Bruce L. Bowerman,
Miami University Richard T. O'Connell,
Miami University Emily S. Murphree,
Miami University
ISBN: 0073373591 Copyright year: 2009
What's New
New to the fifth edition are:
• A shorter and simpler introduction to business processes and an expanded treatment
of data acquisition and sampling (in Chapter 1). In order to make Chapter 1 easier to
cover, the fifth edition has a shortened and simplified introduction to business processes.
This introduction is now part of a discussion of sampling in Section 1.2. In addition,
Chapter 1 contains a new, optional section (Section 1.5) that discusses more advanced
aspects of data acquisition and survey sampling.
• Two chapters on descriptive statistics. Whereas previous editions covered all of descriptive
statistics in a single chapter, the fifth edition breaks this material into two, shorter
chapters. In addition, the explanations of all descriptive statistics topics have been simplified
and clarified. Chapter 2, which discusses the graphical and tabular methods of descriptive
statistics, begins with the analysis of qualitative data and has new material on frequency
polygons, ogives, and contingency tables. Chapter 3, which discusses the numerical methods
of descriptive statistics, contains new material on covariance, correlation, and the least
squares line. Both chapters utilize a substantial number of new examples, exercises, cases,
and data sets, including The Jeep Case and The Household Income Case.
• Use of the cumulative normal table in the discussion of the normal distribution in
Chapter 6 and throughout the rest of the book. Because use of the cumulative normal
table makes many normal curve calculations easier and is consistent with the way that most
statistical software systems give normal curve probabilities, the fifth edition uses this table
for all normal curve applications. A very complete cumulative normal table is given on the
front pages of the book, in Chapter 6, and in Table A.3 of Appendix A. (A table giving areas
under the standard normal curve from 0 to z is given in Table A.19 of Appendix A.) Normal
probability plots are discussed in an optional section of Chapter 6.
• A clearer and more motivating discussion of sampling distributions in Chapter 7.
Chapter 7 begins with a new case: The Risk Reduction Case: Game Shows and Stock
Returns. This case motivates students to think about the properties of sampling distributions
in a “fun” and familiar context.
• A clearer and more motivating introduction to confidence intervals in Chapter 8. Chapter 8 begins with an intuitive example that illustrates a practical application of a confidence interval for a population mean. Then, using this example as a springboard, Chapter 8 develops the logic behind and a formula for a confidence interval for a population mean (as well as other confidence interval formulas). One new real-world case exercise in Chapter 8 is The Air Traffic Control Case.
• A simpler and streamlined discussion of hypothesis testing in Chapter 9. As in the
fourth edition, the basic hypothesis testing chapter uses a seven-step procedure that breaks
hypothesis testing down into small, easy-to-understand steps and clearly shows how to use
the book’s hypothesis testing summary boxes. In addition, the material on hypothesis tests
for a population mean has been simplified and streamlined for the fifth edition. A motivating
new case—The Valentine’s Day Chocolate Case—is used throughout the discussion of
hypothesis testing.
• Three chapters on regression analysis. Chapter 13 discusses simple linear regression
analysis, including an introduction to residual analysis. Chapter 14 discusses multiple
regression analysis, including the use of dummy variables and analyzing residuals from a
multiple regression model. Chapter 15 discusses model building and model diagnostics,
including the use of quadratic and interaction terms, logistic regression, identifying outlying and influential observations, data transformations, and the Durbin–Watson test. Chapters 13 and 14 contain significantly simplified presentations of the simple linear regression model, multiple regression models, the least squares point estimates, and confidence and prediction intervals. Furthermore, the more advanced material in Chapter 15 has been designed for maximum teaching flexibility. For example, the discussions of quadratic and interaction terms in Chapter 15 can be studied before or after the material on dummy variables in Chapter 14. In addition, the presentations of data transformations and the Durbin–Watson test in Chapter 15 can be covered after studying residual analysis in simple linear regression (in Chapter 13) or, alternatively, after studying residual analysis in multiple regression (in Chapter 14).
• Inclusion of Holt–Winters’ double exponential smoothing and multiplicative Winters’
method in Chapter 16, which discusses time series forecasting.
• Increased coverage of Excel, Minitab, and MegaStat (an Excel add-in package included on the text’s CD-ROM). Throughout the fifth edition we provide an abundant number of outputs from all three packages in both examples and exercises that allow students to concentrate on statistical interpretations. This use of outputs is particularly prominent in areas where hand calculations are impossible or impractical and where having students run their own programs (while theoretically optimal) would, because of time constraints, not allow them to see a wide variety of applications. These areas include descriptive statistics, ANOVA, regression, and time series forecasting. In addition, appendices at the end of each chapter demonstrate how to use Excel, Minitab and Megastat. Additional capabilities of all three packages are demonstrated, and the screen captures illustrating the use of the packages are more colorful and easier to read. For the fifth edition, the developer of MegaStat, Professor J.B. Orris of Butler University, has worked closely with us. We believe that MegaStat is the most comprehensive, accurate, and easy to use Excel add-in package in existence. In addition to remedying most of the computational problems associated with the Excel data analysis toolpak, MegaStat is also specifically designed to enhance the use of Business Statistics in Practice.
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2009 McGraw-Hill Higher Education