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Business Statistics: For Contemporary Decision Making, 7th Edition

Book Description

Black's latest outstanding pedagogy of Business Statistics includes the use of extra problems called "Demonstration Problems" to provide additional insight and explanation to working problems, and presents concepts, topics, formulas, and application in a manner that is palatable to a vast audience and minimizes the use of "scary" formulas.

Every chapter opens up with a vignette called a "Decision Dilemma" about real companies, data, and business issues. Solutions to these dilemmas are presented as a feature called "Decision Dilemma Solved." In this edition all cases and "Decision Dilemmas" are updated and revised and 1/3 have been replaced for currency. There is also a significant number of additional problems and an extremely competitive collection of databases (containing real data) on: international stock markets, consumer food, international labor, financial, energy, agribusiness, 12-year gasoline, manufacturing, and hospital.

Note: The ebook version does not provide access to the companion files.

Table of Contents

  1. Cover Page
  2. Title Page
  3. Copyright
  4. Dedication
  5. Brief Contents
  6. Contents
  7. PREFACE
    1. CHANGES FOR THE SEVENTH EDITION
    2. VIDEOTAPE TUTORIALS BY KEN BLACK
    3. FEATURES AND BENEFITS
    4. WILEYPLUS
    5. ANCILLARY TEACHING AND LEARNING MATERIALS
    6. ACKNOWLEDGMENTS
  8. About the Author
  9. UNIT I: INTRODUCTION
    1. CHAPTER 1: Introduction to Statistics
      1. Statistics Describe the State of Business in India's Countryside
      2. 1.1 STATISTICS IN BUSINESS
      3. 1.2 BASIC STATISTICAL CONCEPTS
      4. 1.3 VARIABLES AND DATA
      5. 1.4 DATA MEASUREMENT
      6. SUMMARY
      7. KEY TERMS
      8. SUPPLEMENTARY PROBLEMS
      9. ANALYZING THE DATABASES
      10. CASE: DIGIORNO PIZZA: INTRODUCING A FROZEN PIZZA TO COMPETE WITH CARRY-OUT
    2. CHAPTER 2: Charts and Graphs
      1. Container Shipping Companies
      2. 2.1 FREQUENCY DISTRIBUTIONS
      3. 2.2 QUANTITATIVE DATA GRAPHS
      4. 2.3 QUALITATIVE DATA GRAPHS
      5. 2.4 CHARTS AND GRAPHS FOR TWO VARIABLES
      6. SUMMARY
      7. KEY TERMS
      8. SUPPLEMENTARY PROBLEMS
      9. ANALYZING THE DATABASES
      10. CASE: SOAP COMPANIES DO BATTLE
      11. USING THE COMPUTER
    3. CHAPTER 3: Descriptive Statistics
      1. Laundry Statistics
      2. 3.1 MEASURES OF CENTRAL TENDENCY: UNGROUPED DATA
      3. 3.2 MEASURES OF VARIABILITY: UNGROUPED DATA
      4. 3.3 MEASURES OF CENTRAL TENDENCY AND VARIABILITY: GROUPED DATA
      5. 3.4 MEASURES OF SHAPE
      6. 3.5 DESCRIPTIVE STATISTICS ON THE COMPUTER
      7. SUMMARY
      8. KEY TERMS
      9. FORMULAS
      10. SUPPLEMENTARY PROBLEMS
      11. ANALYZING THE DATABASES
      12. CASE: COCA-COLA DEVELOPS THE AFRICAN MARKET
      13. USING THE COMPUTER
    4. CHAPTER 4: Probability
      1. Equity of the Sexes in the Workplace
      2. 4.1 INTRODUCTION TO PROBABILITY
      3. 4.2 METHODS OF ASSIGNING PROBABILITIES
      4. 4.3 STRUCTURE OF PROBABILITY
      5. 4.4 MARGINAL, UNION, JOINT, AND CONDITIONAL PROBABILITIES
      6. 4.5 ADDITION LAWS
      7. 4.6 MULTIPLICATION LAWS
      8. 4.7 CONDITIONAL PROBABILITY
      9. 4.8 REVISION OF PROBABILITIES: BAYES' RULE
      10. SUMMARY
      11. KEY TERMS
      12. FORMULAS
      13. SUPPLEMENTARY PROBLEMS
      14. ANALYZING THE DATABASES
      15. CASE: COLGATE-PALMOLIVE MAKES A “TOTAL” EFFORT
  10. UNIT II: DISTRIBUTIONS AND SAMPLING
    1. CHAPTER 5: Discrete Distributions
      1. Life with a Cell Phone
      2. 5.1 DISCRETE VERSUS CONTINUOUS DISTRIBUTIONS
      3. 5.2 DESCRIBING A DISCRETE DISTRIBUTION
      4. 5.3 BINOMIAL DISTRIBUTION
