methods thus range from descriptive to explanatory and predictive and encompass a wide variety of approaches that should provide readers with an extensive toolbox that can address a wide palette of questions arising in contemporary multivariate ecological analysis. The second edition of this book features a complete revision to the R code and offers improved procedures and more diverse applications of the major methods. It also highlights important changes in the methods and expands upon topics such as multiple correspondence analysis, principal response curves and co-correspondence analysis. New features include the study of relationships between species traits and the environment, and community diversity analysis. This book is aimed at professional researchers, practitioners, graduate students and teachers in ecology, environmental science and engineering, and in related fields such as oceanography, molecular ecology, agriculture and soil science, who already have a background in general and multivariate statistics and wish to apply this knowledge to their data using the R language, as well as people willing to accompany their disciplinary learning with practical applications. People from other fields (e.g. geology, geography, paleoecology, phylogenetics, anthropology, the social and education sciences, etc.) may also benefit from the materials presented in this book. Users are invited to use this book as a teaching companion at the computer. All the necessary data files, the scripts used in the chapters, as well as extra R functions and packages written by the authors of the book, are available online (URL: http://adn.biol.umontreal.ca/~numericalecology/numecolR/).

Handbook of Cluster Analysis

Author : Christian Hennig
Publisher : CRC Press
ISBN 13 : 1466551895
Total Pages : 753 pages
Book Rating : 4.4/5 (665 download)

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Book Synopsis Handbook of Cluster Analysis by : Christian Hennig

Download or read book Handbook of Cluster Analysis written by Christian Hennig and published by CRC Press. This book was released on 2015-12-16 with total page 753 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Cluster Analysis provides a comprehensive and unified account of the main research developments in cluster analysis. Written by active, distinguished researchers in this area, the book helps readers make informed choices of the most suitable clustering approach for their problem and make better use of existing cluster analysis tools.The

Conducting Personal Network Research

Author : Christopher McCarty
Publisher : Guilford Publications
ISBN 13 : 1462538436
Total Pages : 293 pages
Book Rating : 4.4/5 (625 download)

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Book Synopsis Conducting Personal Network Research by : Christopher McCarty

Download or read book Conducting Personal Network Research written by Christopher McCarty and published by Guilford Publications. This book was released on 2019-02-22 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written at an introductory level, and featuring engaging case examples, this book reviews the theory and practice of personal and egocentric network research. This approach offers powerful tools for capturing the impact of overlapping, changing social relationships and contexts on individuals' attitudes and behavior. The authors provide solid guidance on the formulation of research questions; research design; data collection, including decisions about survey modes and sampling frames; the measurement of network composition and structure, including the use of name generators; and statistical modeling, from basic regression techniques to more advanced multilevel and dynamic models. Ethical issues in personal network research are addressed. User-friendly features include boxes on major published studies, end-of-chapter suggestions for further reading, and an appendix describing the main software programs used in the field.

Statistics for Linguistics with R

Author : Stefan Th. Gries
Publisher : Walter de Gruyter
ISBN 13 : 3110216043
Total Pages : 346 pages
Book Rating : 4.1/5 (12 download)

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Book Synopsis Statistics for Linguistics with R by : Stefan Th. Gries

Download or read book Statistics for Linguistics with R written by Stefan Th. Gries and published by Walter de Gruyter. This book was released on 2009-12-15 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is an introduction to statistics for linguists using the open source software R. It is aimed at students and instructors/professors with little or no statistical background and is written in a non-technical and reader-friendly/accessible style. It first introduces in detail the overall logic underlying quantitative studies: exploration, hypothesis formulation and operationalization, and the notion and meaning of significance tests. It then introduces some basics of the software R relevant to statistical data analysis. A chapter on descriptive statistics explains how summary statistics for frequencies, averages, and correlations are generated with R and how they are graphically represented best. A chapter on analytical statistics explains how statistical tests are performed in R on the basis of many different linguistic case studies: For nearly every single example, it is explained what the structure of the test looks like, how hypotheses are formulated, explored, and tested for statistical significance, how the results are graphically represented, and how one would summarize them in a paper/article. A chapter on selected multifactorial methods introduces how more complex research designs can be studied: methods for the study of multifactorial frequency data, correlations, tests for means, and binary response data are discussed and exemplified step-by-step. Also, the exploratory approach of hierarchical cluster analysis is illustrated in detail. The book comes with many exercises, boxes with short think breaks and warnings, recommendations for further study, and answer keys as well as a statistics for linguists newsgroup on the companion website. The volume is aimed at beginners on every level of linguistic education: undergraduate students, graduate students, and instructors/professors and can be used in any research methods and statistics class for linguists. It presupposes no quantitative/statistical knowledge whatsoever and, unlike most competing books, begins at step 1 for every method and explains everything explicitly.

