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Business Statistics: For Contemporary Decision Making, 8th Edition

Book Description

This text is an unbound, binder-ready edition.

Business Statistics: For Contemporary Decision Making, 8th Edition continues the tradition of presenting and explaining the wonders of business statistics through the use of clear, complete, student-friendly pedagogy. Ken Black's text equips readers with the quantitative decision-making skills and analysis techniques you need to make smart decisions based on real-world data.

Table of Contents

  1. Cover Page
  2. Dedication
  3. Copyright
  4. Title Page
  5. 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 www.wiley.com/college/black
    6. ACKNOWLEDGMENTS
  8. About the Author
  9. UNIT I: INTRODUCTION
    1. CHAPTER 1: Introduction to Statistics
      1. 1.1 STATISTICS IN BUSINESS
      2. 1.2 BASIC STATISTICAL CONCEPTS
      3. 1.3 VARIABLES AND DATA
      4. 1.4 DATA MEASUREMENT
      5. DIGIORNO PIZZA: INTRODUCING A FROZEN PIZZA TO COMPETE WITH CARRY-OUT
    2. CHAPTER 2: Charts and Graphs
      1. 2.1 FREQUENCY DISTRIBUTIONS
      2. 2.2 QUANTITATIVE DATA GRAPHS
      3. 2.3 QUALITATIVE DATA GRAPHS
      4. 2.4 CHARTS AND GRAPHS FOR TWO VARIABLES
      5. SOAP COMPANIES DO BATTLE
    3. CHAPTER 3: Descriptive Statistics
      1. 3.1 MEASURES OF CENTRAL TENDENCY: UNGROUPED DATA
      2. 3.2 MEASURES OF VARIABILITY: UNGROUPED DATA
      3. 3.3 MEASURES OF CENTRAL TENDENCY AND VARIABILITY: GROUPED DATA
      4. 3.4 MEASURES OF SHAPE
      5. 3.5 DESCRIPTIVE STATISTICS ON THE COMPUTER
      6. COCA-COLA DEVELOPS THE AFRICAN MARKET
    4. CHAPTER 4: Probability
      1. 4.1 INTRODUCTION TO PROBABILITY
      2. 4.2 METHODS OF ASSIGNING PROBABILITIES
      3. 4.3 STRUCTURE OF PROBABILITY
      4. 4.4 MARGINAL, UNION, JOINT, AND CONDITIONAL PROBABILITIES
      5. 4.5 ADDITION LAWS
      6. 4.6 MULTIPLICATION LAWS
      7. 4.7 CONDITIONAL PROBABILITY
      8. 4.8 REVISION OF PROBABILITIES: BAYES' RULE
      9. COLGATE-PALMOLIVE MAKES A “TOTAL” EFFORT
  10. UNIT II: DISTRIBUTIONS AND SAMPLING
    1. CHAPTER 5: Discrete Distributions
      1. 5.1 DISCRETE VERSUS CONTINUOUS DISTRIBUTIONS
      2. 5.2 DESCRIBING A DISCRETE DISTRIBUTION
      3. 5.3 BINOMIAL DISTRIBUTION
      4. 5.4 POISSION DISTRIBUTION
      5. 5.5 HYPERGEOMETRIC DISTRIBUTION
      6. WHOLE FOODS MARKET GROWS THROUGH MERGERS AND ACQUISITIONS
    2. CHAPTER 6: Continuous Distributions
      1. 6.1 THE UNIFORM DISTRIBUTION
      2. 6.2 NORMAL DISTRIBUTION
      3. 6.3 USING THE NORMAL CURVE TO APPROXIMATE BINOMIAL DISTRIBUTION PROBLEMS
      4. 6.4 EXPONENTIAL DISTRIBUTION
      5. MERCEDES GOES AFTER YOUNGER BUYERS
    3. CHAPTER 7: Sampling and Sampling Distributions
      1. 7.1 SAMPLING
      2. 7.2 SAMPLING DISTRIBUTION OF
      3. 7.3 SAMPLING DISTRIBUTION OF
      4. SHELL ATTEMPTS TO RETURN TO PREMIERE STATUS
  11. UNIT III: MAKING INFERENCES ABOUT POPULATION PARAMETERS
    1. CHAPTER 8: Statistical Inference: Estimation for Single Populations
      1. 8.1 ESTIMATING THE POPULATION MEAN USING THE z STATISTIC ( σ KNOWN)
      2. 8.2 ESTIMATING THE POPULATION MEAN USING THE t STATISTIC ( σ UNKNOWN)
      3. 8.3 ESTIMATING THE POPULATION PROPORTION
      4. 8.4 ESTIMATING THE POPULATION VARIANCE
      5. 8.5 ESTIMATING SAMPLE SIZE
      6. THE CONTAINER STORE
