Kenett R.S., Zacks S., Gedeck P. Industrial Statistics: A Computer-Based Approach With Python

Springer, 2023. — 486 p.This innovative textbook presents material for a course on industrial statistics that incorporates Python as a pedagogical and practical resource. Drawing on many years of teaching and conducting research in various applied and industrial settings, the authors have carefully tailored the text to provide an ideal balance of theory and practical applications. Numerous examples and case studies are incorporated throughout, and comprehensive Python applications are illustrated in detail. A custom Python package is available for download, allowing students to reproduce these examples and explore others.The first chapters of the text focus on the basic tools and principles of process control, methods of statistical process control (SPC), and multivariate SPC. Next, the authors explore the design and analysis of experiments, quality control and the Quality by Design approach, computer experiments, and cybermanufacturing and digital twins. The text then goes on to cover reliability analysis, accelerated life testing, and Bayesian reliability estimation and prediction. The final chapter considers sampling techniques and measures of inspection effectiveness. Every chapter includes exercises, data sets, and Python applications.Industrial Statistics: A Computer-Based Approach with Python is intended for a one- or two-semester advanced undergraduate or graduate course. In addition, it can be used in focused workshops combining theory, applications, and Python implementations. Researchers, practitioners, and data scientists will also find it to be a useful resource with the numerous applications and case studies that are included.Modern Statistics: A Computer-Based Approach with Python (Companion Volume).The Role of Statistical Methods in Modern Industry.
Evolution of Industry.
Evolution of Quality.
Industry 4.0 Characteristics.
Digital Twin.
Chapter Highlights.
Exercises.
Basic Tools and Principles of Process Control.
Basic Concepts of Statistical Process Control.
Driving a Process with Control Charts.
Setting Up a Control Chart: Process Capability Studies.
Process Capability Indices.
Seven Tools for Process Control and Process Improvement.
Statistical Analysis of Pareto Charts.
The Shewhart Control Charts.
Control Charts for Attributes.
Control Charts for Variables.
Charts.
S-Charts and R-Charts.
Process Analysis with Data Segments.
Data Segments Based on Decision Trees.
Data Segments Based on Functional Data Analysis.
Chapter Highlights.
Exercises.
Advanced Methods of Statistical Process Control.
Tests of Randomness.
Testing the Number of Runs.
Runs Above and Below a Specified Level.
Runs Up and Down.
Testing the Length of Runs Up and Down.
Modified Shewhart Control Charts for.
The Size and Frequency of Sampling for Shewhart Control Charts.
The Economic Design for -charts.
Increasing the Sensitivity of p-charts.
Cumulative Sum Control Charts.
Upper Page's Scheme.
Some Theoretical Background.
Normal Distribution.
Binomial Distributions.
Poisson Distributions.
Lower and Two-Sided Page's Scheme.
Average Run Length, Probability of False Alarm, and Conditional Expected Delay.
Bayesian Detection.
Process Tracking.
The EWMA Procedure.
The BECM Procedure.
The Kalman Filter.
The QMP Tracking Method.
Automatic Process Control.
Chapter Highlights.
Exercises.
Multivariate Statistical Process Control.A Review Multivariate Data Analysis.
Multivariate Process Capability Indices.
Advanced Applications of Multivariate Control Charts.
Multivariate Control Charts Scenarios.
Internally Derived Target.
external Reference Sample.
Externally Assigned Target.
Measurement Units Considered as Batches.
Variable Decomposition and Monitoring Indices.
Multivariate Tolerance Specifications.
Tracking Structural Changes.
The Synthetic Control Method.
Chapter Highlights.
Exercises.
Classical Design and Analysis of Experiments.
Basic Steps and Guiding Principles.
Blocking and Randomization.
Additive and Non-additive Linear Models.
The Analysis of Randomized Complete Block Designs.
Several Blocks, Two Treatments per Block: Paired Comparison.
The t-Test.
Randomization Tests.
Several Blocks, t Treatments per Block.
Balanced Incomplete Block Designs.
Latin Square Design.
Full Factorial Experiments.
he Structure of Factorial Experiments.
The ANOVA for Full Factorial Designs.
