This worldwide best-selling business statistics text teaches students how to apply
statistics to real business problems through the authors unique three-step approach to
problem solving. Students learn to IDENTIFY the right technique by focusing on the problem
objective and data type. They then learn to COMPUTE the statistics either by hand, using
Excel, or using MINITAB. Finally, they INTERPRET the results in the context of the
problem. Kellers approach enhances student comprehension as well as practical skills. The
book offers maximum flexibility to instructors wishing to teach concepts by hand or with
the computer, or by using both hand and computer methods.
Benefits
A consistent, proven three-step method to solving problems is presented.
"Identify, Compute, and Interpret" shows students how to determine the
appropriate technique, how to compute the statistics, and then interpret the results in
the context of the problem at hand.
Providing maximum flexibility for instructors, most calculations are performed in three
ways: by hand, using Excel, and using MINITAB. Output and step-by-step instructions
accompany these illustrations.
Two unique cumulative review chapters (Chapters 14 and 24) further develop
technique-identification skills and show students how previously introduced concepts fit
into the big picture. These review chapters contain exercises and cases that require the
use of techniques previously introduced and feature a flowchart that guides students in
determining the appropriate statistical method.
Chapter-opening cases motivate students and illustrate practical uses of techniques
introduced in the chapter.
Many of the examples, exercises, and cases are based on actual studies performed by
statisticians and published in journals, newspapers, and magazines, or presented at
conferences. Many data files were recreated to produce the original results.
Interactive Java applets let students experience statistics firsthand. Based on the
SEEING STATISTICS Web-based product, the applets have been specifically customized to this
text. The 19 applets and 83 exercises associated with them allow students to visualize
statistical concepts.
Accompanying each new copy of the text, the Students Suite CD-ROM contains a free study
guide, data sets, Java applets, links to the companion Web site, and 25 appendices.
vMentor: FREE Live Online Tutoring. Available FREE to your students when you adopt this
book is text-specific one-to-one online tutoring help with a subject area expert who has a
copy of the text you are using in class. vMentor features two-way audio, an interactive
whiteboard for displaying presentation materials, and instant messaging. With vMentor,
students interact with the tutor and other students using standard Windows or Macintosh
microphones and speakers. Inside the vMentor virtual classroom, icons indicate who is in
the class and who is speaking, sending a message, or using the whiteboard. To ask a
question, students simply click on the "hand" icon. For proprietary, college,
and university adopters only. For more information on vMentor (including hours the virtual
classroom is open), contact your Thomson representative.
Table of contents
1. WHAT IS STATISTICS?
Introduction. Key Statistical Concepts. Statistical Applications in Business. Statistics
and the Computer. World Wide Web and Learning Center. Introduction to Microsoft Excel.
Introduction to MINITAB.
2. GRAPHICAL AND TABULAR DESCRIPTIVE TECHNIQUES.
Introduction. Types of Data and Information. Graphical and Tabular Techniques for Nominal
Data. Graphical Techniques for Interval Data. Describing the Relationship Between Two
Variables. Describing Time-Series Data. Summary.
3. ART AND SCIENCE OF GRAPHICAL PRESENTATIONS.
Introduction. Graphical Excellence. Graphical Deception. Presenting Statistics: Written
Reports and Oral Presentations. Summary.
4. NUMERICAL DESCRIPTIVE TECHNIQUES.
Introduction. Measures of Central Location. Measures of Variability. Measures of Relative
Standing and Box Plots. Measures of Linear Relationship. (Optional) Applications in
Professional Sports: Baseball. Comparing Graphical and Numerical Techniques. General
Guidelines for Exploring Data. Summary.
REVIEW OF DESCRIPTIVE TECHNIQUES.
5. DATA COLLECTION AND SAMPLING.
Introduction. Methods of Collecting Data. Sampling. Sampling Plans. Sampling and
Nonsampling Errors. Summary.
6. PROBABILITY.
Introduction. Assigning Probability to Events. Joint, Marginal, and Conditional
Probability. Probability Rules and Trees. Bayes' Law. Identifying the Correct Method.
Summary.
7. RANDOM VARIABLES AND DISCRETE PROBABILITY DISTRIBUTIONS.
Introduction. Random Variables and Probability Distributions. Bivariate Distributions.
(Optional) Applications in Finance: Portfolio Diversification and Asset Allocation.
Binomial Distribution. Poisson Distribution. Summary.
8. CONTINUOUS PROBABILITY DISTRIBUTIONS.
Introduction. Probability Density Functions. Normal Distribution. (Optional) Exponential
Distribution. Other Continuous Distributions. Summary.
