## Statistics for Business and Economics, 7/e

James T. McClave, University of Florida
P. George Benson, Rutgers University
Terry Sincich, University of South Florida

Published November, 1997 by Prentice Hall Engineering/Science/Mathematics

Cloth
ISBN 0-13-840232-9

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Introduction to Business Statistics (Two Semester)-Decision Science

For a one/two-term business statistics course. Designed for students with a background in basic algebra, this best-selling introduction to statistics for business and economics emphasizes inference — with extensive coverage of data collection and analysis as needed to evaluate the reported results of statistical studies and make good business decisions. It stresses the development of statistical thinking, the assessment of credibility and value of the inferences made from data — both by those who consume and those who produce them — and features numerous case studies, examples, and exercises — all drawing on real business situations and recent economic events.

NEW—Integrates computer output throughout:

• In selected examples and in exercise sets — to give students practice in interpretation with the output of statistical software packages such as SPSS, Minitab, SAS and the spreadsheet package, EXCEL.
NEW—Chapter 1 — emphasizes the science of statistics and its role in business decisions. Includes revised/expanded/new sections on:
• Process and quality control.
• Data collection from published sources, designed experiments, surveys, and observations.
• Statistical thinking.
NEW—Chapter 2 — expands coverage of descriptive analytical tools that are useful in examining assumptions about data. Includes revised/expanded/new sections on:
• Both graphical and numerical methods for summarizing qualitative data.
• Time series plots and the graphing of bivariate data relationships (optional).
• Variability.
NEW—Chapter 7 — provides more emphasis on confidence interval estimation procedures and their interpretation.
• The approach to small-sample confidence intervals is motivated by pharmaceutical testing requirements.
• Adds new optional sections on the finite population correction and survey designs.
NEW—Chapter 8 — features more balanced treatment of p-values and critical values in interpreting testing results.
• New examples make greater use of computer solutions rather than formulas.
NEW—Chapter 9 — reorganized to present inference in the context of the experimental design used for data collection.
NEW—Chapter 10 — includes expanded coverage of correlation — which continues the emphasis, from Ch. 2, on bivariate linear relationships.
• Where appropriate, shifts the emphasis from formulas to the interpretation of computer output, including Excel spreadsheets.
NEW—Chapter 11 — incorporates more extensive computer output and inferences about the beta parameters and includes new material on confidence intervals.
NEW—Chapter 12 — offers new/revised coverage of model-building:
• The consequences of choosing the wrong model — i.e., the errors in prediction.
• Models with more than two quantitative variables.
NEW—Chapter 15 — contains a separate section on multiple comparisons — with emphasis on computer output rather than formulas.
• Presents examples of both Bonferroni's and Tukey's method.
NEW—Features Statistics in Action boxes — two or three per chapter — that examine high profile, contemporary, controversial business, economic, government, or entertainment issues that involve statistics.
• Focus questions prompt students to form their own conclusions and to think through the statistical issues involved.
NEW—Features six extensive business problem-solving casesShow Cases — with real data and assignments.
NEW—Contains six Internet Labs designed to help students retrieve and download raw data from the internet for analysis.
NEW—Contains 60% new or revised examples and exercises — featuring real business data from 1990 to 1996.
NEW—Adds a Chapter-end Review to each chapter:
• Quick Review — provides a list of key terms and formulas with page number references.
• Language Lab — helps students learn the language of statistics through pronunciation guides, descriptions of symbols, names, etc.
• Supplementary Exercises — review all of the important topics covered in the chapter.
Offers a choice in level of coverage of probability.
• Unlike other introductory texts which mix probability and counting rules, this text includes the counting rules in a separate and optional section at the end of the chapter on probability.
• All exercises that require the use of counting rules are marked with an asterisk.
Features extensive coverage of regression analysis — with three chapters covering simple regression, multiple regression, and model building.
• Content is understandable, usable, and much more comprehensive than the presentations in other introductory statistics texts.
• Discusses the major types of inferences that can be derived from a regression analysis — showing how these results appear in computer printouts and selecting multiple regression models to be used in an analysis.
Provides an abundance of exercises (1400) labeled by type and illustrating applications in almost all areas of research. All exercise sections are divided into two parts:
• Learning the Mechanics — straightforward applications of new concepts — to test students' ability to comprehend a concept or a definition.
• Applying the Concepts — based on applications and real data taken from a wide variety of journals, newspapers, magazines, and other sources published since 1990. These exercises develop students' skills at comprehending real world problems that describe situations to which the techniques may be applied.
Highlights important information in colored boxes — Definitions, Strategies, Key Formulas and other important information.
Provides “We're We've Been . . . We're Going” chapter openers — with quick reviews of how information learned previously applies to the chapter at hand and how the chapter topics fit into students' growing understanding of statistical inference.
Includes Footnotes that allow additional flexibility in the mathematical and theoretical level at which the material is presented. They:
• Explain the role of calculus in various derivations.
• Cover some of the theory underlying certain results.

