Statistics is “a branch of applied mathematics that is concerned with the collection and interpretation of quantitative data and the use of probability theory to estimate population parameters” [1]. Statistics is useful for many branches of science and engineering as well as in many practical applications. This book offers an introduction to statistics using the interactive tool MM*Stat. It comes in hardcover, ebook, and online enhanced ebook formats. The enhanced ebook format includes facilities for note taking, highlighting, and searching. It is proposed to contain videos, a glossary, and quizzes. The book is meant for undergraduate and graduate students taking their first statistics courses, as well as for undergraduate students in non-mathematical fields.

There are 12 chapters. The book begins with the basics of statistics and describes its goals. The notions of quantitative and qualitative variables are explained. Then, 1D frequency distributions are discussed. The concepts of frequency distributions for discrete as well as continuous data are explicated. Probability theory is central for statistics. The book looks at the fundamental concepts of probability theory. Sample spaces, events, independent events, and conditional probabilities are dealt with. Total probabilities and the rule of Bayes are also studied. There is a brief look at combinatorics. Permutations, combinations, and variations are discussed. The properties of Euler numbers are presented. Random variables including 1D and 2D random variables are covered. Discrete as well as continuous random variables are examined. Probability theory and probability distributions are key concepts for statistics, and various types of distributions such as uniform, binomial, hypergeometric, Poisson, exponential, normal, chi-square, and F-distributions are touched upon in the book.

There is a cursory look at sampling theory. Distributions of the sample mean, sample proportion, and sample variance are analyzed. The book delves into estimation theory, and properties and construction of estimators are discussed. Confidence intervals of the mean, proportion, and the variance are reported along with other types of confidence intervals. The chapter on statistical tests describes the main concepts related to statistical tests. The chi-square test is described in detail. Akin to 1D frequency distributions, 2D frequency distributions are also important. The chapter on 2D frequency distributions highlights the graphical representation of multidimensional data, 2D frequency tables, and the characteristics of 2D frequency distributions. Regression analysis is briefly glanced at in the book. The last chapter is on time series analysis. An abbreviated view of time series analysis and the quality of the time series model concludes the book. There is an appendix on datasets used in the interactive examples.

The book includes many examples with explanations. The authors refer to these as explained examples. The book also provides enhanced examples, which require knowledge from earlier chapters of the book. The main benefit of the book is its interactive examples. These examples allow the use of various datasets, permit a variety of analysis methods, and allow variation of the parameters of the chosen methods. Many diagrams and illustrations are included, some of which are in color. A glossary appears toward the end of the book. The most disappointing features of the book are the absence of an index and references to the literature. I hope these will be included in a future edition. This book will be useful for statistics students.