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Statistical Thinking: Improving Business Performance, Second Edition

Book Description

How statistical thinking and methodology can help you make crucial business decisions

Straightforward and insightful, Statistical Thinking: Improving Business Performance, Second Edition, prepares you for business leadership by developing your capacity to apply statistical thinking to improve business processes. Unique and compelling, this book shows you how to derive actionable conclusions from data analysis, solve real problems, and improve real processes. Here, you'll discover how to implement statistical thinking and methodology in your work to improve business performance.

  • Explores why statistical thinking is necessary and helpful

  • Provides case studies that illustrate how to integrate several statistical tools into the decision-making process

  • Facilitates and encourages an experiential learning environment to enable you to apply material to actual problems

With an in-depth discussion of JMP® software, the new edition of this important book focuses on skills to improve business processes, including collecting data appropriate for a specified purpose, recognizing limitations in existing data, and understanding the limitations of statistical analyses.

Table of Contents

  1. Cover
  2. Series
  3. Title Page
  4. Copyright
  5. Dedication
  6. Preface
  7. Introduction to JMP
    1. WHY JMP?
    2. JMP MENUS
    3. IMPORTING DATA
    4. THE JMP DATA TABLE
    5. THE ANALYZE MENU
    6. JMP DIALOG WINDOWS
    7. THE GRAPH MENU
    8. THE DOE MENU
    9. THE TOOLS MENU
    10. USING JMP
  8. Part One: Statistical Thinking Concepts
    1. Chapter 1: Need for Business Improvement
      1. TODAY’S BUSINESS REALITIES AND THE NEED TO IMPROVE
      2. WE NOW HAVE TWO JOBS: A MODEL FOR BUSINESS IMPROVEMENT
      3. NEW MANAGEMENT APPROACHES REQUIRE STATISTICAL THINKING
      4. PRINCIPLES OF STATISTICAL THINKING
      5. APPLICATIONS OF STATISTICAL THINKING
      6. SUMMARY
      7. NOTES
    2. Chapter 2: Statistical Thinking Strategy
      1. CASE STUDY: THE EFFECT OF ADVERTISING ON SALES
      2. CASE STUDY: IMPROVEMENT OF A SOCCER TEAM’S PERFORMANCE
      3. STATISTICAL THINKING STRATEGY
      4. CONTEXT OF STATISTICAL THINKING: STATISTICS DISCIPLINE AS A SYSTEM
      5. VARIATION IN BUSINESS PROCESSES
      6. SYNERGY BETWEEN DATA AND SUBJECT MATTER KNOWLEDGE
      7. DYNAMIC NATURE OF BUSINESS PROCESSES
      8. SUMMARY
      9. PROJECT UPDATE
      10. NOTES
    3. Chapter 3: Understanding Business Processes
      1. EXAMPLES OF BUSINESS PROCESSES
      2. SIPOC MODEL FOR PROCESSES
      3. IDENTIFYING BUSINESS PROCESSES
      4. ANALYSIS OF BUSINESS PROCESSES
      5. SYSTEMS OF PROCESSES
      6. MEASUREMENT PROCESS
      7. SUMMARY
      8. PROJECT UPDATE
      9. NOTES
  9. Part Two: Statistical Engineering: Frameworks and Basic Tools
    1. Chapter 4: Statistical Engineering
      1. STATISTICAL ENGINEERING
      2. CASE STUDY: REDUCING RESIN OUTPUT VARIATION
      3. CASE STUDY: REDUCING TELEPHONE WAITING TIME AT A BANK
      4. BASIC PROCESS IMPROVEMENT FRAMEWORK
      5. CASE STUDY: RESOLVING CUSTOMER COMPLAINTS OF BABY WIPE FLUSHABILITY
      6. CASE STUDY: THE REALIZED REVENUE FIASCO
      7. BASIC PROBLEM-SOLVING FRAMEWORK
      8. DMAIC FRAMEWORK
      9. DMAIC CASE STUDY: NEWSPAPER ACCURACY
      10. SUMMARY
      11. PROJECT UPDATE
      12. NOTES
    2. Chapter 5: Process Improvement and Problem-Solving Tools
      1. STRATIFICATION
      2. DATA COLLECTION TOOLS
      3. BASIC GRAPHICAL ANALYSIS TOOLS
      4. KNOWLEDGE-BASED TOOLS
      5. PROCESS STABILITY AND CAPABILITY TOOLS
      6. SUMMARY
      7. PROJECT UPDATE
      8. NOTES
  10. Part Three: Formal Statistical Methods
    1. Chapter 6: Building and Using Models
      1. EXAMPLES OF BUSINESS MODELS
      2. TYPES AND USES OF MODELS
      3. REGRESSION MODELING PROCESS
