Regression for economics / Shahdad Naghshpour.
2016
HB137 .N247 2016
Linked e-resources
Linked Resource
Online Access
Details
Title
Regression for economics / Shahdad Naghshpour.
Edition
Second edition.
ISBN
9781631574436 paperback
9781631574443 (electronic bk.)
9781631574443 (electronic bk.)
Published
New York, New York (222 East 46th Street, New York, NY 10017) : Business Expert Press, 2016.
Language
English
Description
1 online resource (xix, 166 pages)
Call Number
HB137 .N247 2016
Dewey Decimal Classification
519.536
Summary
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.
Bibliography, etc. Note
Includes bibliographical references (page [161]) and index.
Access Note
Access limited to authorized users.
Source of Description
Title from PDF title page (viewed on May 12, 2016).
Series
Economics collection. 2163-7628
Available in Other Form
Print version: 9781631574436
Linked Resources
Online Access
Record Appears in
Online Resources > Ebooks
All Resources
All Resources
Table of Contents
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.
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.