Praise for the First Edition "The attention to detail is impressive. The book is very well written and the author is extremely careful with his descriptions . . . the examples are wonderful." —The American Statistician Fully revised to reflect the latest methodologies and emerging applications, Applied Regression Modeling, Second Edition continues to highlight the benefits of statistical methods, specifically regression analysis and modeling, for understanding, analyzing, and interpreting multivariate data in business, science, and social science applications. The author utilizes a bounty of real-life examples, case studies, illustrations, and graphics to introduce readers to the world of regression analysis using various software packages, including R, SPSS, Minitab, SAS, JMP, and S-PLUS. In a clear and careful writing style, the book introduces modeling extensions that illustrate more advanced regression techniques, including logistic regression, Poisson regression, discrete choice models, multilevel models, and Bayesian modeling. In addition, the Second Edition features clarification and expansion of challenging topics, such as: Transformations, indicator variables, and interaction Testing model assumptions Nonconstant variance Autocorrelation Variable selection methods Model building and graphical interpretation Throughout the book, datasets and examples have been updated and additional problems are included at the end of each chapter, allowing readers to test their comprehension of the presented material. In addition, a related website features the book's datasets, presentation slides, detailed statistical software instructions, and learning resources including additional problems and instructional videos. With an intuitive approach that is not heavy on mathematical detail, Applied Regression Modeling, Second Edition is an excellent book for courses on statistical regression analysis at the upper-undergraduate and graduate level. The book also serves as a valuable resource for professionals and researchers who utilize statistical methods for decision-making in their everyday work.
An applied and concise treatment of statistical regression techniques for business students and professionals who have little or no background in calculus Regression analysis is an invaluable statistical methodology in business settings and is vital to model the relationship between a response variable and one or more predictor variables, as well as the prediction of a response value given values of the predictors. In view of the inherent uncertainty of business processes, such as the volatility of consumer spending and the presence of market uncertainty, business professionals use regression analysis to make informed decisions. Applied Regression Modeling: A Business Approach offers a practical, workable introduction to regression analysis for upper-level undergraduate business students, MBA students, and business managers, including auditors, financial analysts, retailers, economists, production managers, and professionals in manufacturing firms. The book's overall approach is strongly based on an abundant use of illustrations and graphics and uses major statistical software packages, including SPSS(r), Minitab(r), SAS(r), and R/S-PLUS(r). Detailed instructions for use of these packages, as well as for Microsoft Office Excel(r), are provided, although Excel does not have a built-in capability to carry out all the techniques discussed. Applied Regression Modeling: A Business Approach offers special user features, including: * A companion Web site with all the datasets used in the book, classroom presentation slides for instructors, additional problems and ideas for organizing class time around the material in the book, and supplementary instructions for popular statistical software packages. An Instructor's Solutions Manual is also available. * A generous selection of problems-many requiring computer work-in each chapter with fullyworked-out solutions * Two real-life dataset applications used repeatedly in examples throughout the book to familiarize the reader with these applications and the techniques they illustrate * A chapter containing two extended case studies to show the direct applicability of the material * A chapter on modeling extensions illustrating more advanced regression techniques through the use of real-life examples and covering topics not normally seen in a textbook of this nature * More than 100 figures to aid understanding of the material Applied Regression Modeling: A Business Approach fully prepares professionals and students to apply statistical methods in their decision-making, using primarily regression analysis and modeling. To help readers understand, analyze, and interpret business data and make informed decisions in uncertain settings, many of the examples and problems use real-life data with a business focus, such as production costs, sales figures, stock prices, economic indicators, and salaries. A calculus background is not required to understand and apply the methods in the book.
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