Intro -- Statistical Thinking -- Contents -- Preface -- Introduction to JMP -- Why JMP? -- JMP Menus -- Importing Data -- The JMP Data Table -- The Analyze Menu -- JMP Dialog Windows -- The Graph Menu -- The DOE Menu -- The Tools Menu -- Using JMP -- Part One Statistical Thinking Concepts -- Chapter 1 Need for Business Improvement -- Today's Business Realities and the Need to Improve -- We Now Have Two Jobs: A Model for Business Improvement -- New Management Approaches Require Statistical Thinking -- Principles of Statistical Thinking -- Applications of Statistical Thinking -- Summary -- Notes -- Chapter 2 Statistical Thinking Strategy -- Case Study: The Effect of Advertising on Sales -- The First Experiment -- The Second Experiment -- Refining the Research Hypothesis -- Research Outcomes -- Summary -- Case Study: Improvement of a Soccer Team's Performance -- Background -- Overall Approach -- Getting Started -- First Round of Data Collection -- Second and Third Sets of Defensive Data -- Offensive Skills -- Next Round on Offense -- Summary -- Statistical Thinking Strategy -- Commonality of Approach -- Statistical Thinking Strategy -- Context of Statistical Thinking: Statistics Discipline as a System -- Variation in Business Processes -- Synergy between Data and Subject Matter Knowledge -- Dynamic Nature of Business Processes -- Summary -- Project Update -- Notes -- Chapter 3 Understanding Business Processes -- Examples of Business Processes -- SIPOC Model for Processes -- Identifying Business Processes -- Analysis of Business Processes -- Non-Value-Added Work -- Process Complexity -- The Hidden Plant -- Process Measurements -- Benchmarking -- Systems of Processes -- Measurement Process -- Summary -- Project Update -- Notes -- Part Two Statistical Engineering: Frameworks and Basic Tools.

Chapter 4 Statistical Engineering Tactics to Deploy Statistical Thinking -- Statistical Engineering -- Case Study: Reducing Resin Output Variation -- Case Study: Reducing Telephone Waiting Time at a Bank -- Basic Process Improvement Framework -- Case Study: Resolving Customer Complaints of Baby Wipe Flushability -- Case Study: The Realized Revenue Fiasco -- Basic Problem-Solving Framework -- DMAIC Framework -- DMAIC Case Study: Newspaper Accuracy -- Introduction -- Define -- Measure -- Analyze -- Improve -- Control -- Results -- Summary -- Project Update -- Notes -- Chapter 5 Process Improvement and Problem-Solving Tools -- Stratification -- Data Collection Tools -- Checksheet -- Survey -- Practical Sampling Tips -- Basic Graphical Analysis Tools -- Basic Graphs for One Variable: Run Chart (Time Plot) -- Basic Graphs for One Variable: Pareto Chart -- Basic Graphs for One Variable: Histogram -- Basic Graphs for Two or More Variables: Box Plot -- Basic Graphs for Two or More Variables: Scatter Plot -- Knowledge-Based Tools -- Documenting the Process Flow: Flowchart -- Tools for Identifying and Processing Ideas: Brainstorming -- Tools for Identifying and Processing Ideas: Affinity Diagram -- Tools for Identifying and Processing Ideas: Interrelationship Digraph -- Tools for Identifying and Processing Ideas: Multivoting -- Tools for Understanding Causal Relationships: Cause-and-Effect Diagram -- Tools for Understanding Causal Relationships: Cause-and-Effect Matrix -- Tools for Understanding Causal Relationships: Failure Mode and Effects Analysis -- Tools for Understanding Causal Relationships: Five Whys -- Tools for Understanding Causal Relationships: Is-Is Not Analysis -- Process Stability and Capability Tools -- Stability: Control Chart -- Capability: Capability Analysis -- Summary -- Project Update -- Notes -- Part Three Formal Statistical Methods.