9. SAMPLING DISTRIBUTIONS.
Introduction. Sampling Distribution of the Mean. Sampling Distribution of a Proportion.
Sampling Distribution of the Difference Between Two Means. From Here to Inference.
Summary.
10. INTRODUCTION TO ESTIMATION.
Introduction. Concepts of Estimation. Estimating the Population Mean When the Population
Standard Deviation is Known. Selecting the Sample Size. Summary.
11. INTRODUCTION TO HYPOTHESIS TESTING.
Introduction. Concepts of Hypothesis Testing. Testing the Population Mean When the
Population Standard Deviation is Known. Calculating the Probability of a Type II Error.
The Road Ahead. Summary.
12. INFERENCE ABOUT A POPULATION.
Introduction. Inference about a Population Mean When the Standard Deviation is Unknown.
Inference about a Population Variance. Inference about a Population Proportion. (Optional)
Applications in Marketing: Market Segmentation. (Optional) Applications in Accounting:
Auditing. Summary.
13. INFERENCE ABOUT COMPARING TWO POPULATIONS.
Introduction. Inference about the Difference Between Two Means: Independent Samples.
Observational and Experimental Data. Inference about the Difference Between Two Means:
Matched Pairs Experiment. Inference about the Ratio of Two Variances. Inference about the
Difference Between Two Population Proportions. Summary. Excel Instructions for Stacked and
Unstacked Data. MINITAB Instructions for Stacked and Unstacked Data.
14. STATISTICAL INFERENCE: REVIEW OF CHAPTERS 12 AND 13.
Introduction. Guide to Identifying the Correct Technique: Chapters 12 and 13.
15. ANALYSIS OF VARIANCE.
Introduction. One-Way Analysis of Variance. Analysis of Variance Experimental Designs.
Randomized Blocks (Two-Way) Analysis of Variance. Two-Factor Analysis of Variance.
(Optional) Applications in Operations Management: Finding and Reducing Variation. Multiple
Comparisons. Summary.
16. CHI-SQUARED TESTS.
Introduction. Chi-Squared Goodness-of-Fit Test. Chi-Squared Test of a Contingency Table.
Summary of Tests on Nominal Data. (Optional) Chi-Squared Tests for Normality. Summary.
17. SIMPLE LINEAR REGRESSION AND CORRELATION.
Introduction. Model. Estimating the Coefficients. Error Variable: Required Conditions.
Assessing the Model. (Optional) Applications in Finance: Market Model. Using the
Regression Equation. Regression Diagnostics?I. Summary.
18. MULTIPLE REGRESSION.
Introduction. Model and Required Conditions. Estimating the Coefficients and Assessing the
Model. Regression Diagnostics-II. Regression Diagnostics-III (Time Series). Summary.
19. MODEL BUILDING.
Introduction. Polynomial Models. Nominal Independent Variables. (Optional) Applications in
Human Resources Management: Pay Equity. (Optional) Logistic Regression. (Optional)
Stepwise Regression. Model Building. Summary.
20. TIME SERIES ANALYSIS AND FORECASTING.
Introduction. Time Series Components. Smoothing Techniques. Trend and Seasonal Effects.
Introduction to Forecasting. Forecasting Models. Summary.
21. NONPARAMETRIC STATISTICS.
Introduction. Wilcoxon Rank Sum Test. Sign Test and Wilcoxon Signed Rank Sum Test.
Kruskal?Wallis Test. Friedman Test. Spearman Rank Correlation Coefficient. Summary.
22. STATISTICAL PROCESS CONTROL.
Introduction. Process Variation. Control Charts. Control Charts for Variables: and Charts.
Control Charts for Attributes: p Chart. Summary.
23. DECISION ANALYSIS.
Introduction. Decision Problem. Acquiring, Using, and Evaluating Additional Information.
Summary.
24 STATISTICAL INFERENCE: CONCLUSION.
Introduction. Identifying the Correct Technique: Summary of Statistical Inference. The
Last Word.
Appendix A: DATA FILE SAMPLE STATISTICS.
Appendix B: TABLES.
Binomial Probabilities. Poisson Probabilities. Normal Probabilities. Critical Values of t.
Critical Values of. Critical Values of F. Critical Values of the Studentized Range.
Critical Values for the Wilcoxon Rank Sum Test. Critical Values for the Wilcoxon Signed
Rank Sum Test. Critical Values for the Spearman Rank Correlation Coefficient. Critical
Values for the Durbin?Watson Statistic. Control Chart Constants.
Appendix C: ANSWERS TO SELECTED EVEN-NUMBERED EXERCISES.
INDEX.
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