This book differs from the ordinary textbooks on statistics. It aims to show how to apply the methods of statistics to the real world problems. People often try to reduce defects by tracing directly back to the cause of the defect. Though this approach may seem to be efficient at first glance, the cause obtained in most cases are not the true causes. If remedies are based on this knowledge, the attempt may be abortive, the effort wasted. The first step in finding true cause is careful observation of the phenomenon when true cause becomes apparent. Statistical tools lend objectivity and accuracy to observation. The maxim of statistical way of thinking are: “Give greater importance to facts than abstract concepts.” “Do not express facts in terms of senses or ideas. Use figures derived from specific observational results.” “Observational results, accompanied as they are by error and variation, are part of a hidden whole. Finding that hidden whole is observation’s ultimate goal.” “Accept regular tendency which appears in a large number of observational results as reliable information.” One must first thoroughly understand imperfection of human recognition. One must then understand that knowledge presently held in nothing more than grounds for further hypotheses. After gaining that understanding, the above-mentioned methods of thinking can be used to further deepen our understanding of the production process and the ways to improve it.
Preface
Chapter 1 : Introduction
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The Role of Statistical Methods in the Management of Production Processes
Chapter 2 : How to Obtain Data
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2.1 How to Collect Data
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2.2 Check Sheets
Chapter 3 : Pareto Analysis
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3.1 What are Pareto Diagrams?
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3.2 How to Make Pareto Diagrams
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3.3 Pareto Diagrams by Phenomena and Pareto Diagrams by Causes
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3.4 Notes on Pareto Diagrams
Chapter 4 : Cause-and-Effect Diagrams
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4.1 What are Cause-and-Effect Diagrams?
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4.2 How to Make Cause-and-Effect Diagrams
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4.3 Notes on Cause-and-Effect Diagrams
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4.4 Pareto Diagrams and Cause-and-Effect Diagrams
Chapter 5 : Histograms
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5.1 Distributions and Histograms
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5.2 How to Make Histograms
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5.3 How to Read Histograms
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5.4 Measures to Represent the Characteristics of Distributions
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5.5 Normal Distribution and Its Characteristics
Chapter 6 : Scatter Diagrams
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6.1 What are Scatter Diagrams?
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6.2 How to Make Scatter Diagrams
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6.3 How to Read Scatter Diagrams
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6.4 Calculation of Correlation Coefficients
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6.5 Notes on Correlation Analysis
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6.6 What is Regression Analysis?
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6.7 Estimation of Regression Lines
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6.8 Notes on Regression Analysis
Chapter 7 : Control Charts
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7.1 What are Control Charts?
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7.2 Types of Control Charts
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7.3 How to Make Control Charts
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7.4 How to Read Control Charts
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7.5 Process Analysis by Control Charts
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7.6 Case Study of Process Analysis
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7.7 Process Control by Control Charts
Chapter 8 : Additivity of Variances
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8.1 The Means and the Variances of Sums
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8.2 Precision of Parts Assembly
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8.3 Theoretical Formulas
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8.4 The Expectation and the Variance of Sample Means
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8.5 Sampling Error and Measurement Error
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8.6 The Variance of Function Values
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8.7 When Random Variables are not Independent
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8.8 Selective Combination
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8.9 Statistical Quality Control
Chapter 9 : Introduction to Statistical Inference
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9.1 Statistics
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9.2 Distribution of Statistics
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9.3 Test of Hypothesis
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9.4 Estimation of Parameters
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9.5 Tests and Estimations of Population Means When σ is not Known
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9.6 Tests and Estimations of the Differences Between Two Population Means
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9.7 Tests and Estimations in Paired Observations
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9.8 Tests of Significance of Correlation Coefficients
Chapter 10 : The QC Story
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10.1 Problem
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10.2 Observation
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10.3 Analysis
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10.4 Action
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10.5 Check
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10.6 Standardization
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10.7 Conclusion
Epilogue
Appendix
Table A.1 : Tables of Normal Distribution
Table A.2 : Coefficients for x-R Chart
Table A.3 : Percentage Points of t-Distribution
Answers to Exercises
Index
