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.
Tabla de materias
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.
Sobre el autor
IAIN PARDOE, PHD, is Assistant Professor in the Department
of Decision Sciences in the Charles H. Lundquist College of
Business at the University of Oregon. His areas of interest include
Bayesian analysis, multilevel modeling, graphical methods,
diagnostics and validation, choice modeling, and statistics
education. He has published research in many leading statistical
journals and has received multiple university-wide and
association-related awards and honors.
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