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An outstanding introduction to the fundamentals of regression analysis-updated and expanded The methods of regression analysis are the most widely used statistical tools for discovering the relationships among variables. This classic text, with its emphasis on clear, thorough presentation of concepts and applications, offers a complete, easily accessible introduction to the fundamentals of regression analysis. Assuming only a basic knowledge of elementary statistics, Applied Regression Analysis, Third Edition focuses on the fitting and checking of both linear and nonlinear regression models, using small and large data sets, with pocket calculators or computers. This Third Edition features separate chapters on multicollinearity, generalized linear models, mixture ingredients, geometry of regression, robust regression, and resampling procedures. Extensive support materials include sets of carefully designed exercises with full or partial solutions and a series of true/false questions with answers. All data sets used in both the text and the exercises can be found on the companion disk at the back of the book. For analysts, researchers, and students in university, industrial, and government courses on regression, this text is an excellent introduction to the subject and an efficient means of learning how to use a valuable analytical tool. It will also prove an invaluable reference resource for applied scientists and statisticians.… (meer)
Applied Regression Analysis is, as one might expect, a textbook concerning the methods and application of regression analysis. The book is laid out in the usual fashion: An initial introductory chapter describing the whys and wherefores of linear regression, a second chapter which re-casts the first chapter in terms of matrix algebra, a third chapter on residual analysis and then a series of chapters that branch off into discussing and teaching a variety of regression subjects such as multiple predictor variables, “best” regression equation, model building, multiple regression applied to ANOVA, and a final chapter with an introduction to non-linear regression.
For me, as a practicing statistician for many years, what sets this book apart from its counterparts are Chapters 1 and 3. The discussion of the basic concepts of simple linear regression in Chapter 1, particularly the discussion from pages 8 to 31 of the 2nd edition, is simply the best explanation of the process I have encountered. Of particular value are the paragraphs and sentences in section 1.4 – Examining the Regression Equation. I have quoted the words at the bottom of page 22 and the top of page 23 to more people under more circumstances than I can recall. They completely destroy the ridiculous notion offered up in books, papers, internet chat rooms, etc. concerning the supposed need for Y and/or X to be normally distributed before one can use regression analysis to analyze the data.
As for Chapter 3 – it clearly explains the NEED for graphical analysis of residuals. It also, by illustration, provides an understanding of why the current general practice of just applying tests such as the Anderson-Darling or the Shapiro-Wilks or any other test for normality of residuals without a first careful examination of the graphs of the residuals guarantees you will go wrong with great assurance.
About the only major residual pattern not discussed in Chapter 3 is that of sloped parallel lines. For the interested reader, Searle discussed this pattern in a 1988 article in Technometrics.
This copy is the 2nd edition of the book. It has gone into a 3rd edition and is still available. I would recommend this book to anyone interested in learning the methods of linear regression or in obtaining a better understanding of what is going on when you click on “run regression” in whatever statistics package you happen to be using. ( )
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The foregoing analysis raises a question we have avoided until now. When a factor like X2 is dropped from a model, should we re-assess our treatment of pure error? In Table 7.6, runs 15-20 are the only repeats but, when X2 is dropped so that the data become as in Table 7.11, the pairs of runs numbered (1,3), (2,4), (5,7), and (6,8) now apparently form four pairs of repeat runs in variables X1 and X3.
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Ways of detecting this, and of allowing for it, are discussed in the second reference mentioned above.
An outstanding introduction to the fundamentals of regression analysis-updated and expanded The methods of regression analysis are the most widely used statistical tools for discovering the relationships among variables. This classic text, with its emphasis on clear, thorough presentation of concepts and applications, offers a complete, easily accessible introduction to the fundamentals of regression analysis. Assuming only a basic knowledge of elementary statistics, Applied Regression Analysis, Third Edition focuses on the fitting and checking of both linear and nonlinear regression models, using small and large data sets, with pocket calculators or computers. This Third Edition features separate chapters on multicollinearity, generalized linear models, mixture ingredients, geometry of regression, robust regression, and resampling procedures. Extensive support materials include sets of carefully designed exercises with full or partial solutions and a series of true/false questions with answers. All data sets used in both the text and the exercises can be found on the companion disk at the back of the book. For analysts, researchers, and students in university, industrial, and government courses on regression, this text is an excellent introduction to the subject and an efficient means of learning how to use a valuable analytical tool. It will also prove an invaluable reference resource for applied scientists and statisticians.
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For me, as a practicing statistician for many years, what sets this book apart from its counterparts are Chapters 1 and 3. The discussion of the basic concepts of simple linear regression in Chapter 1, particularly the discussion from pages 8 to 31 of the 2nd edition, is simply the best explanation of the process I have encountered. Of particular value are the paragraphs and sentences in section 1.4 – Examining the Regression Equation. I have quoted the words at the bottom of page 22 and the top of page 23 to more people under more circumstances than I can recall. They completely destroy the ridiculous notion offered up in books, papers, internet chat rooms, etc. concerning the supposed need for Y and/or X to be normally distributed before one can use regression analysis to analyze the data.
As for Chapter 3 – it clearly explains the NEED for graphical analysis of residuals. It also, by illustration, provides an understanding of why the current general practice of just applying tests such as the Anderson-Darling or the Shapiro-Wilks or any other test for normality of residuals without a first careful examination of the graphs of the residuals guarantees you will go wrong with great assurance.
About the only major residual pattern not discussed in Chapter 3 is that of sloped parallel lines. For the interested reader, Searle discussed this pattern in a 1988 article in Technometrics.
This copy is the 2nd edition of the book. It has gone into a 3rd edition and is still available. I would recommend this book to anyone interested in learning the methods of linear regression or in obtaining a better understanding of what is going on when you click on “run regression” in whatever statistics package you happen to be using. (