Dekorationsartikel gehören nicht zum Leistungsumfang.
Introduction to Probability, Statistics & R
Foundations for Data-Based Sciences
Buch von Sujit K. Sahu
Sprache: Englisch

62,95 €*

inkl. MwSt.

Versandkostenfrei per Post / DHL

Lieferzeit 2-3 Wochen

Kategorien:
Beschreibung
A strong grasp of elementary statistics and probability, along with basic skills in using R, is essential for various scientific disciplines reliant on data analysis. This book serves as a gateway to learning statistical methods from scratch, assuming a solid background in high school mathematics. Readers gradually progress from basic concepts to advanced statistical modelling, with examples from actuarial, biological, ecological, engineering, environmental, medicine, and social sciences highlighting the real-world relevance of the subject. An accompanying R package enables seamless practice and immediate application, making it ideal for beginners.
The book comprises 19 chapters divided into five parts. Part I introduces basic statistics and the R software package, teaching readers to calculate simple statistics and create basic data graphs. Part II delves into probability concepts, including rules and conditional probability, and introduces widelyused discrete and continuous probability distributions (e.g., binomial, Poisson, normal, log-normal). It concludes with the central limit theorem and joint distributions for multiple random variables. Part III explores statistical inference, covering point and interval estimation, hypothesis testing, and Bayesian inference. This part is intentionally less technical, making it accessible to readers without an extensive mathematical background. Part IV addresses advanced probability and statistical distribution theory, assuming some familiarity with (or concurrent study of) mathematical methods like advanced calculus and linear algebra. Finally, Part V focuses on advanced statistical modelling using simple and multiple regression and analysis of variance, laying the foundation for further studies in machine learning and data science applicable to various data and decision analytics contexts.
Based on years of teaching experience, this textbook includes numerousexercises and makes extensive use of R, making it ideal for year-long data science modules and courses. In addition to university courses, the book amply covers the syllabus for the Actuarial Statistics 1 examination of the Institute and Faculty of Actuaries in London. It also provides a solid foundation for postgraduate studies in statistics and probability, or a reliable reference for statistics.
A strong grasp of elementary statistics and probability, along with basic skills in using R, is essential for various scientific disciplines reliant on data analysis. This book serves as a gateway to learning statistical methods from scratch, assuming a solid background in high school mathematics. Readers gradually progress from basic concepts to advanced statistical modelling, with examples from actuarial, biological, ecological, engineering, environmental, medicine, and social sciences highlighting the real-world relevance of the subject. An accompanying R package enables seamless practice and immediate application, making it ideal for beginners.
The book comprises 19 chapters divided into five parts. Part I introduces basic statistics and the R software package, teaching readers to calculate simple statistics and create basic data graphs. Part II delves into probability concepts, including rules and conditional probability, and introduces widelyused discrete and continuous probability distributions (e.g., binomial, Poisson, normal, log-normal). It concludes with the central limit theorem and joint distributions for multiple random variables. Part III explores statistical inference, covering point and interval estimation, hypothesis testing, and Bayesian inference. This part is intentionally less technical, making it accessible to readers without an extensive mathematical background. Part IV addresses advanced probability and statistical distribution theory, assuming some familiarity with (or concurrent study of) mathematical methods like advanced calculus and linear algebra. Finally, Part V focuses on advanced statistical modelling using simple and multiple regression and analysis of variance, laying the foundation for further studies in machine learning and data science applicable to various data and decision analytics contexts.
Based on years of teaching experience, this textbook includes numerousexercises and makes extensive use of R, making it ideal for year-long data science modules and courses. In addition to university courses, the book amply covers the syllabus for the Actuarial Statistics 1 examination of the Institute and Faculty of Actuaries in London. It also provides a solid foundation for postgraduate studies in statistics and probability, or a reliable reference for statistics.
Über den Autor
Sujit Sahu is a Professor of Statistics at the University of Southampton. He is the author of the book Bayesian Modeling of Spatio-Temporal Data with R published by Chapman and Hall/CRC Press. He has published more than 60 research papers on statistical methods and modelling.
Zusammenfassung

Covers the most fundamental topics in probability and statistics, both theoretical and applied

Includes a dedicated R package to learn, practise and use the theory

Request lecturer material: [...]