Complex Surveys

Author : Thomas Lumley
Publisher : John Wiley & Sons
ISBN 13 : 111821093X
Total Pages : 329 pages
Book Rating : 4.1/5 (182 download)

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Book Synopsis Complex Surveys by : Thomas Lumley

Download or read book Complex Surveys written by Thomas Lumley and published by John Wiley & Sons. This book was released on 2011-09-20 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: A complete guide to carrying out complex survey analysis using R As survey analysis continues to serve as a core component of sociological research, researchers are increasingly relying upon data gathered from complex surveys to carry out traditional analyses. Complex Surveys is a practical guide to the analysis of this kind of data using R, the freely available and downloadable statistical programming language. As creator of the specific survey package for R, the author provides the ultimate presentation of how to successfully use the software for analyzing data from complex surveys while also utilizing the most current data from health and social sciences studies to demonstrate the application of survey research methods in these fields. The book begins with coverage of basic tools and topics within survey analysis such as simple and stratified sampling, cluster sampling, linear regression, and categorical data regression. Subsequent chapters delve into more technical aspects of complex survey analysis, including post-stratification, two-phase sampling, missing data, and causal inference. Throughout the book, an emphasis is placed on graphics, regression modeling, and two-phase designs. In addition, the author supplies a unique discussion of epidemiological two-phase designs as well as probability-weighting for causal inference. All of the book's examples and figures are generated using R, and a related Web site provides the R code that allows readers to reproduce the presented content. Each chapter concludes with exercises that vary in level of complexity, and detailed appendices outline additional mathematical and computational descriptions to assist readers with comparing results from various software systems. Complex Surveys is an excellent book for courses on sampling and complex surveys at the upper-undergraduate and graduate levels. It is also a practical reference guide for applied statisticians and practitioners in the social and health sciences who use statistics in their everyday work.

Practical Graph Mining with R

Author : Nagiza F. Samatova
Publisher : CRC Press
ISBN 13 : 1439860858
Total Pages : 495 pages
Book Rating : 4.4/5 (398 download)

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Book Synopsis Practical Graph Mining with R by : Nagiza F. Samatova

Download or read book Practical Graph Mining with R written by Nagiza F. Samatova and published by CRC Press. This book was released on 2013-07-15 with total page 495 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover Novel and Insightful Knowledge from Data Represented as a GraphPractical Graph Mining with R presents a "do-it-yourself" approach to extracting interesting patterns from graph data. It covers many basic and advanced techniques for the identification of anomalous or frequently recurring patterns in a graph, the discovery of groups or cluste

Practical Statistics in R for Comparing Groups

Author : Alboukadel Kassambara
Publisher :
ISBN 13 : 9781712330883
Total Pages : 206 pages
Book Rating : 4.3/5 (38 download)

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Book Synopsis Practical Statistics in R for Comparing Groups by : Alboukadel Kassambara

Download or read book Practical Statistics in R for Comparing Groups written by Alboukadel Kassambara and published by . This book was released on 2019-11-28 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: This R Statistics book provides a solid step-by-step practical guide to statistical inference for comparing groups means using the R software. Additionally, we developed an R package named rstatix, which provides a simple and intuitive pipe-friendly framework, coherent with the `tidyverse` design philosophy, for computing the most common R statistical analyses, including t-test, Wilcoxon test, ANOVA, Kruskal-Wallis and correlation analyses, outliers identification and more. This book is designed to get you doing the statistical tests in R as quick as possible. The book focuses on implementation and understanding of the methods, without having to struggle through pages of mathematical proofs. You will be guided through the steps of summarizing and visualizing the data, checking the assumptions and performing statistical tests in R, interpreting and reporting the results. The main parts of the book include: PART I. Statistical tests and assumptions for the comparison of groups means; PART II. comparing two means (t-test, Wilcoxon test, Sign test); PART III. comparing multiple means (ANOVA - Analysis of Variance for independent measures, repeated measures ANOVA, mixed ANOVA, ANCOVA and MANOVA, Kruskal-Wallis test and Friedman test).