Applied Regression Modeling

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.

An applied and concise treatment of statistical regressiontechniques for business students and professionals who have littleor no background in calculusRegression analysis is an invaluable statistical methodology inbusiness settings and is vital to model the relationship between aresponse variable and one or more predictor variables, as well asthe prediction of a response value given values of the predictors.In view of the inherent uncertainty of business processes, such asthe volatility of consumer spending and the presence of marketuncertainty, business professionals use regression analysis to makeinformed decisions. Applied Regression Modeling: A BusinessApproach offers a practical, workable introduction to regressionanalysis for upper-level undergraduate business students, MBAstudents, and business managers, including auditors, financialanalysts, retailers, economists, production managers, andprofessionals in manufacturing firms.The book's overall approach is strongly based on an abundant use ofillustrations and graphics and uses major statistical softwarepackages, including SPSS(r), Minitab(r), SAS(r), and R/S-PLUS(r).Detailed instructions for use of these packages, as well as forMicrosoft Office Excel(r), are provided, although Excel does nothave a built-in capability to carry out all the techniquesdiscussed.Applied Regression Modeling: A Business Approach offers specialuser features, including:* A companion Web site with all the datasets used in the bookclassroom presentation slides for instructors, additional problemsand ideas for organizing class time around the material in thebook, and supplementary instructions for popular statisticalsoftware packages. An Instructor's Solutions Manual is alsoavailable.* A generous selection of problems-many requiring computer work-ineach chapter with fullyworked-out solutions* Two real-life dataset applications used repeatedly in examplesthroughout the book to familiarize the reader with theseapplications and the techniques they illustrate* A chapter containing two extended case studies to show the directapplicability of the material* A chapter on modeling extensions illustrating more advancedregression techniques through the use of real-life examples andcovering topics not normally seen in a textbook of thisnature* More than 100 figures to aid understanding of the materialApplied Regression Modeling: A Business Approach fully preparesprofessionals and students to apply statistical methods in theirdecision-making, using primarily regression analysis and modeling.To help readers understand, analyze, and interpret business dataand make informed decisions in uncertain settings, many of theexamples and problems use real-life data with a business focussuch as production costs, sales figures, stock prices, economicindicators, and salaries. A calculus background is not required tounderstand and apply the methods in the book.

Preface.Acknowledgments.Introduction.1. Foundations.2. Simple linear regression.3. Multiple liner regression.4. Regression model building I.5. Regression model building II.6. Case studies.7. Extensions.Appendix A: Computer software help.Appendix B: Critical Values for t-distributions.Appendix C: Notation and formulas.Appendix D: Mathematics refresher.Appendix E: Brief answers to selected problems.References.Glossary.Index.