Applied Regression and Modeling creates a balance between the theory, practical applications, and computer implementation behind Regression-one of the most widely used techniques in analyzing and solving real world problems.It begins with a thorough explanation of prerequisite knowledge with a discussion of Simple Regression Analysis including the computer applications. Multiple Regression-a widely used tool to predict a response variable using two or more predictors, follows this. Since the analyses of regression models involve tedious and complex computations, complete computer analysis including the interpretation of multiple regression problems along with the model adequacy tests and residual analysis using widely used computer software are presented. The use of computers relieves the analyst of tedious, repetitive calculations, and allows one to focus on creating and interpreting successful models.Finally, the book extends the concepts to Regression and Modeling. Different models that provide a good fit to a set of data and provide a good prediction of the response variable are discussed. Computer analysis and interpretation of computer results are presented with real world applications.Ancillary data sets to accompany this book can be downloaded from the publisher’s website.
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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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