Solving Problems of Probability and Statistics using R.
Preface
Probability and statistics are essential components of modern data science. They provide the tools and techniques for analyzing and interpreting data, making predictions, and making decisions under uncertainty. The R programming language is widely used in data science for its rich set of statistical functions and packages.
This book aims to provide a comprehensive guide to solving problems in probability and statistics using R. It is intended for students, researchers, and practitioners who want to gain a deeper understanding of probability and statistics and develop their skills in data analysis using R.
The book covers a broad range of topics in probability and statistics, including fundamentals in probability theory, distributions, hypothesis testing, regression analysis, and ANOVA. Each chapter is structured around a set of problems, which are presented with detailed explanations, R code, and output. The problems range from basic concepts to advanced topics, allowing readers to build their knowledge gradually and practice their skills with real-world data.
To use this book effectively, readers should have a basic understanding of probability and statistics and be familiar with R programming. The book includes a brief introduction to R and the packages used in the book, but readers should have some prior experience with R.
We hope that this book will be a valuable resource for anyone interested in probability and statistics and their applications in data science. We welcome feedback and suggestions for improvement and hope that readers will find the book both informative and enjoyable.
License:
is licensed under Creative Commons Attribution License v4.01