    2. CHAPTER 9: Statistical Inference: Hypothesis Testing for Single Populations
      1. 9.1 INTRODUCTION TO HYPOTHESIS TESTING
      2. 9.2 TESTING HYPOTHESES ABOUT A POPULATION MEAN USING THE z STATISTIC ( σ KNOWN)
      3. 9.3 TESTING HYPOTHESES ABOUT A POPULATION MEAN USING THE t STATISTIC (σ UNKNOWN)
      4. 9.4 TESTING HYPOTHESES ABOUT A PROPORTION
      5. 9.5 TESTING HYPOTHESES ABOUT A VARIANCE
      6. 9.6 SOLVING FOR TYPE II ERRORS
      7. FRITO-LAY TARGETS THE HISPANIC MARKET
    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. SEITZ CORPORATION: PRODUCING QUALITY GEAR-DRIVEN AND LINEAR-MOTION PRODUCTS
    4. CHAPTER 11: Analysis of Variance and Design of Experiments
      1. 11.1 INTRODUCTION TO DESIGN OF EXPERIMENTS
      2. 11.2 THE COMPLETELY RANDOMIZED DESIGN (ONE-WAY ANOVA)
      3. 11.3 MULTIPLE COMPARISON TESTS
      4. 11.4 THE RANDOMIZED BLOCK DESIGN
      5. 11.5 A FACTORIAL DESIGN (TWO-WAY ANOVA)
      6. THE CLARKSON COMPANY: A DIVISION OF TYCO INTERNATIONAL
  12. UNIT IV: REGRESSION ANALYSIS AND FORECASTING
    1. CHAPTER 12: Simple Regression Analysis and Correlation
      1. 12.1 CORRELATION
      2. 12.2 INTRODUCTION TO SIMPLE REGRESSION ANALYSIS
      3. 12.3 DETERMINING THE EQUATION OF THE REGRESSION LINE
      4. 12.4 RESIDUAL ANALYSIS
      5. 12.5 STANDARD ERROR OF THE ESTIMATE
      6. 12.6 COEFFICIENT OF DETERMINATION
      7. 12.7 HYPOTHESIS TESTS FOR THE SLOPE OF THE REGRESSION MODEL AND TESTING THE OVERALL MODEL
      8. 12.8 ESTIMATION
      9. 12.9 USING REGRESSION TO DEVELOP A FORECASTING TREND LINE
      10. 12.10 INTERPRETING THE OUTPUT
      11. DELTA WIRE USES TRAINING AS A WEAPON
    2. CHAPTER 13: Multiple Regression Analysis
      1. 13.1 THE MULTIPLE REGRESSION MODEL
      2. 13.2 SIGNIFICANCE TESTS OF THE REGRESSION MODEL AND ITS COEFFICIENTS
      3. 13.3 RESIDUALS, STANDARD ERROR OF THE ESTIMATE, AND R 2
      4. 13.4 INTERPRETING MULTIPLE REGRESSION COMPUTER OUTPUT
      5. STARBUCKS INTRODUCES DEBIT CARD
    3. CHAPTER 14: Building Multiple Regression Models
      1. 14.1 NONLINEAR MODELS: MATHEMATICAL TRANSFORMATION
      2. 14.2 INDICATOR (DUMMY) VARIABLES
      3. 14.3 MODEL-BUILDING: SEARCH PROCEDURES
      4. 14.4 MULTICOLLINEARITY
      5. 14.5 LOGISTIC REGRESSION
      6. VIRGINIA SEMICONDUCTOR
    4. CHAPTER 15: Time-Series Forecasting and Index Numbers
      1. 15.1 INTRODUCTION TO FORECASTING
      2. 15.2 SMOOTHING TECHNIQUES
      3. 15.3 TREND ANALYSIS
      4. 15.4 SEASONAL EFFECTS
      5. 15.5 AUTOCORRELATION AND AUTOREGRESSION
      6. 15.6 INDEX NUMBERS
      7. DEBOURGH MANUFACTURING COMPANY
  13. UNIT V: NONPARAMETRIC STATISTICS AND QUALITY
    1. CHAPTER 16: Analysis of Categorical Data
      1. 16.1 CHI-SQUARE GOODNESS-OF-FIT TEST
      2. 16.2 CONTINGENCY ANALYSIS: CHI-SQUARE TEST OF INDEPENDENCE
      3. FOOT LOCKER IN THE SHOE MIX
    2. CHAPTER 17: Nonparametric Statistic
      1. 17.1 RUNS TEST
      2. 17.2 MANN-WHITNEY U TEST
      3. 17.3 WILCOXON MATCHED-PAIRS SIGNED RANK TEST
      4. 17.4 KRUSKAL-WALLIS TEST
      5. 17.5 FRIEDMAN TEST
      6. 17.6 SPEARMAN'S RANK CORRELATION
      7. SCHWINN
    3. CHAPTER 18: Statistical Quality Control
      1. 18.1 INTRODUCTION TO QUALITY CONTROL
      2. 18.2 PROCESS ANALYSIS
      3. 18.3 CONTROL CHARTS
      4. ROBOTRON-ELOTHERM
    4. APPENDIX A: Tables
    5. APPENDIX B: Answers to Selected Odd-Numbered Quantitative Problems
  14. GLOSSARY
  15. INDEX