# Mathematical Statistics with Applications in R 2nd Edition

## Mathematical Statistics with Applications in R2nd Edition

This textbook is of an interdisciplinary nature and is designed for a one- or two semester course in probability and statistics, with basic calculus as a prerequisite. The book is primarily written to give a sound theoretical introduction to statistics while emphasizing applications. If teaching statistics is the main purpose of a two semester course in probability and statistics, this textbook covers all the probability concepts necessary for the theoretical development of statistics in two chapters, and goes on to cover all major aspects of statistical theory in two semesters, instead of only a portion of statistical concepts. What is more, using the optional section on computer examples at the end of each chapter, the student can also simultaneously learn to utilize statistical software packages for data analysis. It is our aim, without sacrificing any rigor, to encourage students to apply the theoretical concepts they have learned. There are many examples and exercises concerning diverse application areas that will show the pertinence of statistical methodology to solving real-world problems. The examples with statistical software and projects at the end of the chapters will provide good perspective on the usefulness of statistical methods. To introduce the students to modern and increasingly popular statistical methods, we have introduced separate chapters on Bayesian analysis and empirical methods.

In the second edition, while keeping much of the material from the first edition, there are some significant changes and additions. Due to the popularity of R and its free availability, we have incorporated R-codes throughout the book. This will make it easier for students to do the data analysis. We have also added a chapter on goodness of fit tests and illustrated their applicability with several examples. In addition we have introduced more probability distribution functions with real world data driven applications in global warming, brain and prostate cancer, national unemployment, and total rain fall. In this edition, we have shortened the point estimation chapter and merged it with interval estimation. In addition, many corrections and additions are made to reflect the continuous feedback we have obtained.