      5. 5.4 POISSON DISTRIBUTION
      6. 5.5 HYPERGEOMETRIC DISTRIBUTION
      7. SUMMARY
      8. KEY TERMS
      9. FORMULAS
      10. SUPPLEMENTARY PROBLEMS
      11. ANALYZING THE DATABASES
      12. CASE: WHOLE FOODS MARKET GROWS THROUGH MERGERS AND ACQUISITIONS
      13. USING THE COMPUTER
    2. CHAPTER 6: Continuous Distributions
      1. The Cost of Human Resources
      2. 6.1 THE UNIFORM DISTRIBUTION
      3. 6.2 NORMAL DISTRIBUTION
      4. 6.3 USING THE NORMAL CURVE TO APPROXIMATE BINOMIAL DISTRIBUTION PROBLEMS
      5. 6.4 EXPONENTIAL DISTRIBUTION
      6. SUMMARY
      7. KEY TERMS
      8. FORMULAS
      9. SUPPLEMENTARY PROBLEMS
      10. ANALYZING THE DATABASES
      11. CASE: MERCEDES GOES AFTER YOUNGER BUYERS
      12. USING THE COMPUTER
    3. CHAPTER 7: Sampling and Sampling Distributions
      1. What Is the Attitude of Maquiladora Workers?
      2. 7.1 SAMPLING
      3. 7.2 SAMPLING DISTRIBUTION OF
      4. 7.3 SAMPLING DISTRIBUTION OF
      5. SUMMARY
      6. KEY TERMS
      7. FORMULAS
      8. SUPPLEMENTARY PROBLEMS
      9. ANALYZING THE DATABASES
      10. CASE: SHELL ATTEMPTS TO RETURN TO PREMIERE STATUS
      11. USING THE COMPUTER
  11. UNIT III: MAKING INFERENCES ABOUT POPULATION PARAMETERS
    1. CHAPTER 8: Statistical Inference: Estimation for Single Populations
      1. Compensation for Purchasing Managers
      2. 8.1 ESTIMATING THE POPULATION MEAN USING THE z STATISTIC (σ KNOWN)
      3. 8.2 ESTIMATING THE POPULATION MEAN USING THE t STATISTIC (σ UNKNOWN)
      4. 8.3 ESTIMATING THE POPULATION PROPORTION
      5. 8.4 ESTIMATING THE POPULATION VARIANCE
      6. 8.5 ESTIMATING SAMPLE SIZE
      7. SUMMARY
      8. KEY TERMS
      9. FORMULAS
      10. SUPPLEMENTARY PROBLEMS
      11. ANALYZING THE DATABASES
      12. CASE: THE CONTAINER STORE
      13. USING THE COMPUTER
    2. CHAPTER 9: Statistical Inference: Hypothesis Testing for Single Populations
      1. Word-of-Mouth Business Referrals and Influentials
      2. 9.1 INTRODUCTION TO HYPOTHESIS TESTING
      3. 9.2 TESTING HYPOTHESES ABOUT A POPULATION MEAN USING THE z STATISTIC (σ KNOWN)
      4. 9.3 TESTING HYPOTHESES ABOUT A POPULATION MEAN USING THE t STATISTIC (σ UNKNOWN)
      5. 9.4 TESTING HYPOTHESES ABOUT A PROPORTION
      6. 9.5 TESTING HYPOTHESES ABOUT A VARIANCE
      7. 9.6 SOLVING FOR TYPE II ERRORS
      8. SUMMARY
      9. KEY TERMS
      10. FORMULAS
      11. SUPPLEMENTARY PROBLEMS
      12. ANALYZING THE DATABASES
      13. CASE: FRITO-LAY TARGETS THE HISPANIC MARKET
      14. USING THE COMPUTER
    3. CHAPTER 10: Statistical Inferences about Two Populations
      1. 10.1 HYPOTHESIS TESTING AND CONFIDENCE INTERVALS ABOUT THE DIFFERENCE IN TWO MEANS USING THE z STATISTIC (POPULATION VARIANCES KNOWN)
      2. 10.2 HYPOTHESIS TESTING AND CONFIDENCE INTERVALS ABOUT THE DIFFERENCE IN TWO MEANS: INDEPENDENT SAMPLES AND POPULATION VARIANCES UNKNOWN
      3. 10.3 STATISTICAL INFERENCES FOR TWO RELATED POPULATIONS
      4. 10.4 STATISTICAL INFERENCES ABOUT TWO POPULATION PROPORTIONS, p 1 – p 2
      5. 10.5 TESTING HYPOTHESES ABOUT TWO POPULATION VARIANCES
      6. SUMMARY
      7. KEY TERMS
      8. FORMULAS
      9. SUPPLEMENTARY PROBLEMS
      10. ANALYZING THE DATABASES
      11. CASE: SEITZ CORPORATION: PRODUCING QUALITY GEAR-DRIVEN AND LINEAR-MOTION PRODUCTS
      12. USING THE COMPUTER
    4. CHAPTER 11: Analysis of Variance and Design of Experiments
      1. Job and Career Satisfaction of Foreign Self-Initiated Expatriates
      2. 11.1 INTRODUCTION TO DESIGN OF EXPERIMENTS
      3. 11.2 THE COMPLETELY RANDOMIZED DESIGN (ONE-WAY ANOVA)
      4. 11.3 MULTIPLE COMPARISON TESTS
      5. 11.4 THE RANDOMIZED BLOCK DESIGN
      6. 11.5 A FACTORIAL DESIGN (TWO-WAY ANOVA)