Estimating Main Effects and Interactions.
m Factorial Designs.
m Factorial Designs.
Blocking and Fractional Replications of 2m Factorial Designs.
Exploration of Response Surfaces.
Second Order Designs.
Some Specific Second Order Designs.
k-Designs.
Central Composite Designs.
Approaching the Region of the Optimal Yield.
Canonical Representation.
Evaluating Designed Experiments.
Chapter Highlights.
Exercises.
Quality by Design.
Off-Line Quality Control, Parameter Design, and the Taguchi Method.
Product and Process Optimization Using Loss Functions.
Major Stages in Product and Process Design.
Design Parameters and Noise Factors.
Parameter Design Experiments.
Performance Statistics.
The Effects of Non-linearity.
Taguchi's Designs.
Quality by Design in the Pharmaceutical Industry.
Introduction to Quality by Design.
A Quality by Design Case Study: The Full Factorial Design.
A Quality by Design Case Study: The Desirability Function.
A Quality by Design Case Study: The Design Space.
Tolerance Designs.
Case Studies.
The Quinlan Experiment.
Computer Response Time Optimization.
Chapter Highlights.
Exercises.
Computer Experiments.
Introduction to Computer Experiments.
Designing Computer Experiments.
Analyzing Computer Experiments.
Stochastic Emulators.
Integrating Physical and Computer Experiments.
Simulation of Random Variables.
Basic Procedures.
Generating Random Vectors.
Approximating Integrals.
Chapter Highlights.
Exercises.
Cybermanufacturing and Digital Twins.
Introduction to Cybermanufacturing.
Cybermanufacturing Analytics.
Information Quality in Cybermanufacturing.
Modeling in Cybermanufacturing.
Computational Pipelines.
Digital Twins.
Chapter Highlights.
Exercises.
Reliability Analysis.
Basic Notions.
Time Categories.
Reliability and Related Functions.
System Reliability.
Availability of Repairable Systems.
Types of Observations on TTF.
Graphical Analysis of Life Data.
Nonparametric Estimation of Reliability.
Estimation of Life Characteristics.
Maximum Likelihood Estimators for Exponential TTF Distribution.
Maximum Likelihood Estimation of the Weibull Parameters.
Reliability Demonstration.
Binomial Testing.
Exponential Distributions.
The SPRT for Binomial Data.
The SPRT for Exponential Lifetimes.
The SPRT for Poisson Processes.
Accelerated Life Testing.
The Arrhenius Temperature Model.
Other Models.
Burn-In Procedures.
Chapter Highlights.
Exercises.
Bayesian Reliability Estimation and Prediction.
Prior and Posterior Distributions.
Loss Functions and Bayes Estimators.
Distribution-Free Bayes Estimator of Reliability.
Bayes Estimator of Reliability for Exponential Life Distributions.
Bayesian Credibility and Prediction Intervals.
Distribution-Free Reliability Estimation.
Exponential Reliability Estimation.
Prediction Intervals.
Applications with Python: Lifelines and sync.
Credibility Intervals for the Asymptotic Availability of Repairable Systems: The Exponential Case.
Empirical Bayes Method.
Chapter Highlights.
Exercises.
Sampling Plans for Batch and Sequential Inspection.
General Discussion.
Single-Stage Sampling Plans for Attributes.
Approximate Determination of the Sampling Plan.
Double Sampling Plans for Attributes.
Sequential Sampling and A/B Testing.
The One-Armed Bernoulli Bandits.
Two-Armed Bernoulli Bandits.
Acceptance Sampling Plans for Variables.
Rectifying Inspection of Lots.
National and International Standards.
Skip-Lot Sampling Plans for Attributes.
The ISO 2859 Skip-Lot Sampling Procedures.
The Deming Inspection Criterion.
Published Tables for Acceptance Sampling.
Sequential Reliability Testing.
Chapter Highlights.
Exercises.
Introduction to Python.
List, Set, and Dictionary Comprehensions.
Scientific Computing Using numpy and scipy.
Pandas Data Frames.
Data Visualization Using pandas and matplotlib.
List of Python Packages.
Code Repository and Solution Manual.