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Introduction to Probabilistic and Statistical Methods with Examples in R

Author: Katarzyna Stapor

Publisher: Springer Nature

Published: 2020-05-22

Total Pages: 163

ISBN-13: 3030457990

This book strikes a healthy balance between theory and applications, ensuring that it doesn’t offer a set of tools with no mathematical roots. It is intended as a comprehensive and largely self-contained introduction to probability and statistics for university students from various faculties, with accompanying implementations of some rudimentary statistical techniques in the language R. The content is divided into three basic parts: the first includes elements of probability theory, the second introduces readers to the basics of descriptive and inferential statistics (estimation, hypothesis testing), and the third presents the elements of correlation and linear regression analysis. Thanks to examples showing how to approach real-world problems using statistics, readers will acquire stronger analytical thinking skills, which are essential for analysts and data scientists alike.

Introduction to Probability and Statistics Using R

Author: G. Jay Kerns

Publisher: Lulu.com

Published: 2010-01-10

Total Pages: 388

ISBN-13: 0557249791

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This is a textbook for an undergraduate course in probability and statistics. The approximate prerequisites are two or three semesters of calculus and some linear algebra. Students attending the class include mathematics, engineering, and computer science majors.

R by Example

Author: Jim Albert

Publisher: Springer Science & Business Media

Published: 2012-01-28

Total Pages: 359

ISBN-13: 1461413656

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R by Example is an example-based introduction to the statistical computing environment that does not assume any previous familiarity with R or other software packages. R functions are presented in the context of interesting applications with real data. The purpose of this book is to illustrate a range of statistical and probability computations using R for people who are learning, teaching, or using statistics. Specifically, this book is written for users who have covered at least the equivalent of (or are currently studying) undergraduate level calculus-based courses in statistics. These users are learning or applying exploratory and inferential methods for analyzing data and this book is intended to be a useful resource for learning how to implement these procedures in R.

Learning Statistics with R

Author: Daniel Navarro

Publisher: Lulu.com

Published: 2013-01-13

Total Pages: 617

ISBN-13: 1326189727

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"Learning Statistics with R" covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information (and the opportunity to check the book out before you buy!) visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com

Introductory Statistics with R

Author: Peter Dalgaard

Publisher: Springer Science & Business Media

Published: 2008-08-15

Total Pages: 370

ISBN-13: 0387790535

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This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. Brief sections introduce the statistical methods before they are used. A supplementary R package can be downloaded and contains the data sets. All examples are directly runnable and all graphics in the text are generated from the examples. The statistical methodology covered includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one-and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last four chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, and survival analysis.

Introduction to Statistics and Data Analysis

Author: Christian Heumann

Publisher: Springer Nature

Published: 2023-01-30

Total Pages: 584

ISBN-13: 3031118332

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Now in its second edition, this introductory statistics textbook conveys the essential concepts and tools needed to develop and nurture statistical thinking. It presents descriptive, inductive and explorative statistical methods and guides the reader through the process of quantitative data analysis. This revised and extended edition features new chapters on logistic regression, simple random sampling, including bootstrapping, and causal inference. The text is primarily intended for undergraduate students in disciplines such as business administration, the social sciences, medicine, politics, and macroeconomics. It features a wealth of examples, exercises and solutions with computer code in the statistical programming language R, as well as supplementary material that will enable the reader to quickly adapt the methods to their own applications.

Introduction to Probability, Statistics and R

Author: Sujit K. Sahu

Publisher: Springer Nature

Published: 2024

Total Pages: 560

ISBN-13: 3031378652

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Zusammenfassung: 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

An Introduction to Probability and Statistics

Author: Dr. Arun Kaushik & Dr. Rajwant K. Singh

Publisher: K.K. Publications

Published: 2021-09-09

Total Pages: 168

ISBN-13:

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An Introduction to Probability and Statistics An Introduction to Probability and Statistics, First Edition, guides the readers through basic probability and statistical methods along with graphs and tables and helps to analyse critically about various basic concepts. Written by two friends i.e. Dr. Arun Kaushik and Dr. Rajwant K. Singh, this book introduces readers with no or very little prior knowledge in probability or statistics to a thinking process to help them obtain the best solution to a posed situation. It provides lots of examples for each topic discussed, and examples are covered from the medical field giving the reader more exposure in applying statistical methods to different situations. This text contains an enhanced number of exercises and graphical illustrations to motivate the readers and demonstrate the applicability of probability and statistical inference in a vast variety of human activities. Each section includes relevant proofs where ever need arises, followed by exercises with some useful clues to their solutions. Furthermore, if the need arises then the detailed solutions to all exercises will be provided in near future in an Answers Manual. This text will appeal to advanced undergraduate and graduate students, as well as researchers and practitioners in engineering, medical sciences, business, social sciences or agriculture. The material discussed in this book is enough for undergraduate and graduate courses. It consists of 5 chapters. Chapter 1 is devoted to the basic concept of probability. Chapters 2 and 3 deal with the concept of a random variable and its distribution and related topics. Chapters 4 and 5 presents an overview of statistical inference, discuss the standard topics of parametric statistical inference, namely, point estimation, interval estimation and testing hypotheses.

Probability with R

Author: Jane M. Horgan

Publisher: John Wiley & Sons

Published: 2019-12-18

Total Pages: 522

ISBN-13: 1119536987

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Provides a comprehensive introduction to probability with an emphasis on computing-related applications This self-contained new and extended edition outlines a first course in probability applied to computer-related disciplines. As in the first edition, experimentation and simulation are favoured over mathematical proofs. The freely down-loadable statistical programming language R is used throughout the text, not only as a tool for calculation and data analysis, but also to illustrate concepts of probability and to simulate distributions. The examples in Probability with R: An Introduction with Computer Science Applications, Second Edition cover a wide range of computer science applications, including: testing program performance; measuring response time and CPU time; estimating the reliability of components and systems; evaluating algorithms and queuing systems. Chapters cover: The R language; summarizing statistical data; graphical displays; the fundamentals of probability; reliability; discrete and continuous distributions; and more. This second edition includes: improved R code throughout the text, as well as new procedures, packages and interfaces; updated and additional examples, exercises and projects covering recent developments of computing; an introduction to bivariate discrete distributions together with the R functions used to handle large matrices of conditional probabilities, which are often needed in machine translation; an introduction to linear regression with particular emphasis on its application to machine learning using testing and training data; a new section on spam filtering using Bayes theorem to develop the filters; an extended range of Poisson applications such as network failures, website hits, virus attacks and accessing the cloud; use of new allocation functions in R to deal with hash table collision, server overload and the general allocation problem. The book is supplemented with a Wiley Book Companion Site featuring data and solutions to exercises within the book. Primarily addressed to students of computer science and related areas, Probability with R: An Introduction with Computer Science Applications, Second Edition is also an excellent text for students of engineering and the general sciences. Computing professionals who need to understand the relevance of probability in their areas of practice will find it useful.