Ali S. Hadi – författare

The essentials of regression analysis through practical applications 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 judgement. Regression Analysis by Example, Fourth 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. This new edition features the following enhancements: Chapter 12, Logistic Regression, is expanded to reflect the increased use of the logit models in statistical analysis A new chapter entitled Further Topics discusses advanced areas of regression analysis Reorganized, expanded, and upgraded exercises appear at the end of each chapter A fully integrated Web page provides data sets Numerous graphical displays highlight the significance of visual appeal Regression Analysis by Example, Fourth Edition is suitable for anyone with an understanding of elementary statistics. 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 of the techniques themselves, the required assumptions, and the evaluated success of each technique. The methods described throughout the book can be carried out with most of the currently available statistical software packages, such as the software package R. An Instructor''s Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

Treats linear regression diagnostics as a tool for application of linear regression models to real-life data. Presentation makes extensive use of examples to illustrate theory. Assesses the effect of measurement errors on the estimated coefficients, which is not accounted for in a standard least squares estimate but is important where regression coefficients are used to apportion effects due to different variables. Also assesses qualitatively and numerically the robustness of the regression fit.

A straightforward, practical guide to extreme value modeling for today's world Measuring and interpreting data for extreme values presents a unique and important challenge that has far-reaching implications for all aspects of modern engineering and science. Extreme Value and Related Models with Applications in Engineering and Science reflects the latest information in this growing field. The book incorporates illuminating real-world examples from such areas as structural engineering, hydraulics, meteorology, materials science, highway traffic analysis, environmetrics, and climatology, and is designed to help engineers, mathematicians, statisticians, and scientists gain a clearer understanding of extreme value theory and then translate that knowledge into practical applications within their own fields of research.The book provides: A unique focus on modern topics including data analysis and inferenceSpecific data in such areas as wind, flood, chain strength, electrical insulation, fatigue, precipitation, and wave heightsUseful techniques for addressing extreme value problems, including discrete, continuous, univariate, and multivariate modelsCoverage of order statistics, return period, exceedances and shortfalls, along with detailed explanations on how to obtain exact distributions for these statisticsAn in-depth look at asymptotic models and the limit distributions of maxima, minima, and other order statisticsEnhanced with numerous graphs and exercises, plus an extensive bibliography for further study, this text is an important reference source for engineers designing structures that will withstand even the most extreme circumstances.

Treats linear regression diagnostics as a tool for application of linear regression models to real-life data. Presentation makes extensive use of examples to illustrate theory. Assesses the effect of measurement errors on the estimated coefficients, which is not accounted for in a standard least squares estimate but is important where regression coefficients are used to apportion effects due to different variables. Also assesses qualitatively and numerically the robustness of the regression fit.

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.

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.

Regression Analysis By Example Using RA STRAIGHTFORWARD AND CONCISE DISCUSSION OF THE ESSENTIALS OF REGRESSION ANALYSISIn the newly revised sixth edition of Regression Analysis By Example Using R, distinguished statistician Dr Ali S. Hadi delivers an expanded and thoroughly updated discussion of exploratory data analysis using regression analysis in R. The book provides in-depth treatments of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression.The author clearly demonstrates effective methods of regression analysis with examples that contain the types of data irregularities commonly encountered in the real world. This newest edition also offers a brand-new, easy to read chapter on the freely available statistical software package R.Readers will also find: Reorganized, expanded, and upgraded exercises at the end of each chapter with an emphasis on data analysisUpdated data sets and examples throughout the bookComplimentary access to a companion website that provides data sets in xlsx, csv, and txt formatPerfect for upper-level undergraduate or beginning graduate students in statistics, mathematics, biostatistics, and computer science programs, Regression Analysis By Example Using R will also benefit readers who need a reference for quick updates on regression methods and applications.

Regression Analysis By Example Using R

A STRAIGHTFORWARD AND CONCISE DISCUSSION OF THE ESSENTIALS OF REGRESSION ANALYSIS

In the newly revised sixth edition of Regression Analysis By Example Using R, distinguished statistician Dr Ali S. Hadi delivers an expanded and thoroughly updated discussion of exploratory data analysis using regression analysis in R. The book provides in-depth treatments of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression.

The author clearly demonstrates effective methods of regression analysis with examples that contain the types of data irregularities commonly encountered in the real world. This newest edition also offers a brand-new, easy to read chapter on the freely available statistical software package R.

Readers will also find:

Reorganized, expanded, and upgraded exercises at the end of each chapter with an emphasis on data analysis Updated data sets and examples throughout the book Complimentary access to a companion website that provides data sets in xlsx, csv, and txt format

Perfect for upper-level undergraduate or beginning graduate students in statistics, mathematics, biostatistics, and computer science programs, Regression Analysis By Example Using R will also benefit readers who need a reference for quick updates on regression methods and applications.

Regression Analysis By Example Using R

A STRAIGHTFORWARD AND CONCISE DISCUSSION OF THE ESSENTIALS OF REGRESSION ANALYSIS

In the newly revised sixth edition of Regression Analysis By Example Using R, distinguished statistician Dr Ali S. Hadi delivers an expanded and thoroughly updated discussion of exploratory data analysis using regression analysis in R. The book provides in-depth treatments of regression diagnostics, transformation, multicollinearity, logistic regression, and robust regression.

The author clearly demonstrates effective methods of regression analysis with examples that contain the types of data irregularities commonly encountered in the real world. This newest edition also offers a brand-new, easy to read chapter on the freely available statistical software package R.

Readers will also find:

Reorganized, expanded, and upgraded exercises at the end of each chapter with an emphasis on data analysis Updated data sets and examples throughout the book Complimentary access to a companion website that provides data sets in xlsx, csv, and txt format

Perfect for upper-level undergraduate or beginning graduate students in statistics, mathematics, biostatistics, and computer science programs, Regression Analysis By Example Using R will also benefit readers who need a reference for quick updates on regression methods and applications.

Artificial intelligence and expert systems have seen a great deal of research in recent years, much of which has been devoted to methods for incorporating uncertainty into models. This book is devoted to providing a thorough and up-to-date survey of this field for researchers and students.