Includes bibliographical references (page ) and index
1. The concept of regression -- 2. The method of least squares -- 3. Simple linear regression using software packages -- 4. Multiple regression -- 5. Goodness of fit -- 6. Regression coefficients -- 7. Causality: correlation is not causality -- 8. Qualitative variables in regression -- 9. Pitfalls of regression analysis -- Appendix -- Glossary of terms -- Notes -- References -- Index.
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 R. A. 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..