Each chapter concludes with a Quick Review.
1. Statistics, Data, and Statistical Thinking.

The Science of Statistics. Types of Statistical Applications in Business. Fundamental Elements of Statistics. Processes (Optional). Types of Data. STATISTICS IN ACTION: Quality Improvement: U.S. Firms Respond to the Challenge from Japan. Collecting Data. The Role of Statistics in Managerial Decision-Making. STATISTICS IN ACTION: A 20/20 View of Survey Results: Fact or Fiction.

2. Methods for Describing Sets of Data.

Describing Qualitative Data. STATISTICS IN ACTION: Pareto Analysis. Graphical Methods for Describing Quantitative Data. The Time Series Plot (Optional). Summation Notation. Numerical Measures of Central Tendency. Numerical Measures of Variability. Interpreting the Standard Deviation. Numerical Measures of Relative Standing. Quartiles and Box Plots (Optional). Graphing Bivariate Relationships (Optional). Distorting the Truth with Descriptive Techniques. STATISTICS IN ACTION: Car and Driver's “Road Test Digest.” Quick Review. SHOWCASE: The Kentucky Milk Case — Part I. INTERNET LAB: Accessing and Summarizing Business and Economics Data Maintained by the U.S. Government.

3. Probability.

Events, Sample Spaces, and Probability. STATISTICS IN ACTION: Game Show Strategy: To Switch or Not to Switch. Unions and Intersections. Complementary Events. The Additive Rule and Mutually Exclusive Events. Conditional Probability. The Multiplicative Rule and Independent Events. Random Sampling. STATISTICS IN ACTION: Lottery Buster.

4. Discrete Random Variables.

Two Types of Random Variables. Probability Distributions for Discrete Random Variables. Expected Values of Discrete Random Variables. STATISTICS IN ACTION: Portfolio Selection. The Space Shuttle Challenger: Catastrophe in Space. The Binomial Random Variable. The Poisson Random Variable (Optional).

5. Continuous Random Variables.

Continuous Probability Distributions. The Uniform Distribution (Optional). The Normal Distribution. STATISTICS IN ACTION: IQ, Economic Mobility , and the Bell Curve Approximating a Binomial Distribution with a Normal Distribution. The Exponential Distribution (Optional). STATISTICS IN ACTION: Queuing Theory.

6. Sampling Distributions.

The Concept of Sampling Distributions. Properties of Sampling Distributions: Unbiased and Minimum Variance. STATISTICS IN ACTION: Reducing Investment Risk Through Diversification. The Sampling Distribution of the Sample Mean. STATISTICS IN ACTION: The Insomnia Pill. Quick Review. SHOWCASE: The Furniture Fire Case. INTERNET LAB: Analyzing Monthly Business Start-ups.

7. Inferences Based on a Single Sample: Estimation with Confidence Intervals.

Large-Sample Confidence Interval for a Population Mean. Small-Sample Confidence Interval for a Population Mean. STATISTICS IN ACTION: Scallops, Sampling, and the Law. Large-Sample Confidence Interval for a Population Proportion. Determining the Sample Size. STATISTICS IN ACTION: Is Caffeine Addictive? Finite Population Correction for Simple Random Sampling (Optional). Sample Survey Designs. STATISTICS IN ACTION: Sampling Error versus Nonsampling Error.

8. Inferences Based on a Single Sample: Tests of Hypothesis.

The Elements of a Test of Hypothesis. STATISTICS IN ACTION: Statistics Is Murder! Large-Sample Test of Hypothesis About a Population Mean. STATISTICS IN ACTION: Statistical Quality Control, Part I. Observed Significance Levels: p-values. Small Sample Test of Hypothesis About a Population Mean. Large-Sample Test of Hypothesis About a Population Proportion. STATISTICS IN ACTION: Statistical Quality Control, Part II. Calculating Type II Error Probabilities. More about b (Optional).

9. Inferences Based an Two Samples: Confidence Intervals and Tests of Hypotheses.

Comparing Two Population Means: Independent Sampling. STATISTICS IN ACTION: The Effect of Self-Managed Work Teams on Family Life. Comparing Two Population Means: Paired Difference Experiments. Comparing Two Population Proportions: Independent Sampling. Determining the Sample Size for Comparing Means and Proportions. STATISTICS IN ACTION: Unpaid Overtime and the Fair Labor Standards Act. Comparing Two Population Variances: Independent Sampling... SHOWCASE: The Kentucky Milk Case — Part II. INTERNET LAB: Choosing Between Economic Indicators.

10. Simple Linear Regression.

Probabilistic Models. Fitting the Model: The Least Squares Approach. Model Assumptions. An Estimator of o^2. Assessing the Utility of the Model: Making Inferences about the Slope b1. The Coefficient of Correlation. STATISTICS IN ACTION: New Jersey Banks — Serving Minorities? The Coefficient of Determination. Using the Model for Estimation and Prediction. STATISTICS IN ACTION: Statistical Assessment of Damage to Bronx Bricks. Simple Linear Regression: An Example.

