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| Author | : Donald Eugene Farrar |
| Publisher | : |
| Total Pages | : 16 |
| Release | : 1967 |
| Genre | : |
| ISBN | : |
| Author | : D. E. Farrar |
| Publisher | : Forgotten Books |
| Total Pages | : 58 |
| Release | : 2017-11-24 |
| Genre | : Mathematics |
| ISBN | : 9780331842623 |
Excerpt from Multicollinearity in Regression Analysis: The Problem Revisited Y, X as observed values, measured as standardized deviates, of the dependent and independent variables, 8 as the true (structural) coefficients, u as the true (unobserved) error term, with distributional properties specified by the general linear model. About the Publisher Forgotten Books publishes hundreds of thousands of rare and classic books. Find more at www.forgottenbooks.com This book is a reproduction of an important historical work. Forgotten Books uses state-of-the-art technology to digitally reconstruct the work, preserving the original format whilst repairing imperfections present in the aged copy. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in our edition. We do, however, repair the vast majority of imperfections successfully; any imperfections that remain are intentionally left to preserve the state of such historical works.
| Author | : Donald Eugene Farrar |
| Publisher | : Nabu Press |
| Total Pages | : 56 |
| Release | : 2014-01 |
| Genre | : |
| ISBN | : 9781295545964 |
This is a reproduction of a book published before 1923. This book may have occasional imperfections such as missing or blurred pages, poor pictures, errant marks, etc. that were either part of the original artifact, or were introduced by the scanning process. We believe this work is culturally important, and despite the imperfections, have elected to bring it back into print as part of our continuing commitment to the preservation of printed works worldwide. We appreciate your understanding of the imperfections in the preservation process, and hope you enjoy this valuable book.
| Author | : Donald Eugene Farrar |
| Publisher | : Legare Street Press |
| Total Pages | : 0 |
| Release | : 2022-10-27 |
| Genre | : |
| ISBN | : 9781016613965 |
This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work is in the "public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.
| Author | : Dr.M. Chandrasekhar Reddy & Dr.P. Balasubramanyam |
| Publisher | : KY Publications |
| Total Pages | : 150 |
| Release | : 2021-09-01 |
| Genre | : Business & Economics |
| ISBN | : 8194807549 |
There are several textbooks are available in literature in Econometrics, but we thought it is really beneficial to students and researchers to have a special textbook on multicollinearity problem in the general linear model. The topic of multicollinearity has gained high importance in recent times as the data getting generated is increased enormously. Because of this data exploration, many variables are representing the same amount of information which leads to the problem of multicollinearity. In the current textbook, the authors tried to explore the topic of multicollinearity along with the basic definitions and key tests available to detect multicollinearity. For all practical application purposes, we included a chapter on empirical analysis that will show how the model goes improved through dealing with the problem of multicollinearity. This book acts as a textbook, reference manual for all students who are studying econometrics at their graduate and post-graduate levels and also for research scholars. The design of contents is structured in such a way that users find it easy to understand and implement the same in their research works.
| Author | : Douglas C. Montgomery |
| Publisher | : John Wiley & Sons |
| Total Pages | : 679 |
| Release | : 2015-06-29 |
| Genre | : Mathematics |
| ISBN | : 1119180171 |
Praise for the Fourth Edition "As with previous editions, the authors have produced a leading textbook on regression." —Journal of the American Statistical Association A comprehensive and up-to-date introduction to the fundamentals of regression analysis Introduction to Linear Regression Analysis, Fifth Edition continues to present both the conventional and less common uses of linear regression in today’s cutting-edge scientific research. The authors blend both theory and application to equip readers with an understanding of the basic principles needed to apply regression model-building techniques in various fields of study, including engineering, management, and the health sciences. Following a general introduction to regression modeling, including typical applications, a host of technical tools are outlined such as basic inference procedures, introductory aspects of model adequacy checking, and polynomial regression models and their variations. The book then discusses how transformations and weighted least squares can be used to resolve problems of model inadequacy and also how to deal with influential observations. The Fifth Edition features numerous newly added topics, including: A chapter on regression analysis of time series data that presents the Durbin-Watson test and other techniques for detecting autocorrelation as well as parameter estimation in time series regression models Regression models with random effects in addition to a discussion on subsampling and the importance of the mixed model Tests on individual regression coefficients and subsets of coefficients Examples of current uses of simple linear regression models and the use of multiple regression models for understanding patient satisfaction data. In addition to Minitab, SAS, and S-PLUS, the authors have incorporated JMP and the freely available R software to illustrate the discussed techniques and procedures in this new edition. Numerous exercises have been added throughout, allowing readers to test their understanding of the material. Introduction to Linear Regression Analysis, Fifth Edition is an excellent book for statistics and engineering courses on regression at the upper-undergraduate and graduate levels. The book also serves as a valuable, robust resource for professionals in the fields of engineering, life and biological sciences, and the social sciences.
