Essential Statistical Concepts for the Quality Professional

The essence of any root cause analysis in our modern quality thinking is to go beyond the actual problem. This means not only do we have to fix the problem at hand but we also have to identify why the failure occurred and what was the opportunity to apply the appropriate knowledge to avoid the problem in the future. Essential Statistical Concepts for the Quality Professional offers a new non-technical statistical approach to quality for effective improvement and productivity by focusing on very specific and fundamental methodologies and tools for the future.

Written by an expert with more than 30 years of experience in management, quality training, and consulting, the book examines the fundamentals of statistical understanding, and by doing so demonstrates the importance of using statistics in the decision making process. The author points out pitfalls to keep in mind when undertaking an experiment for improvement and explains how to use statistics in improvement endeavors. He discusses data interpretation, common tests and confidence intervals, and how to plan experiments for improvement. The book expands the notion of experimentation by dealing with mathematical models such as regression to optimize the improvement and understand the relationship between several factors. It emphasizes the need for sampling and introduces specific techniques to make sure accuracy and precision of the data is appropriate and applicable for the study at hand.

The author’s approach is somewhat new and unique; however, he details tools and methodologies that can be used to evaluate the system for prevention. These tools and methodologies focus on structured, repeatable processes that can be instrumental in finding real, fixable causes of the human errors and equipment failures that lead to quality issues.

Table of Contents

What Is Statistics?
Deductive and Inductive Statistics
The Stages of a Sample Study
Warning!!!
Control the Data
Significance Testing 
Wrong Signs 
Summary 
References 
Selected Bibliography 

Data 
Describing Data 
Data Orientation 
Nominal, Ordinal, Interval, and Ratio 
Sampling Considerations 
The Frequency of the Sample
Degrees of Freedom
Where Do I Start? 
What Is a P-Value?
Assessing Normality
Summary
Selected Bibliography 

Summarizing Data 
Frequency Distributions 
Dot Plots
Stem and Leaf Displays
Box Plots
Descriptive Statistics 
Measures of Dispersion
Normality 
Areas in the Normal Distribution 
Standard Score
A Sample from the Normal Distribution
Distributions that Aren’t Normal
The Standard Error of the Mean
Tests of Distributions as a Whole
Transformations to Obtain Normality
Confidence and Sensitivity
Test for Independence in Contingency Tables
Summary
References
Selected Bibliography

Tests and Confidence Intervals for Means 
Test for μ When σ Is Known
Test for μ When σ Is Unknown 
Test for μ − μ When σ and σ Are Known
Test for μ − μ When σ and σ Are Unknown
Test for Two Independent Samples When σ = σ 
Test of μ − μ = for Two Independent Samples When σ ≠ σ 
Test for Differences Between Proportions p – p
Test for Differences Among Several Means 
Detection of a Trend in a Set of Means with Standard Deviations
Unknown but Assumed Equal 
Summary 
Selected Bibliography 

Tests and Confidence Intervals for Standard Deviations
Computation and Understanding s and σ
Chi-Square Test for σ in a Normal Distribution
F-Test for σ/σ for Normal Distributions
M-Test for Homogeneity of Variances 
Summary 
Selected Bibliography

Tests of Distributions and Means
Underlying Distribution Analysis
Methods of Testing for Underlying Distributions
Probability Plotting 
Selected Tests
Summary
References
Selected Bibliography

Understanding and Planning the Experiment and Analysis of Variance
Understanding the Fundamentals of Experimentation
Planning the Process 
Data Analysis versus Statistical Analysis 
Analysis of Variance 
Hypothesis Testing 
Calculating the F-Ratio 
Multiple-Comparison Procedures 
Interactions 
How Can You Test the Null Hypothesis that Several Population
Means Are Equal? 
Special Considerations 
Types of Analysis of Variance 
Taguchi Approach to Design of Experiments 
Summary 
References 
Selected Bibliography
Newer Sources

Fitting Functions 
Selecting Dependent Variables 
Selecting Independent Variables 
Correlation Analysis 
Regression 
Step Regression 
General Linear Model 
Discriminant Analysis 
Log-Linear Models 
Logistic Regression 
Factor Analysis 
Cluster Analysis 
Testing Hypothesis About Many Means 
Conjoint Analysis 
Multiple Regression 
Partial Regression 
After Regression 
Summary 
References 
Selected Bibliography 

Typical Sampling Techniques 
Notation and Formula for the Hypergeometric Distribution 
Binomial Experiment 
Notation and Formula 
Binomial Distribution 
Cumulative Binomial Probability 
Types of Acceptance Plans 
Definitions of Basic Acceptance Plans 
The Final Choice Is a Tradeoff Analysis 
MIL STD (D and ) 
Stratified Sampling 
Proportionate Versus Disproportionate Stratification 
Surveys 
Quality of Survey Results 
Sample Design 
Simple Random Sampling 
Cluster Sampling 
The Difference between Strata and Clusters 
How to Choose the Best Sampling Method 
Quality-Control Charts and Sampling 
Attributes or Variables 
Choice of Plan 
Inspection Level 
Summary 
Selected Bibliography 

Understanding Computer Programs for Design and Estimating Design Power
Examples of ANOVA and ANCOVA Models 
Key to types of statistical models 
Strong Recommendations 
Summary 
References 
Epilogue 

Appendix A: Minitab Computer Usage 
Appendix B: Formulae Based on Statistical Categories
Appendix C: General Statistical Formulae
Appendix D: Hypothesis Testing Road Map 
Appendix E: Test indicators
Appendix F: Hypothesis Testing—Selected Explanations and Examples 
Appendix G: When to Use Quality Tools—A Selected List 
Glossary
Selected Bibliography 

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