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Sahay A. Applied Regression And Modeling: A Computer Integrated Approach

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Sahay A. Applied Regression And Modeling: A Computer Integrated Approach
New York: BEP, 2016. — 215 p.
This book 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. The statistical concepts for regression, computer instructions for the software—Excel and MINITAB—used in the book, and all of the data files used can be downloaded from the website link provided.
Computer Software Integration, Computer Instructions, and Data Files
Introduction to Regression and Correlation Analysis
Regression, Covariance, and Coefficient of Correlation
Illustration of Least Squares Regression Method
Regression Analysis Using a Computer
Multiple Regression: Computer Analysis
Model Building and Computer Analysis
Models with Qualitative Independent (Dummy) Variables, Interaction Models, All Subset and Stepwise Regression Models with Computer Analysis
Notes on Implementation of Regression Models
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