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3 produkter
3 produkter
Häftad, Engelska, 2026
1 023 kr
Skickas inom 10-15 vardagar
Implementing R for Statistics provides comprehensive coverage of basic statistical concepts using this important open-source programming language tool, from installing R and RStudio, to exploring its basic structure and uses, to extending some core functions such as vectors, basic mathematical operations, and data frames. The book will help readers understand the latest advances in the R programming language, as R allows for sophisticated and elegant data visualization. Illustrated examples are an integral part of the text, carefully designed to apply the core principles illustrated in the text to emerging topics in the field.The text also focuses on exploiting the flexible and user-friendly nature of R. Basic concepts and recent advances in the field, including understanding the R basics, as well as implementing and practicing them in statistics, are also covered. This first edition is an essential text for students, lecturers, data scientists, and applied researchers in all areas of statistics, as well as in related fields such as biostatistics, health care, finance, risk management, social sciences, market research, and environmental and climate research.Introduces core statistics concepts and how to apply them using R in an accessible mannerExplains to readers the newest advances in the area of implementing R in statistics through clear and meticulously crafted examplesExplores the process of learning R from installation to package creation, employing clear steps and illustrated examplesCovers important and evolving aspects of R, such as using RStudio, managing metadata, advanced regression modeling techniques, and creating packagesIncludes updated package references to the latest R packages, incorporating up-to-date tools available in the field
E-bok
Engelska, 20261 139 kr
Läs direkt efter köp
Implementing R for Statistics provides comprehensive coverage of basic statistical concepts using this important open-source programming language tool, from installing R and RStudio, to exploring its basic structure and uses, to extending some core functions such as vectors, basic mathematical operations, and data frames. The book will help readers understand the latest advances in the R programming language, as R allows for sophisticated and elegant data visualization. Illustrated examples are an integral part of the text, carefully designed to apply the core principles illustrated in the text to emerging topics in the field.The text also focuses on exploiting the flexible and user-friendly nature of R. Basic concepts and recent advances in the field, including understanding the R basics, as well as implementing and practicing them in statistics, are also covered. This first edition is an essential text for students, lecturers, data scientists, and applied researchers in all areas of statistics, as well as in related fields such as biostatistics, health care, finance, risk management, social sciences, market research, and environmental and climate research. - Introduces core statistics concepts and how to apply them using R in an accessible manner- Explains to readers the newest advances in the area of implementing R in statistics through clear and meticulously crafted examples- Explores the process of learning R from installation to package creation, employing clear steps and illustrated examples- Covers important and evolving aspects of R, such as using RStudio, managing metadata, advanced regression modeling techniques, and creating packages- Includes updated package references to the latest R packages, incorporating up-to-date tools available in the field
Häftad, Engelska, 2027
984 kr
Kommande
Discrete Choices: A Comprehensive Guide to Distributions and Inference for Categorical Data provides a unified and modern treatment of probability, inference, and modeling for categorical and discrete data. Although categorical outcomes are ubiquitous across statistics, data science, economics, epidemiology, and the social sciences, existing resources often treat these topics in fragmented ways, separating probability theory, inference, diagnostics, and applications into distinct texts. This book addresses that gap by presenting a coherent framework that begins with probability foundations and sampling theory, develops classical and hierarchical discrete distributions, and advances to modern likelihood-based and Bayesian inference. Core topics include Bernoulli, Binomial, Multinomial, and Poisson models; compound distributions such as the Beta-Binomial and Dirichlet-Multinomial; resampling methods; model diagnostics; and principled model selection. Advanced chapters extend this framework to high-dimensional categorical data, compositional data, network and relational structures, causal inference with multi-valued treatments, and fairness and bias diagnostics in categorical prediction. Extensive case studies and interactive hands-on data labs illustrate real-world applications in health, the social sciences, marketing, and text analytics, emphasizing reproducible and applied workflows.Balancing theoretical rigor with practical relevance, Discrete Choices: A Comprehensive Guide to Distributions and Inference for Categorical Data is suitable both as a graduate-level textbook and as a professional reference for researchers and practitioners working with discrete data.Introduces a clear and didactic understanding of essential concepts in statistics and data science, including categorical data and statistical inferenceIncludes numerous illustrations of theoretical concepts and worked examples, which provide explanations and additional contextAligns with commonly offered upper-level courses in statistics, data science, and related topics in the fieldServes as a valuable resource for students and instructors and as solid foundational material with a unified approach for early-stage researchersOffers ancillary support, including an Instructor’s Solutions Manual and additional R and Python programming study resources for students