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9780128207888

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This monograph presents on a total of 283 pages an introduction into the basic concepts of the statistical analysis software R and addresses to readers with no previous knowledge. There are 20 chapters and two appendices in the book which are organized into five principal parts.In the first part of the book, the author introduces in Chapter 1 the basic framework of statistical thinking like the steps of scientific process (generation of hypotheses, data collection and description, statistical inference, theory/decision making). A general overview of the software R is provided in Chapter 2. Aspects concerning data collection are described in part two of the book covering the Chapters 3 to 5. Theoretical concepts on data collection are discussed in Chapter 3 and the implementation using R is provided in Chapter 4 (subsetting data, random numbers and random samples) and Chapter 5 (libraries and loading data into R). Part three of the book is devoted to explorative and descriptive statistics and covers the Chapters 6 and 7. Chapter 6 presents the methods of parameters and statistics for qualitative and quantitative variables and their implementation in R is provided in Chapter 7. Parts four and five (Chapters 8 to 20) focus on statistical inference. After an introduction into the framework of probability (Chapter 8), sample distributions (Chapter 9), hypothesis testing (Chapter 10), central limit theorem (Chapter 11), interval estimates (Chapter 12), hypothesis testing (Chapter 13) and confidence intervals for single parameter (Chapter 14) as well as for two parameters (hypothesis testing in Chapter 15 and confidence intervals in Chapter 16) the transfer of the theoretical concepts in R is described in Chapter 17. Chapter 18 deals with inference for two quantitative variables and simple linear regression is presented in Chapter 19. The fifth part ends with an overview of advanced statistical methods in Chapter 20. The volume ends with an appendix containing the solutions to all self-learning questions and an appendix listing all R example data sets. In summary, the book under review is recommended to interested students with no prior knowledge. Each chapter is enriched with a large number of supportive exercises and control questions supporting self-learning activities. --zbMath/European Mathematical Society and the Heidelberg Academy of Sciences and Humanities

"""This monograph presents on a total of 283 pages an introduction into the basic concepts of the statistical analysis software R and addresses to readers with no previous knowledge. There are 20 chapters and two appendices in the book which are organized into five principal parts.In the first part of the book, the author introduces in Chapter 1 the basic framework of statistical thinking like the steps of scientific process (generation of hypotheses, data collection and description, statistical inference, theory/decision making). A general overview of the software R is provided in Chapter 2. Aspects concerning data collection are described in part two of the book covering the Chapters 3 to 5. Theoretical concepts on data collection are discussed in Chapter 3 and the implementation using R is provided in Chapter 4 (subsetting data, random numbers and random samples) and Chapter 5 (libraries and loading data into R). Part three of the book is devoted to explorative and descriptive statistics and covers the Chapters 6 and 7. Chapter 6 presents the methods of parameters and statistics for qualitative and quantitative variables and their implementation in R is provided in Chapter 7. Parts four and five (Chapters 8 to 20) focus on statistical inference. After an introduction into the framework of probability (Chapter 8), sample distributions (Chapter 9), hypothesis testing (Chapter 10), central limit theorem (Chapter 11), interval estimates (Chapter 12), hypothesis testing (Chapter 13) and confidence intervals for single parameter (Chapter 14) as well as for two parameters (hypothesis testing in Chapter 15 and confidence intervals in Chapter 16) the transfer of the theoretical concepts in R is described in Chapter 17. Chapter 18 deals with inference for two quantitative variables and simple linear regression is presented in Chapter 19. The fifth part ends with an overview of advanced statistical methods in Chapter 20. The volume ends with an appendix containing the solutions to all self-learning questions and an appendix listing all R example data sets. In summary, the book under review is recommended to interested students with no prior knowledge. Each chapter is enriched with a large number of supportive exercises and control questions supporting self-learning activities."" --zbMath/European Mathematical Society and the Heidelberg Academy of Sciences and Humanities"

Author Information

Dr. Stephen Loftus is an Analyst in Research & Development for the Atlanta Braves. Prior to this, he held academic positions at Randolph-Macon College and Sweet Briar College. In his experience in academia and industry, Dr. Loftus has spent a great deal of time studying and developing Bayesian models for a variety of projects. These highly collaborative projects range from analysis in baseball to studies in numerical ecology. In developing these models, he found himself, on many occasions, needing to explain not only the decisions made in making these models, but also the rationale behind the Bayesian philosophy of statistics to individuals with diverse mathematical backgrounds.

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