      7. SUMMARY
      8. KEY TERMS
      9. FORMULAS
      10. SUPPLEMENTARY PROBLEMS
      11. ANALYZING THE DATABASES
      12. CASE: THE CLARKSON COMPANY: A DIVISION OF TYCO INTERNATIONAL
      13. USING THE COMPUTER
  12. UNIT IV: REGRESSION ANALYSIS AND FORECASTING
    1. CHAPTER 12: Simple Regression Analysis and Correlation
      1. Predicting International Hourly Wages by the Price of a Big Mac
      2. 12.1 CORRELATION
      3. 12.2 INTRODUCTION TO SIMPLE REGRESSION ANALYSIS
      4. 12.3 DETERMINING THE EQUATION OF THE REGRESSION LINE
      5. 12.4 RESIDUAL ANALYSIS
      6. 12.5 STANDARD ERROR OF THE ESTIMATE
      7. 12.6 COEFFICIENT OF DETERMINATION
      8. 12.7 HYPOTHESIS TESTS FOR THE SLOPE OF THE REGRESSION MODEL AND TESTING THE OVERALL MODEL
      9. 12.8 ESTIMATION
      10. 12.9 USING REGRESSION TO DEVELOP A FORECASTING TREND LINE
      11. 12.10 INTERPRETING THE OUTPUT
      12. SUMMARY
      13. KEY TERMS
      14. FORMULAS
      15. SUPPLEMENTARY PROBLEMS
      16. ANALYZING THE DATABASES
      17. CASE: DELTA WIRE USES TRAINING AS A WEAPON
      18. USING THE COMPUTER
    2. CHAPTER 13: Multiple Regression Analysis
      1. Are You Going to Hate Your New Job?
      2. 13.1 THE MULTIPLE REGRESSION MODEL
      3. 13.2 SIGNIFICANCE TESTS OF THE REGRESSION MODEL AND ITS COEFFICIENTS
      4. 13.3 RESIDUALS, STANDARD ERROR OF THE ESTIMATE, AND R 2
      5. 13.4 INTERPRETING MULTIPLE REGRESSION COMPUTER OUTPUT
      6. SUMMARY
      7. KEY TERMS
      8. FORMULAS
      9. SUPPLEMENTARY PROBLEMS
      10. ANALYZING THE DATABASES
      11. CASE: STARBUCKS INTRODUCES DEBIT CARD
      12. USING THE COMPUTER
    3. CHAPTER 14: Building Multiple Regression Models
      1. Determining Compensation for CEOs
      2. 14.1 NONLINEAR MODELS: MATHEMATICAL TRANSFORMATION
      3. 14.2 INDICATOR (DUMMY) VARIABLES
      4. 14.3 MODEL-BUILDING: SEARCH PROCEDURES
      5. 14.4 MULTICOLLINEARITY
      6. 14.5 LOGISTIC REGRESSION
      7. SUMMARY
      8. KEY TERMS
      9. FORMULAS
      10. SUPPLEMENTARY PROBLEMS
      11. ANALYZING THE DATABASES
      12. CASE: VIRGINIA SEMICONDUCTOR
      13. USING THE COMPUTER
    4. CHAPTER 15: Time-Series Forecasting and Index Numbers
      1. Forecasting Air Pollution
      2. 15.1 INTRODUCTION TO FORECASTING
      3. 15.2 SMOOTHING TECHNIQUES
      4. 15.3 TREND ANALYSIS
      5. 15.4 SEASONAL EFFECTS
      6. 15.5 AUTOCORRELATION AND AUTOREGRESSION
      7. 15.6 INDEX NUMBERS
