This set contains: 9780471221753 Logistic Regression Using the SAS System: Theory and Application by Paul D. Allison and 9780471746966 Regression Analysis by Example, Fourth Edition by Samprit Chatterjee, Ali S. Hadi.

Logistic Regression Using the SAS System: Theory and Application + Regression Analysis by Example

Science & Math

Chatterjee, Samprit

Allison, Paul D.

Hadi, Ali S.

Mathematics

Wiley-Interscience

Regression analysis provides 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, Third Edition explains the principles underlying exploratory data analysis, emphasizing data analysis rather than statistical theory. This is not just another edition of the book; it is a major rewriting and reorganization of the previous edition. The new edition is expanded and updated to reflect recent advances in the field, offering in-depth treatment of diagnostic plots, time series regression, multicollinearity, and logistic regression. Suitable for anyone with an understanding of elementary statistics, Regression Analysis by Example, Third Edition illustrates methods of regression analysis, with examples containing the types of irregularities commonly encountered in the real world. 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. Each of the methods described can be carried out with most currently available statistical software packages.

Regression Analysis by Example, 3rd Edition

Price, Bertram

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: <ul>
<li>A unique focus on modern topics including data analysis and inference</li> <li>Specific data in such areas as wind, flood, chain strength, electrical insulation, fatigue, precipitation, and wave heights</li> <li>Useful techniques for addressing extreme value problems, including discrete, continuous, univariate, and multivariate models</li> <li>Coverage of order statistics, return period, exceedances and shortfalls, along with detailed explanations on how to obtain exact distributions for these statistics</li> <li>An in-depth look at asymptotic models and the limit distributions of maxima, minima, and other order statistics</li> </ul>
Enhanced 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.

Extreme Value and Related Models with Applications in Engineering and Science

Balakrishnan, Narayanaswamy

Castillo, Enrique

Sarabia, Jose M.

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: 
<ul>
<li>Chapter 12, Logistic Regression, is expanded to reflect the increased use of the logit models in statistical analysis 
</li> <li>A new chapter entitled Further Topics discusses advanced areas of regression analysis 
</li> <li>Reorganized, expanded, and upgraded exercises appear at the end of each chapter 
</li> <li>A fully integrated Web page provides data sets 
</li> <li>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.</li> </ul>

Regression Analysis by Example

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.

Sensitivity Analysis in Linear Regression

wiley

Offering an applied research approach with a strong geometric flavor, this book shows students the value of matrix algebra as a tool for a number of advanced statistics and mathematical techniques.

Matrix Algebra as a Tool

Duxbury Press

Regression Analysis by Example 5e with SAS Univariate and Multivariate 2e Set (Wiley Series in Probability and Statistics)

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 (Wiley Series in Probability and Statistics)

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.

Expert Systems and Probabilistic Network Models (Monographs in Computer Science)

Computers & Technology

Gutierrez, Jose M.

Computer Science

Springer

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