Introduction to Probability, Statistics & R : Foundations for Data-Based Sciences / by Sujit K. Sahu.

Format:

Book

Author/Creator:

Sahu, Sujit K., author.

Language:

English

Subjects (All):

Mathematical statistics.

Statistics.

Probabilities.

Mathematical Statistics.

Probability Theory.

Local Subjects:

Mathematical Statistics.

Statistics.

Probability Theory.

Physical Description:

1 online resource (559 pages)

Edition:

1st ed. 2024.

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2024.

Summary:

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.

Contents:

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.

Notes:

Includes bibliographical references and index.

ISBN:

3-031-37865-2