Regression for economics /

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,...

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Bibliographic Details
Main Author: Naghshpour, Shahdad., (Author)
Format: eBook
Language:English
Published: New York, New York (222 East 46th Street, New York, NY 10017) : Business Expert Press, 2016.
Edition:Second edition.
Series:Economics collection.
Subjects:
Online Access:Click to View
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100 1 |a Naghshpour, Shahdad.,  |e author. 
245 1 0 |a Regression for economics /  |c Shahdad Naghshpour. 
250 |a Second edition. 
264 1 |a New York, New York (222 East 46th Street, New York, NY 10017) :  |b Business Expert Press,  |c 2016. 
300 |a 1 online resource (xix, 166 pages) 
336 |a text  |2 rdacontent 
337 |a computer  |2 rdamedia 
338 |a online resource  |2 rdacarrier 
490 1 |a Economics collection,  |x 2163-7628 
504 |a Includes bibliographical references (page [161]) and index. 
505 0 |a 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. 
506 1 |a Access restricted to authorized users and institutions. 
520 3 |a 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. 
588 |a Title from PDF title page (viewed on May 12, 2016). 
590 |a Electronic reproduction. Ann Arbor, MI : ProQuest, 2016. Available via World Wide Web. Access may be limited to ProQuest affiliated libraries. 
650 0 |a Economics  |x Statistical methods. 
650 0 |a Regression analysis. 
653 |a analysis 
653 |a causality 
653 |a ceteris paribus 
653 |a coefficient of determination 
653 |a control variables 
653 |a error 
653 |a goodness of fit 
653 |a inference 
653 |a misspecification 
653 |a model 
653 |a proxy variables 
653 |a regression 
653 |a spurious regression 
653 |a Stata 
655 4 |a Electronic books. 
776 0 8 |i Print version:  |z 9781631574436 
797 2 |a ProQuest (Firm) 
830 0 |a Economics collection.  |x 2163-7628 
856 4 0 |u https://ebookcentral.proquest.com/lib/matrademy/detail.action?docID=4518807  |z Click to View