| Author | : Patsy S. M. Chan |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 1974 |
| Genre | : Multicollinearity |
| ISBN | : |
The purpose of this study is to review the problem of multicollinearity in regression analysis. Specifically, the difficulties that arise when multicollinearity is present, the alternative procedures available for detecting the problem, and the methods by which it may be resolved, are discussed. It is discovered that an error exists in Kirchdorfer's method of detection, thus rendering the procedure invalid. In considering the strengths and weaknesses of each method of detection or resolution, what crystallizes is the view that to date, no highly satisfactory means of treating the problem has yet appeared. An illustrative application of the theory is obtained using Farrar- Glauber's techniques to detect multicollinearity in a sample of economic data. Hoerl and Kennard's Ridge Trace is then constructed, followed by calculation of Mayer-Willke shrunken estimator to remedy the detected multicollinearity.
| Author | : Richard F. Gunst |
| Publisher | : Routledge |
| Total Pages | : 426 |
| Release | : 2018-04-27 |
| Genre | : Mathematics |
| ISBN | : 1351419293 |
Regression Analysis and Its Application: A Data-Oriented Approach answers the need for researchers and students who would like a better understanding of classical regression analysis. Useful either as a textbook or as a reference source, this book bridges the gap between the purely theoretical coverage of regression analysis and its practical application. The book presents regression analysis in the general context of data analysis. Using a teach-by-example format, it contains ten major data sets along with several smaller ones to illustrate the common characteristics of regression data and properties of statistics that are employed in regression analysis. The book covers model misspecification, residual analysis, multicollinearity, and biased regression estimators. It also focuses on data collection, model assumptions, and the interpretation of parameter estimates. Complete with an extensive bibliography, Regression Analysis and Its Application is suitable for statisticians, graduate and upper-level undergraduate students, and research scientists in biometry, business, ecology, economics, education, engineering, mathematics, physical sciences, psychology, and sociology. In addition, data collection agencies in the government and private sector will benefit from the book.
| Author | : D. Neeleman |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 111 |
| Release | : 2012-12-06 |
| Genre | : Business & Economics |
| ISBN | : 9401174865 |
It was R. Frisch, who in his publications 'Correlation and Scatter Analysis in Statistical Variables' (1929) and 'Statistical Confluence Analysis by means of Complete Regression Systems' (1934) first pointed out the complications that arise if one applies regression analysis to variables among which several independent linear relations exist. Should these relationships be exact, then there exist two closely related solutions for this problem, viz. 1. The estimation of 'stable' linear combinations of coefficients, the so-called estimable functions. 2. The dropping of the wen-known condition of unbiasedness of the estimators. This leads to minimum variance minimum bias estimators. This last solution is generalised in this book for the case of a model consisting of several equations. In econometrics however, the relations among variables are nearly always approximately linear so that one cannot apply one of the solutions mentioned above, because in that case the matrices used in these methods are, although ill-conditioned, always of full rank. Approximating these matrices by good-conditioned ones of the desired rank, it is possible to apply these estimation methods. In order to get an insight in the consequences of this approximation a simulation study has been carried out for a two-equation model. Two Stage Least Squares estimators and estimators found with the aid of the above mentioned estimation method have been compared. The results of this study seem to be favourable for this new method.
| Author | : Valérie Mignon |
| Publisher | : Springer Nature |
| Total Pages | : 417 |
| Release | : |
| Genre | : |
| ISBN | : 3031525353 |