      8. SUMMARY
      9. KEY TERMS
      10. FORMULAS
      11. SUPPLEMENTARY PROBLEMS
      12. ANALYZING THE DATABASES
      13. CASE: DEBOURGH MANUFACTURING COMPANY
      14. USING THE COMPUTER
  13. UNIT V: NONPARAMETRIC STATISTICS AND QUALITY
    1. CHAPTER 16: Analysis of Categorical Data
      1. Selecting Suppliers in the Electronics Industry
      2. 16.1 CHI-SQUARE GOODNESS-OF-FIT TEST
      3. 16.2 CONTINGENCY ANALYSIS: CHI-SQUARE TEST OF INDEPENDENCE
      4. SUMMARY
      5. KEY TERMS
      6. FORMULAS
      7. SUPPLEMENTARY PROBLEMS
      8. ANALYZING THE DATABASE
      9. CASE: FOOT LOCKER IN THE SHOE MIX
      10. USING THE COMPUTER
    2. CHAPTER 17: Nonparametric Statistics
      1. How Is the Doughnut Business?
      2. 17.1 RUNS TEST
      3. 17.2 MANN-WHITNEY U TEST
      4. 17.3 WILCOXON MATCHED-PAIRS SIGNED RANK TEST
      5. 17.4 KRUSKAL-WALLIS TEST
      6. 17.5 FRIEDMAN TEST
      7. 17.6 SPEARMAN'S RANK CORRELATION
      8. SUMMARY
      9. KEY TERMS
      10. FORMULAS
      11. SUPPLEMENTARY PROBLEMS
      12. ANALYZING THE DATABASES
      13. CASE: SCHWINN
      14. USING THE COMPUTER
    3. CHAPTER 18: Statistical Quality Control
      1. Italy's Piaggio Makes a Comeback
      2. 18.1 INTRODUCTION TO QUALITY CONTROL
      3. 18.2 PROCESS ANALYSIS
      4. 18.3 CONTROL CHARTS
      5. SUMMARY
      6. KEY TERMS
      7. FORMULAS
      8. SUPPLEMENTARY PROBLEMS
      9. ANALYZING THE DATABASES
      10. CASE: ROBOTRON-ELOTHERM
      11. USING THE COMPUTER
  14. APPENDIX A: Tables
  15. APPENDIX B: Answers to Selected Odd-Numbered Quantitative Problems
  16. GLOSSARY
  17. INDEX
  18. CHAPTER 19: Decision Analysis
    1. Decision Making at the CEO Level
    2. 19.1 THE DECISION TABLE AND DECISION MAKING UNDER CERTAINTY
    3. 19.2 DECISION MAKING UNDER UNCERTAINTY
    4. 19.3 DECISION MAKING UNDER RISK
    5. 19.4 REVISING PROBABILITIES IN LIGHT OF SAMPLE INFORMATION
    6. SUMMARY
    7. KEY TERMS
    8. FORMULA
    9. SUPPLEMENTARY PROBLEMS
    10. ANALYZING THE DATABASES
    11. CASE: FLETCHER-TERRY: ON THE CUTTING EDGE
  19. SUPPLEMENT 1: SUMMATION NOTATION
  20. SUPPLEMENT 2: DERIVATION OF SIMPLE REGRESSION FORMULAS FOR SLOPE AND Y INTERCEPT
  21. SUPPLEMENT 3: ADVANCED EXPONENTIAL SMOOTHING
    1. EXPONENTIAL SMOOTHING WITH TREND EFFECTS:: HOLT's METHOD
    2. EXPONENTIAL SMOOTHING WITH BOTH TREND AND SEASONALITY:: WINTER's METHOD
    3. SOME PRACTICE PROBLEMS