Chapter 6 Building and Using Models -- Examples of Business Models -- Types and Uses of Models -- Uses of Models -- Regression Modeling Process -- Multiple Predictor Variables -- A Method for Building Regression Models -- Least Squares -- Building Models with One Predictor Variable -- Step 1. Get to Know Your Data -- Step 2. Formulate the Model -- Step 3. Fit the Model to the Data -- Step 4. Check the Model Fit -- Step 5. Report and Use the Model -- Extrapolation Can Be Like Skating on Thin Ice -- Building Models with Several Predictor Variables -- Step 1. Get to Know Your Data -- Step 2. Formulate the Model -- Step 3. Fit the Model to the Data -- Step 4. Check the Model Fit -- Step 5. Report and Use the Model -- Multicollinearity: Another Model Check -- Some Limitations of Using Existing Data -- Summary -- Project Update -- Notes -- Chapter 7 Using Process Experimentation to Build Models -- Why Do We Need a Statistical Approach? -- Haphazard Experimentation -- One-Factor-at-a-Time Experimentation -- The Statistical Approach -- Examples of Process Experiments -- Effect of Advertising on Sales -- Product Development Case Study -- Reducing Defects in Plastic Parts Case Study -- Statistical Approach to Experimentation -- Planning Test Programs -- Designing the Experiment -- Two-Factor Experiments: A Case Study -- Interaction between Factors -- Regression Analysis of Two-Level Designs -- Three-Factor Experiments: A Case Study -- Designing the Experiment -- Analysis of Results -- Importance of the Factor Effects -- Efficiency and Hidden Replication -- Larger Experiments -- Blocking, Randomization, and Center Points -- Summary -- Project Update -- Notes -- Chapter 8 Applications of Statistical Inference Tools -- Examples of Statistical Inference Tools -- Process of Applying Statistical Inference -- Statistical Confidence and Prediction Intervals.

Confidence Interval for the Average -- Prediction Interval for One Observation -- Confidence Interval for the Proportion -- Confidence Interval for the Standard Deviation -- Confidence Interval for a Regression Coefficient -- Prediction Interval for Future y Values Using a Regression Equation -- Confidence Interval for the Difference between Two Averages -- Confidence Interval for the Difference between Two Proportions -- Statistical Hypothesis Tests -- Hypothesis Testing Process -- Connection to Confidence Intervals -- Stating the Hypotheses -- Obtaining the Data -- Evaluating the Consistency between the Data and the Null Hypothesis -- Rejecting or Failing to Reject -- Tests for Continuous Data -- Test for One Average -- Test for Comparing Two Averages -- Test for Comparing Several Averages -- Test for Comparing Two Variances (Standard Deviations) -- Test for Comparing Several Variances (Standard Deviations) -- Test for Discrete Data: Comparing Two or More Proportions -- Test for Regression Analysis: Test on a Regression Coefficient -- Sample Size Formulas -- Sampling from an Infinite Population -- Sampling from Finite Populations -- Sample Sizes for Hypothesis Tests -- Summary -- Project Update -- Notes -- Chapter 9 Underlying Theory of Statistical Inference -- Applications of the Theory -- Theoretical Framework of Statistical Inference -- Types of Data -- Nominal Data -- Ordinal Data -- Integer Data -- Continuous Data -- Probability Distributions -- Discrete Distributions -- Continuous Distributions -- Sampling Distributions -- Sample Average -- Central Limit Theorem -- Sample Variance (Standard Deviation) -- t-Distribution -- Linear Combinations -- Transformations -- Why Do We Use Transformations? -- Other Examples of Transformations -- Common Transformations -- Goodwill Case -- Process of Applying Transformations -- Summary -- Project Update.

Notes -- Chapter 10 Summary and Path Forward -- A Personal Case Study by Tom Pohlen -- The Objectives -- The "Process" -- The Goal -- Understanding Variation -- Finding Solutions -- Successful Results -- Benefits -- Lessons Learned -- Review of the Statistical Thinking Approach -- Text Summary -- Potential Next Steps to Deeper Understanding of Statistical Thinking -- Project Summary and Debriefing -- Notes -- APPENDIX A Effective Teamwork -- Benefits of Using Teams -- When to Use a Team -- Forming a Team -- Selecting Team Projects -- Ingredients for a Successful Team -- Stages of Team Growth -- Running Effective Meetings -- Dealing with Conflict -- Why Project Teams Fail -- Notes -- APPENDIX B Presentations and Report Writing -- Presentations to Individuals or Small Groups -- Presentations or Project Reviews for Large Groups -- Pitfalls -- One-Paragraph Summary or Abstract -- Written Reports -- Use of Graphics -- Presenting Statistical Results -- Pitfalls -- Notes -- APPENDIX C More on Surveys -- What Is a Survey? -- Then, What Is a Survey? -- How Large Must the Sample Size Be? -- Who Conducts Surveys? -- What Are Some Common Survey Methods? -- What Survey Questions Do You Ask? -- Who Works on Surveys? -- What about Confidentiality and Integrity? -- What Are Other Potential Concerns? -- Where Can I Get More Information? -- Note -- APPENDIX D More on Regression -- My Regression Model Has a Low Adjusted R-squared Value-Now What Do I Do? -- Dealing with Outliers -- Regression Modeling when Some or All of the Variables (x's) Are Qualitative -- Developing Trade-Offs among Multiple Responses -- Model Verification -- Notes -- APPENDIX E More on Design of Experiments -- Analysis of Two-Level Experiments -- Analysis of Mixed-Level Factorial Experiments -- Response Surface Experiments -- Notes -- APPENDIX F More on Inference Tools -- Notes.

APPENDIX G More on Probability Distributions.

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.

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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2018. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.