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Applied Regression and Modeling: A Computer Integrated Approach - Softcover

Amar Sahay (author)

 
9781631573293: Applied Regression and Modeling: A Computer Integrated Approach

Synopsis

The book is divided into three parts - (1) prerequisite to regression analysis followed by a discussion on simple regression, (2) multiple regression analysis with applications, and (3) regression and modeling including the second order models, nonlinear regression, and interaction models in regressions. All these sections provide examples with complete computer analysis and instructions commonly used in modeling and analyzing these problems. The book deals with detailed analysis and interpretation of computer results. This will help readers to appreciate the power of computer in applying regression models. The readers will find that the understanding of computer results is critical to implementing regression and modeling in real world situation. The book is written for juniors, seniors and graduate students in business, MBAs, professional MBAs, and working people in business and industry. Managers, practitioners, professionals, quality professionals, quality engineers, and anyone involved in data analysis, business analytics, and quality and six sigma will find the book to be a valuable resource.

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About the Author

Dr. Amar Sahay is a professor engaged in teaching, research, consulting, and training. He has a BS in production engineering from Birla Institute of Technology, India, an MS in industrial engineering and a PhD in mechanical engineering, – both from University of Utah. He has taught/is teaching at several Utah institutions including the University of Utah, school of engineering and management, SLCC, Westminster college, and others. Amar is a certified Six Sigma Master Black Belt and holds expert-level certification in lean manufacturing / and lean management. He has contributed over 30 research papers in national and international conferences. Amar is the author of 11 books and is a senior member of Industrial & Systems Engineers, American Society for Quality, and Data Science Central.

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