11. Multiple Regression.

Multiple Regression: The Model and the Procedure. Fitting the Model: The Least Squares Approach. Model Assumptions. Inferences About the b Parameters. Checking the Usefulness of a Model: R^2 and the Analysis of Variance F-Test. Using the Model for Estimation and Prediction. Multiple Regression: An Example. Residual Analysis: Checking the Regression Assumptions. STATISTICS IN ACTION: Predicting the Price of Vintage Red Bordeaux Wine. Some Pitfalls: Estimability, Multicollinearity, and Extrapolation. STATISTICS IN ACTION: “Wringing” The Bell Curve.

12. Model Building.

Introduction. The Two Types of Independent Variables: Quantitative and Qualitative. Models with a Single Quantitative Independent Variable. Models with Two or More Quantitative Independent Variables. Testing Portions of a Model. Models with One Qualitative Independent Variable. Comparing the Slopes of Two or More Lines. Comparing Two or More Response Curves. STATISTICS IN ACTION: Forecasting Peak Hour Traffic Volume. Stepwise Regression. Quick Review. SHOWCASE: The Cando Sales Case. INTERNET LAB: Using the Consumer Price Index in Business Forecasts of Labor, Wages, and Compensation.

13. Methods for Quality Improvement.

Quality, Processes, and Systems. STATISTICS IN ACTION: Deming's 14 Points. Statistical Control. The Logic of Control Charts. A Control Chart for Monitoring the Mean of a Process: The x-Chart. A Control Chart for Monitoring the Variation of a Process: The R-Chart. A Control Chart for Monitoring the Proportion of Defectives Generated by a Process: The p-Chart. Diagnosing the Causes of Variation (Optional). STATISTICS IN ACTION: Quality Control in a Service Operation. Capability Analysis.

14. Time Series: Descriptive Analyses, Models, and Forecasting.

Descriptive Analysis: Index Numbers. STATISTICS IN ACTION: The Consumer Price Index: CPI-U and CPI-W. Descriptive Analysis: Exponential Smoothing. Time Series Components. Forecasting: Exponential Smoothing. Forecasting Trends: The Holt-Winters Model (Optional). Measuring Forecast Accuracy: MAD and RMSE. Forecasting Trends: Simple Linear Regression. STATISTICS IN ACTION: Forecasting the Demand for Emergency Room Services. Seasonal Regression Models. Autocorrelation and the Durbin-Watston test. Quick Review. SHOWCASE: The Gasket Manufacturing Case. INTERNET LAB: Quality Management Outside of the Manufacturing Operation.

15. Design of Experiments and Analysis of Variance.

Elements of a Designed Experiment. The Completely Randomized Design: Single Factor. Multiple Comparisons of Means. STATISTICS IN ACTION: Is Therapy the New “Diet Pill” for Binge Eaters? Factorial Experiments. STATISTICS IN ACTION: Improving a Ground Meat Canning Process Through Experimental Design. Using Regression for ANOVA (Optional).

16. Nonparametric Statistics.

Introduction. Single Population Inferences: The Sign Test. Comparing Two Populations: The Wilcoxon Rank Sum Test for Independent Samples. Comparing Two Populations: The Wilcoxon Signed Rank Test for the Paired Difference Experiment. STATISTICS IN ACTION: Reanalyzing the Scallop Weight Data. The Kruskal-Wallis H-Test for a Completely Randomized Design. STATISTICS IN ACTION: Taxpayers versus the IRS: Selecting the Trial Court. Spearman's Rank Correlation Coefficient.

17. The Chi-Square Test and the Analysis of Contingency Tables.

One-Dimensional Count Data: The Multinomial Distribution. Contingency Tables. STATISTICS IN ACTION: Ethics in Computer Technology and Use. A Word of Caution About Chi-Square Tests. SHOWCASE: Discrimination in the Workplace. INTERNET LAB: Sampling and Analyzing NYSE Stock Quotes.

18. Decision Analysis.

Introduction. Three Types of Decision Problems. Decision-Making Under Uncertainty. Basic Concepts. Two Ways of Expressing Outcomes: Payoffs and Opportunity Losses. Characterizing the Uncertainty in Decision-Problems. Solving the Decision Problem Using the Expected Payoff Criterion. STATISTICS IN ACTION: Evaluating Uncertainty in Research and Development. The Expected Utility Criterion. Classifying Decision-Makers by Their Utility Functions. Revising State of Nature Probabilities: Bayes' Rule. Solving Decision Problems Using Posterior Probalilitics. The Expected Value of Sample Information: Preposterior Analysis. STATISTICS IN ACTION: Hurricanes: To Seed or Not to Seed?

Appendix A: Basic Counting Rules.
Appendix B: Tables.
Appendix C: Calculation Formulas for Analysis of Variance. Answers to Selected Exercises.

References.

Index.