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"Chatterjee, Samprit"
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Handbook of regression analysis
\"Written by two established experts in the field, the purpose of this handbook is to provide a practical, one-stop reference on regression analysis. The focus is on the tools that both practitioners and researchers use in real life. It is intended to be a comprehensive collection of the theory, methods, and applications of the subject matter, but it is deliberately written at an accessible level. The handbook will provide a quick and convenient reference or \"refresher\" on ideas and methods that are useful for the accurate analysis of data and its resulting interpretations. Students can use the book as an introduction to and/or summary of key concepts in regression and related course work (such as linear, nonlinear, and nonparametric regressions). Plentiful references are supplied for the more motivated readers. Theory is presented when necessary, and always supplemented by hands-on examples. Software routines are available via an author-maintained web site\"-- Provided by publisher.
Book
Handbook of regression analysis
\"Written by an established expert in the field, the purpose of this handbook is to provide a practical, one-stop reference on regression analysis. The focus is on the tools that both practitioners and researchers use in real life. It is intended to be a comprehensive collection of the theory, methods, and applications of the subject matter, but it is deliberately written at an accessible level. The handbook will provide a quick and convenient reference or \"refresher\" on ideas and methods that are useful for the accurate analysis of data and its resulting interpretations. Students can use the book as an introduction to and/or summary of key concepts in regression and related course work (such as linear, nonlinear, and nonparametric regressions). Plentiful references are supplied for the more motivated readers. Theory is presented when necessary, and always supplemented by hands-on examples. Software routines are available via an author-maintained web site\"--
eBook
Regression analysis by example
Praise for the Fourth Edition:
\"This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable.\"
—Journal of the American Statistical Association Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded and thoroughly updated to reflect recent advances in the field. The emphasis continues to be on exploratory data analysis rather than statistical theory. The book offers in-depth treatment of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression.
The book now includes a new chapter on the detection and correction of multicollinearity, while also showcasing the use of the discussed methods on newly added data sets from the fields of engineering, medicine, and business. The Fifth Edition also explores additional topics, including:
* Surrogate ridge regression
* Fitting nonlinear models
* Errors in variables
* ANOVA for designed experiments
Methods of regression analysis are clearly demonstrated, and examples containing the types of irregularities commonly encountered in the real world are provided. Each example isolates one or two techniques and features detailed discussions, the required assumptions, and the evaluated success of each technique. Additionally, methods described throughout the book can be carried out with most of the currently available statistical software packages, such as the software package R.
Regression Analysis by Example, Fifth Edition is suitable for anyone with an understanding of elementary statistics.
eBook
Strain-Dependent Differences in the Efficiency of Transgenic Mouse Production
Transgenic mouse production via pronuclear microinjection is a complex process consisting of a number of sequential steps. Many different factors contribute to the effectiveness of each step and thus influence the overall efficiency of transgenic mouse production. The response of egg donor females to superovulation, the fertilization rate, egg survival after injection, ability of manipulated embryos to implant and develop to term, and concentration and purity of the injected DNA all contribute to transgenic production efficiency. We evaluated and compared the efficiency of transgenic mouse production using four different egg donor mouse strains: B6D2/F1 hybrids, Swiss Webster (SW) outbred, and inbred FVB/N and C57BL/6. The data included experiments involving approximately 350 DNA transgene constructs performed by a high capacity core transgenic mouse facility. Significant influences of particular genetic backgrounds on the efficiency of different steps of the production process were found. Except for egg production, FVB/N mice consistently produced the highest efficiency of transgenic mouse production at each step of the process. B6D2/F2 hybrid eggs are also quite efficient, but lyze more frequently than FVB/N eggs after DNA microinjection. SW eggs on the other hand block at the 1-cell stage more often than eggs from the other strains. Finally, using C57BL/6 eggs the main limiting factor is that the fetuses derived from injected eggs do not develop to term as often as the other strains. Based on our studies, the procedure for transgenic mouse production can be modified for each egg donor strain in order to overcome any deficiencies, and thus to increase the overall efficiency of transgenic mouse production.
Journal Article
Influential Observations, High Leverage Points, and Outliers in Linear Regression
A bewilderingly large number of statistical quantities have been proposed to study outliers and influence of individual observations in regression analysis. In this article we describe the inter-relationships which exist among the proposed measures. An examination of these relationships leads us to conclude that only three of these measures along with some graphical displays can provide an analyst a complete picture of outliers (major discrepant points) and points which excessively influence the fitted regression equation. Illustrative examples based on real data are presented.
Journal Article
Regression analysis by example, fifth edition
Praise for the Fourth Edition: \"This book is . . . an excellent source of examples for regression analysis. It has been and still is readily readable and understandable.\" —Journal of the American Statistical Association Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. Regression Analysis by Example, Fifth Edition has been expanded and thoroughly updated to reflect recent advances in the field. The emphasis continues to be on exploratory data analysis rather than statistical theory. The book offers in-depth treatment of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression. The book now includes a new chapter on the detection and correction of multicollinearity, while also showcasing the use of the discussed methods on newly added data sets from the fields of engineering, medicine, and business. The Fifth Edition also explores additional topics, including: -Surrogate ridge regression -Fitting nonlinear models -Errors in variables -ANOVA for designed experiments Methods of regression analysis are clearly demonstrated, and examples containing the types of irregularities commonly encountered in the real world are provided. Each example isolates one or two techniques and features detailed discussions, the required assumptions, and the evaluated success of each technique. Additionally, methods described throughout the book can be carried out with most of the currently available statistical software packages, such as the software package R. Regression Analysis by Example, Fifth Edition is suitable for anyone with an understanding of elementary statistics.
eBook
Regression Analysis by Example. 5th Edition
Regression analysis is a conceptually simple method for investigating relationships among variables. Carrying out a successful application of regression analysis, however, requires a balance of theoretical results, empirical rules, and subjective judgment. \"Regression Analysis by Example, Fifth Edition\" has been expanded and thoroughly updated to reflect recent advances in the field. The emphasis continues to be on exploratory data analysis rather than statistical theory. The book offers in-depth treatment of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression. The book now includes a new chapter on the detection and correction of multicollinearity, while also showcasing the use of the discussed methods on newly added data sets from the fields of engineering, medicine, and business. The \"Fifth Edition\" also explores additional topics, including: (1) Surrogate ridge regression; (2) Fitting nonlinear models; (3) Errors in variables; and (4) ANOVA for designed experiments. Methods of regression analysis are clearly demonstrated, and examples containing the types of irregularities commonly encountered in the real world are provided. Each example isolates one or two techniques and features detailed discussions, the required assumptions, and the evaluated success of each technique. Additionally, methods described throughout the book can be carried out with most of the currently available statistical software packages, such as the software package R. \"Regression Analysis by Example, Fifth Edition\" is suitable for anyone with an understanding of elementary statistics.
Book
The Numerical Effect of Measurement Error in the Explanatory Variables on the Observed Least Squares Estimate
The numerical effect of measurement error on the least squares estimate in the linear regression model is examined. The change in the least squares estimate is measured by calculating a stochastic upper bound on the relative distance between the true (unobserved) and observed (with error) $j$th component. The bound is derived in the case of $k$ of $p$ explanatory variables measured with error. If the bound indicates that none of the estimates are badly perturbed, the analysis can continue without concern about the effect of measurement error. Simulations are carried out to compare this bound with the first-order upper bound of Golub and Van Loan [Matrix Computations, Johns Hopkins University Press, 1983], and the componentwise upper bound of Higham [Contemp. Math., 112 (1990), pp. 195-208].
Journal Article