      4. BUILDING MODELS WITH ONE PREDICTOR VARIABLE
      5. BUILDING MODELS WITH SEVERAL PREDICTOR VARIABLES
      6. MULTICOLLINEARITY: ANOTHER MODEL CHECK
      7. SOME LIMITATIONS OF USING EXISTING DATA
      8. SUMMARY
      9. PROJECT UPDATE
      10. NOTES
    2. Chapter 7: Using Process Experimentation to Build Models
      1. WHY DO WE NEED A STATISTICAL APPROACH?
      2. EXAMPLES OF PROCESS EXPERIMENTS
      3. STATISTICAL APPROACH TO EXPERIMENTATION
      4. TWO-FACTOR EXPERIMENTS: A CASE STUDY
      5. THREE-FACTOR EXPERIMENTS: A CASE STUDY
      6. LARGER EXPERIMENTS
      7. BLOCKING, RANDOMIZATION, AND CENTER POINTS
      8. SUMMARY
      9. PROJECT UPDATE
      10. NOTES
    3. Chapter 8: Applications of Statistical Inference Tools
      1. EXAMPLES OF STATISTICAL INFERENCE TOOLS
      2. PROCESS OF APPLYING STATISTICAL INFERENCE
      3. STATISTICAL CONFIDENCE AND PREDICTION INTERVALS
      4. STATISTICAL HYPOTHESIS TESTS
      5. TESTS FOR CONTINUOUS DATA
      6. TEST FOR DISCRETE DATA: COMPARING TWO OR MORE PROPORTIONS
      7. TEST FOR REGRESSION ANALYSIS: TEST ON A REGRESSION COEFFICIENT
      8. SAMPLE SIZE FORMULAS
      9. SUMMARY
      10. PROJECT UPDATE
      11. NOTES
    4. Chapter 9: Underlying Theory of Statistical Inference
      1. APPLICATIONS OF THE THEORY
      2. THEORETICAL FRAMEWORK OF STATISTICAL INFERENCE
      3. TYPES OF DATA
      4. PROBABILITY DISTRIBUTIONS
      5. SAMPLING DISTRIBUTIONS
      6. LINEAR COMBINATIONS
      7. TRANSFORMATIONS
      8. SUMMARY
      9. PROJECT UPDATE
      10. NOTES
    5. Chapter 10: Summary and Path Forward
      1. A PERSONAL CASE STUDY BY TOM POHLEN
      2. REVIEW OF THE STATISTICAL THINKING APPROACH
      3. TEXT SUMMARY
      4. POTENTIAL NEXT STEPS TO DEEPER UNDERSTANDING OF STATISTICAL THINKING
      5. PROJECT SUMMARY AND DEBRIEFING
      6. NOTES
  11. Appendix A: Effective Teamwork
    1. BENEFITS OF USING TEAMS
    2. WHEN TO USE A TEAM
    3. FORMING A TEAM
    4. SELECTING TEAM PROJECTS
    5. INGREDIENTS FOR A SUCCESSFUL TEAM
    6. STAGES OF TEAM GROWTH
    7. RUNNING EFFECTIVE MEETINGS
    8. DEALING WITH CONFLICT
    9. WHY PROJECT TEAMS FAIL
    10. NOTES
  12. Appendix B: Presentations and Report Writing
    1. PRESENTATIONS TO INDIVIDUALS OR SMALL GROUPS
    2. PRESENTATIONS OR PROJECT REVIEWS FOR LARGE GROUPS
    3. PITFALLS
    4. ONE-PARAGRAPH SUMMARY OR ABSTRACT
    5. WRITTEN REPORTS
    6. USE OF GRAPHICS
    7. PRESENTING STATISTICAL RESULTS
    8. PITFALLS
    9. NOTES
  13. Appendix C: More on Surveys
    1. WHAT IS A SURVEY?
    2. THEN, WHAT IS A SURVEY?
    3. HOW LARGE MUST THE SAMPLE SIZE BE?
    4. WHO CONDUCTS SURVEYS?
    5. WHAT ARE SOME COMMON SURVEY METHODS?
    6. WHAT SURVEY QUESTIONS DO YOU ASK?
    7. WHO WORKS ON SURVEYS?
    8. WHAT ABOUT CONFIDENTIALITY AND INTEGRITY?
    9. WHAT ARE OTHER POTENTIAL CONCERNS?
    10. WHERE CAN I GET MORE INFORMATION?
    11. NOTE
  14. Appendix D: More on Regression
    1. MY REGRESSION MODEL HAS A LOW ADJUSTED R-SQUARED VALUE—NOW WHAT DO I DO?
    2. DEALING WITH OUTLIERS
    3. REGRESSION MODELING WHEN SOME OR ALL OF THE VARIABLES (x’S) ARE QUALITATIVE
    4. DEVELOPING TRADE-OFFS AMONG MULTIPLE RESPONSES
    5. MODEL VERIFICATION
    6. NOTES
  15. Appendix E: More on Design of Experiments
    1. ANALYSIS OF TWO-LEVEL EXPERIMENTS
    2. ANALYSIS OF MIXED-LEVEL FACTORIAL EXPERIMENTS
    3. RESPONSE SURFACE EXPERIMENTS
    4. NOTES
  16. Appendix F: More on Inference Tools
    1. NOTES
  17. Appendix G: More on Probability Distributions
    1. DISCRETE PROBABILITY DISTRIBUTIONS
    2. CONTINUOUS PROBABILITY DISTRIBUTIONS
    3. NOTES
  18. Appendix H: Process Design (Reengineering)
    1. WHY DESIGN?
    2. MEASURES OF SUCCESS
    3. CONCEPTUAL DESIGN
    4. DETAILED DESIGN
    5. TRUST BUT VERIFY!
    6. IMPLEMENTATION AND FOLLOW-UP
    7. SUMMARY
    8. NOTES
  19. Appendix I: t Critical Values
  20. Appendix J: Standard Normal Probabilities (Cumulative z Curve Areas)
  21. Index