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. A 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.Preface
Modern Statistics: A Computer-Based Approach with Python (Companion Volume)
List of Abbreviations
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
1 -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
Introduction
A Review Multivariate Data Analysis
Multivariate Process Capability Indices
Advanced Applications of Multivariate Control Charts
Multivariate Control Charts Scenarios
Internally Derived Target
xternal 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
2m Factorial Designs
3m Factorial Designs
Blocking and Fractional Replications of 2m Factorial Designs
Exploration of Response Surfaces
Second Order Designs
Some Specific Second Order Designs
3k-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 pymc
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
Bibliography
Index