Inhaltsverzeichnis
Part I Introduction to basic Statistics and R.- 1 Introduction to basic statistics.- 2 Getting started with R.- Part II Introduction to Probability.- 3 Introduction to probability.- 4 Conditional probability and independence.- 5 Random variables and their probability distributions.- 6 Standard discrete distributions.- 7 Standard continuous distributions.- 8 Joint distributions and the CLT.- Part III Introduction to Statistical Inference.- 9 Introduction to statistical inference.- 10 Methods of point estimation.- 11 Interval estimation.- 12 Hypothesis testing.- Part IV Advanced Distribution Theory and Probability.- 13 Generating functions.- 14 Transformation and transformed distributions.- 15 Multivariate distributions.- 16 Convergence of estimators.- Part V Introduction to statistical modelling.- 17 Simple linear regression model.- 18 Multiple linear regression model.- 19 Analysis of variance.- Appendix: Table of common distributions.
Details
Erscheinungsjahr: 2024
Fachbereich: Wahrscheinlichkeitstheorie
Genre: Mathematik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Seiten: 576
Inhalt: xix
557 S.
27 s/w Illustr.
82 farbige Illustr.
555 p. 109 illus.
82 illus. in color.
ISBN-13: 9783031378645
ISBN-10: 3031378644
Sprache: Englisch
Ausstattung / Beilage: HC runder Rücken kaschiert
Einband: Gebunden
Autor: Sahu, Sujit K.
Auflage: 2024
Hersteller: Springer International Publishing
Maße: 241 x 160 x 37 mm
Von/Mit: Sujit K. Sahu
Erscheinungsdatum: 02.04.2024
Gewicht: 1,021 kg
preigu-id: 127155729
Über den Autor
Sujit Sahu is a Professor of Statistics at the University of Southampton. He is the author of the book Bayesian Modeling of Spatio-Temporal Data with R published by Chapman and Hall/CRC Press. He has published more than 60 research papers on statistical methods and modelling.
Zusammenfassung

Covers the most fundamental topics in probability and statistics, both theoretical and applied

Includes a dedicated R package to learn, practise and use the theory

Request lecturer material: [...]

Inhaltsverzeichnis
Part I Introduction to basic Statistics and R.- 1 Introduction to basic statistics.- 2 Getting started with R.- Part II Introduction to Probability.- 3 Introduction to probability.- 4 Conditional probability and independence.- 5 Random variables and their probability distributions.- 6 Standard discrete distributions.- 7 Standard continuous distributions.- 8 Joint distributions and the CLT.- Part III Introduction to Statistical Inference.- 9 Introduction to statistical inference.- 10 Methods of point estimation.- 11 Interval estimation.- 12 Hypothesis testing.- Part IV Advanced Distribution Theory and Probability.- 13 Generating functions.- 14 Transformation and transformed distributions.- 15 Multivariate distributions.- 16 Convergence of estimators.- Part V Introduction to statistical modelling.- 17 Simple linear regression model.- 18 Multiple linear regression model.- 19 Analysis of variance.- Appendix: Table of common distributions.
Details
Erscheinungsjahr: 2024
Fachbereich: Wahrscheinlichkeitstheorie
Genre: Mathematik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Seiten: 576
Inhalt: xix
557 S.
27 s/w Illustr.
82 farbige Illustr.
555 p. 109 illus.
82 illus. in color.
ISBN-13: 9783031378645
ISBN-10: 3031378644
Sprache: Englisch
Ausstattung / Beilage: HC runder Rücken kaschiert
Einband: Gebunden
Autor: Sahu, Sujit K.
Auflage: 2024
Hersteller: Springer International Publishing
Maße: 241 x 160 x 37 mm
Von/Mit: Sujit K. Sahu
Erscheinungsdatum: 02.04.2024
Gewicht: 1,021 kg
preigu-id: 127155729
Warnhinweis

Ähnliche Produkte

Ähnliche Produkte