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In this present book Chapter-I is an introductory one. Chapter-II describes the various criteria for selection of regressors in the multiple regression analysis existing in this book. Chapter-III deals with the basic stepwise regression procedures for variable selection in multiple regression analysis and The mean square error of prediction criterion has been discussed along with a similar average estimated variance criterion for the selection of variables in the general linear model. Chapter-IV presents the various methods for choosing variable subsets in multiple linear regression analysis…mehr

Produktbeschreibung
In this present book Chapter-I is an introductory one. Chapter-II describes the various criteria for selection of regressors in the multiple regression analysis existing in this book. Chapter-III deals with the basic stepwise regression procedures for variable selection in multiple regression analysis and The mean square error of prediction criterion has been discussed along with a similar average estimated variance criterion for the selection of variables in the general linear model. Chapter-IV presents the various methods for choosing variable subsets in multiple linear regression analysis under these methods, the mean squared prediction error has been considered as basis of the criteria. Chapter-V proposes some new criteria for selection of regressors in econometrics based on different types of residuals such as Ordinary Least Squares, Studentized and Predicted residuals. Chapter-VI depicts the main conclusions of the present research study. It also narrates the plan for future research as an extension in the lines of study. Several relevant references have been documented under a separate title "BIBLIOGRAPHY".
Autorenporträt
Ashok worked as a Lead Consultant with multiple Global Consulting Leaders with over 6 years of Statistical Modelling / Consulting experience in the areas BFSI (Banking, Financial Services & Insurance), Health Care Insurance and Retail & consumer products. He has 5 years of teaching and research experience.