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.

What Is Statistics?Deductive and Inductive StatisticsThe Stages of a Sample StudyWarning!!!Control the DataSignificance Testing Wrong Signs Summary References Selected Bibliography Data Describing Data Data Orientation Nominal, Ordinal, Interval, and Ratio Sampling Considerations The Frequency of the SampleDegrees of FreedomWhere Do I Start? What Is a P-Value?Assessing NormalitySummarySelected Bibliography Summarizing Data Frequency Distributions Dot PlotsStem and Leaf DisplaysBox PlotsDescriptive Statistics Measures of DispersionNormality Areas in the Normal Distribution Standard ScoreA Sample from the Normal DistributionDistributions that Aren’t NormalThe Standard Error of the MeanTests of Distributions as a WholeTransformations to Obtain NormalityConfidence and SensitivityTest for Independence in Contingency TablesSummaryReferencesSelected BibliographyTests and Confidence Intervals for Means Test for μ When σ Is KnownTest for μ When σ Is Unknown Test for μ − μ When σ and σ Are KnownTest for μ − μ When σ and σ Are UnknownTest for Two Independent Samples When σ = σ Test of μ − μ = for Two Independent Samples When σ ≠ σ Test for Differences Between Proportions p – pTest for Differences Among Several Means Detection of a Trend in a Set of Means with Standard DeviationsUnknown but Assumed Equal Summary Selected Bibliography Tests and Confidence Intervals for Standard DeviationsComputation and Understanding s and σChi-Square Test for σ in a Normal DistributionF-Test for σ/σ for Normal DistributionsM-Test for Homogeneity of Variances Summary Selected BibliographyTests of Distributions and MeansUnderlying Distribution AnalysisMethods of Testing for Underlying DistributionsProbability Plotting Selected TestsSummaryReferencesSelected BibliographyUnderstanding and Planning the Experiment and Analysis of VarianceUnderstanding the Fundamentals of ExperimentationPlanning 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 PopulationMeans Are Equal? Special Considerations Types of Analysis of Variance Taguchi Approach to Design of Experiments Summary References Selected BibliographyNewer SourcesFitting 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 PowerExamples 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 CategoriesAppendix C: General Statistical FormulaeAppendix D: Hypothesis Testing Road Map Appendix E: Test indicatorsAppendix F: Hypothesis Testing—Selected Explanations and Examples Appendix G: When to Use Quality Tools—A Selected List GlossarySelected Bibliography