Introduction To Linear Regression Analysis(6th Edition)
Authors:
Douglas C. Montgomery, Elizabeth A. Peck, G. Geoffrey Vining
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Solution Manual Includes
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Book details
ISBN: 1119578728, 9781119578727
Book publisher: Wiley
Book Price $0 : Introduction to Linear Regression Analysis, authored by Douglas C. Montgomery, Elizabeth A. Peck, and G. Geoffrey Vining, serves as a comprehensive guide to understanding and applying linear regression techniques. This 6th edition book is an essential resource for those delving into the complexities of linear models and the nuances of statistical analysis. A critical component of this text is its robust exploration of multiple regression methods, emphasizing both theoretical understanding and practical application. The book is meticulously organized, beginning with simple linear regression and advancing towards more complex topics like logistic regression and generalized linear models. A noteworthy feature of this work is the inclusion of a 'solution manual,' providing an 'Answer Key' to critical exercises. This enhances its utility for self-learners and educators alike. Each chapter carefully unfolds a 'table of content' that guides readers through the depths of regression diagnostics, model selection, and validation techniques. Concepts such as ANOVA, the least squares method, and residual analysis are thoroughly discussed, allowing readers to build a solid foundation in regression analysis techniques. Statistical software applications, including R and SAS, are integrated into discussions, offering practical insight into data analysis workflows. Recognized for its clarity and depth, this book is highly regarded in the field for its rich examples and extensive problem sets, making it indispensable for students and professionals seeking to master linear regression. 40 LSI words: This book comprehensively covers regression assumptions and provides guidance on practical implementation. Students benefit from the intuitive presentation, comprehensive exercises, and step-by-step illustrations that consolidate learning and guide through hypothesis testing and statistical inference, critical for data analysis careers. This guide provides a cost-effective and cheap way to master complex topics quickly.
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