Process Improvement Using Data
  • Preface

Table of Contents

  • 1. Visualizing Process Data
    • 1.1. Data visualization in context
    • 1.2. References and readings
    • 1.3. Time-series plots
    • 1.4. Bar plots
    • 1.5. Box plots
    • 1.6. Relational graphs: scatter plots
    • 1.7. Tables as a form of data visualization
    • 1.8. Topics of aesthetics and style
    • 1.9. General summary: revealing complex data graphically
    • 1.10. Exercises
  • 2. Univariate Data Analysis
    • 2.1. Univariate data analysis in context
    • 2.2. References and readings
    • 2.3. What is variability?
    • 2.4. Histograms and probability distributions
    • 2.5. Some terminology
    • 2.6. Binary (Bernoulli) distribution
    • 2.7. Uniform distribution
    • 2.8. The normal distribution and checking for normality
    • 2.9. The t-distribution
    • 2.10. Poisson distribution
    • 2.11. Confidence intervals
    • 2.12. Testing for differences and similarity
    • 2.13. Paired tests
    • 2.14. Other types of confidence intervals
    • 2.15. Statistical tables for the normal- and t-distribution
    • 2.16. Exercises
  • 3. Process Monitoring
    • 3.1. Process monitoring in context
    • 3.2. References and readings
    • 3.3. What are process monitoring charts?
    • 3.4. Shewhart charts
    • 3.5. CUSUM charts
    • 3.6. EWMA charts
    • 3.7. Other types of monitoring charts
    • 3.8. Process capability
    • 3.9. The industrial practice of process monitoring
    • 3.10. Industrial case study
    • 3.11. Summary
    • 3.12. Exercises
  • 4. Least Squares Modelling Review
    • 4.1. Least squares modelling in context
    • 4.2. References and readings
    • 4.3. Covariance
    • 4.4. Correlation
    • 4.5. Some definitions
    • 4.6. Least squares models with a single x-variable
    • 4.7. Least squares model analysis
    • 4.8. Investigating an existing linear model
    • 4.9. Summary of steps to build and investigate a linear model
    • 4.10. More than one variable: multiple linear regression (MLR)
    • 4.11. Outliers: discrepancy, leverage, and influence of the observations
    • 4.12. Enrichment topics
    • 4.13. Exercises
  • 5. Design and Analysis of Experiments
    • 5.1. Design and analysis of experiments in context
    • 5.2. Terminology
    • 5.3. Usage examples
    • 5.4. References and readings
    • 5.5. Why learning about systems is important
    • 5.6. Experiments with a single variable at two levels
    • 5.7. Changing one single variable at a time (COST)
    • 5.8. Full factorial designs
      • 5.8.1. Using two levels for two or more factors
      • 5.8.2. Analysis of a factorial design: main effects
      • 5.8.3. Analysis of a factorial design: interaction effects
      • 5.8.4. Analysis by least squares modelling
      • 5.8.5. Example: design and analysis of a three-factor experiment
      • 5.8.6. Assessing significance of main effects and interactions
      • 5.8.7. Summary so far
      • 5.8.8. Example: analysis of systems with 4 factors
    • 5.9. Fractional factorial designs
      • 5.9.1. Half fractions
      • 5.9.2. Generators and defining relationships
      • 5.9.3. Generating the complementary half-fraction
      • 5.9.4. Generators: to determine confounding due to blocking
      • 5.9.5. Highly fractionated designs: beyond half-fractions
      • 5.9.6. Design resolution
      • 5.9.7. Saturated designs for screening
      • 5.9.8. Design foldover
      • 5.9.9. Projectivity
    • 5.10. Blocking and confounding for disturbances
    • 5.11. Response surface methods
    • 5.12. Evolutionary operation
    • 5.13. General approach for experimentation
    • 5.14. Extended topics related to designed experiments
    • 5.15. Exercises
  • 6. Latent Variable Modelling
    • 6.1. In context
    • 6.2. References and readings
    • 6.3. Extracting value from data
    • 6.4. What is a latent variable?
    • 6.5. Principal Component Analysis (PCA)
      • 6.5.1. Visualizing multivariate data
      • 6.5.2. Geometric explanation of PCA
      • 6.5.3. Mathematical derivation for PCA
      • 6.5.4. More about the direction vectors (loadings)
      • 6.5.5. PCA example: Food texture analysis
      • 6.5.6. Interpreting score plots
      • 6.5.7. Interpreting loading plots
      • 6.5.8. Interpreting loadings and scores together
      • 6.5.9. Predicted values for each observation
      • 6.5.10. Interpreting the residuals
      • 6.5.11. PCA example: analysis of spectral data
      • 6.5.12. Hotelling’s T²
      • 6.5.13. Preprocessing the data before building a model
      • 6.5.14. Algorithms to calculate (build) PCA models
      • 6.5.15. Testing the PCA model
      • 6.5.16. Determining the number of components to use in the model with cross-validation
      • 6.5.17. Some properties of PCA models
      • 6.5.18. Latent variable contribution plots
      • 6.5.19. Using indicator variables in a latent variable model
      • 6.5.20. Visualization latent variable models with linking and brushing
      • 6.5.21. PCA Exercises
    • 6.6. Principal Component Regression (PCR)
    • 6.7. Introduction to Projection to Latent Structures (PLS)
      • 6.7.1. Advantages of the projection to latent structures (PLS) method
      • 6.7.2. A conceptual explanation of PLS
      • 6.7.3. A mathematical/statistical interpretation of PLS
      • 6.7.4. A geometric interpretation of PLS
      • 6.7.5. Interpreting the scores in PLS
      • 6.7.6. Interpreting the loadings in PLS
      • 6.7.7. How the PLS model is calculated
      • 6.7.8. Variability explained with each component
      • 6.7.9. Coefficient plots in PLS
      • 6.7.10. Analysis of designed experiments using PLS models
      • 6.7.11. PLS Exercises
    • 6.8. Applications of Latent Variable Models
  • 7. Applications of Process Improvement using Data
    • 7.1. Product development and product improvement
    • 7.2. Important concepts
Complete index
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  • Updated: 09 February 2025
  • Version: 2da3b0

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