Product details

This chapter is excerpted from ‘Regression for Economics, Second Edition'. The concept of regression was introduced by Legendre in 1805 and advanced by Gauss in 1809. The term was popularized after Galton's 1886 article. Contribution of RA Fisher in the early 20th century was instrumental to the spread of the method to every scientific branch. Regression analysis, used in economics and many other fields, is now the most commonly used statistical method. Although few would characterize this technique as simple, regression is in fact both simple and elegant. The complexity that many attribute to regression analysis is often a reflection of their lack of familiarity with the language of mathematics. But regression analysis can be understood even without the mastery of sophisticated mathematical concepts. This book provides the foundation of regression analysis in a way that is easy to comprehend. All the examples are from economics and in almost all the examples real data are used to show the application of the method. This book seeks to demystify regression analysis. The logic of regression analysis is similar to the intrinsic logic that we apply in comprehending the various events that fill our lives, which are probabilistic rather than deterministic in nature. What hinders peoples' comprehension of regression analysis is often the mathematical symbols and derivations. By removing this obstacle, the present book enables the logical reader to learn regression. Although the book is largely nonmathematical in its approach, it does not in any way shortchange the subject of regression. The book is targeted to all business students and executives who need to understand the concept of regression for practical and professional purposes. Regression analysis can be used to establish causal relationships between factors and the response variable. However, in order to be able to do so, economic theory must be used to provide the causal relationship and then regression analysis is applied to verify the validity of the theory. All the examples in the book are answered using Stata software as well as Microsoft Excel. Stata is powerful yet affordable, while Excel is easily accessible and already available on most computers. Users seeking quick solutions away from their own computer can find Excel easily. Users who wish to become more proficient and require computational power should use Stata.