With the rise of "big data," there is an increasing demand to learn the skills needed to undertake sound quantitative analysis without requiring students to spend too much time on high-level math and proofs. This book provides an efficient alternative approach, with more time devoted to the practical aspects of regression analysis and how to recognize the most common pitfalls.

By doing so, the book will better prepare readers for conducting, interpreting, and assessing regression analyses, while simultaneously making the material simpler and more enjoyable to learn. Logical and practical in approach, Regression Analysis teaches: (1) the tools for conducting regressions; (2) the concepts needed to design optimal regression models (based on avoiding the pitfalls); and (3) the proper interpretations of regressions. Furthermore, this book emphasizes honesty in research, with a prevalent lesson being that statistical significance is not the goal of research.

This book is an ideal introduction to regression analysis for anyone learning quantitative methods in the social sciences, business, medicine, and data analytics. It will also appeal to researchers and academics looking to better understand what regressions do, what their limitations are, and what they can tell us. This will be the most engaging book on regression analysis (or Econometrics) you will ever read!

chapter 1|12 pages


chapter 2|34 pages

Regression analysis basics

chapter 3|14 pages

Essential tools for regression analysis

chapter 7|14 pages

Strategies for other regression objectives

chapter 8|48 pages

Methods to address biases

chapter 10|29 pages

Time-series models

chapter 11|12 pages

Some really interesting research

chapter 12|12 pages

How to conduct a research project

chapter 13|10 pages

Summarizing thoughts

chapter |15 pages

Appendix of background statistical tools