東華大學圖書館 |

Introduction to probability, statistics & R = foundations for data-based sciences /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Introduction to probability, statistics & R/ by Sujit K. Sahu.
其他題名:	foundations for data-based sciences /
作者:	Sahu, Sujit K.
出版者:	Cham :Springer International Publishing : : 2024.,
面頁冊數:	xix, 555 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Mathematical statistics. -
電子資源:	https://doi.org/10.1007/978-3-031-37865-2
ISBN:	9783031378652

Introduction to probability, statistics & R = foundations for data-based sciences /
Sahu, Sujit K.

Introduction to probability, statistics & Rfoundations for data-based sciences /[electronic resource] :by Sujit K. Sahu. - Cham :Springer International Publishing :2024. - xix, 555 p. :ill., digital ;24 cm.

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.

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 widely used 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 numerous exercises 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.

ISBN: 9783031378652

Standard No.: 10.1007/978-3-031-37865-2doiSubjects--Topical Terms:

516858
Mathematical statistics.

LC Class. No.: QA276

Dewey Class. No.: 519.5

Introduction to probability, statistics & R = foundations for data-based sciences /
LDR:04303nmm a22003255a 4500 001 2370624
003 DE-He213
005 20240402095822.0
006 m d
007 cr nn 008maaau
008 241127s2024 sz s 0 eng d
020 $a 9783031378652 $q (electronic bk.)
020 $a 9783031378645 $q (paper)
024 7 $a 10.1007/978-3-031-37865-2 $2 doi
035 $a 978-3-031-37865-2
040 $a GP $c GP
041 0 $a eng
050 4 $a QA276
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a PBT $2 thema
082 0 4 $a 519.5 $2 23
090 $a QA276 $b .S131 2024
100 1 $a Sahu, Sujit K. $3 3716111
245 1 0 $a Introduction to probability, statistics & R $h [electronic resource] : $b foundations for data-based sciences / $c by Sujit K. Sahu.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2024.
300 $a xix, 555 p. : $b ill., digital ; $c 24 cm.
505 0 $a 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.
520 $a 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 widely used 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 numerous exercises 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.
650 0 $a Mathematical statistics. $3 516858
650 0 $a R (Computer program language) $3 784593
650 1 4 $a Mathematical Statistics. $3 3538791
650 2 4 $a Statistics. $3 517247
650 2 4 $a Probability Theory. $3 3538789
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-37865-2
950 $a Mathematics and Statistics (SpringerNature-11649)