Welcome

This book is the result of several years of teaching introductory statistics and econometrics at Azim Premji University. It was shaped not only by lectures and classroom discussions, but also by countless questions from students who wanted to understand why statistical methods work, rather than simply how to apply them.

Those questions made me rethink the way statistics is usually introduced.

Many introductory courses place considerable emphasis on mathematical derivations, definitions and problem-solving techniques. These are important, and this book does not seek to diminish their value. Yet, in my experience, many students complete such courses having learned the mechanics of statistical procedures without fully appreciating the logic that makes them work. They know the steps involved in carrying out a hypothesis test or estimating a regression model, but the underlying ideas — the beauty of reasoning from a sample to an entire population — often remain hidden beneath the mathematics.

This book begins from a different premise. Before introducing notation, formulas or proofs, it asks a simple question: what problem are we trying to solve? Once that question is clear, intuition follows naturally. The mathematics is then introduced not as something to be memorised, but as a precise language for expressing ideas that the reader has already encountered through examples, simulation and reasoning.

The philosophy of the book can be summarised in five words:

Question → Intuition → Rigour → Computation → Interpretation

Every chapter follows this progression. We begin with a real question, usually drawn from economics or public policy. We build intuition through examples and simulation. We then develop the mathematical foundations carefully and without unnecessary shortcuts. Finally, every idea is implemented in R using real data so that the transition from theory to empirical analysis becomes seamless.

This integration of intuition, mathematical reasoning and programming reflects the way statistics is practised today. There is no separate chapter devoted to software. Programming is not an optional add-on; it is part of the process of learning statistical thinking. Every table, figure and numerical result in this book is generated directly from R, allowing readers to reproduce every analysis for themselves.

The examples throughout the book are grounded, wherever possible, in the Indian economy. Rather than relying on unfamiliar datasets and examples, we study questions involving wages, education, poverty, agriculture, inflation, employment, health and public policy. My hope is that students will not only learn statistical methods but also develop a deeper understanding of the economic and social issues that shape contemporary India.

This emphasis is particularly important because empirical economics has changed dramatically over the past two decades. Modern applied research increasingly seeks to answer causal questions rather than merely document statistical relationships. Although this book develops the foundations of frequentist statistics, estimation and regression analysis, it does so with this broader objective in mind. The methods introduced here are intended not simply as computational tools, but as the foundations upon which modern empirical economics is built.

This book also draws extensively on the outstanding textbooks that have shaped the teaching of statistics and econometrics over many decades. I have learned enormously from those works, and this book should be viewed as a companion to that tradition rather than a replacement for it. Its contribution lies not in presenting new statistical methods, but in presenting familiar ideas through a pedagogical approach that places intuition, conceptual understanding and empirical application at its centre.

The book is written primarily for undergraduate students in India who are studying quantitative social science — economics, but equally public policy, sociology, political science, development studies and related disciplines.

I want to be explicit about one group of readers in particular. Many students arrive in these programmes without a mathematics training in school, and find statistics and econometrics genuinely difficult as a result. In my experience the difficulty is almost never a lack of ability. It is that most textbooks are written for readers who already have the background, and a student without it is left trying to learn the ideas and the notation simultaneously, from a book that explains only the second.

This book assumes no such background. Every idea is introduced before the symbols for it, mathematical steps are shown rather than left to the reader, and nothing is described as obvious. Motivated readers should be able to work through it independently.

My hope is that readers finish the book not only knowing how to perform statistical analyses in R, but also understanding the logic of frequentist inference and developing the habits of mind needed to think critically about empirical evidence.

I owe a considerable debt to my colleagues in economics at Azim Premji University. Good pedagogy is treated here as a serious professional undertaking rather than an afterthought, and the care that colleagues take — day after day, and largely unremarked — to become better teachers and better practitioners has been invaluable. That environment has done a great deal to bring this project to light.

Finally, this book would never have been written without my students. Every question asked after class, every thoughtful challenge to an explanation, every mistake that forced me to rethink the way I taught, and every conversation about data and economics has left its mark on these pages. I owe a sincere debt of gratitude to all the students who have taken my courses in introductory statistics and econometrics over the years. Your curiosity, patience and willingness to ask difficult questions have continually pushed me to become a better teacher. This book is the result of that shared journey, and I hope it helps future students discover the same excitement for statistics that you brought into my classroom.