Dunn Kevin G. Process Improvement Using Data

2016. — 378 p.Visualizing Process Data.
Data visualization in context.
References and readings.
Time-series plots.
Bar plots.
Box plots.
Relational graphs: scatter plots.
Tables as a form of data visualization.
Topics of aesthetics and style.
General summary: revealing complex data graphically.
Exercises.
Univariate Data Analysis.
Univariate data analysis in context.
References and readings.
What is variability?
Histograms and probability distributions.
Binary (Bernoulli) distribution.
Uniform distribution.
Normal distribution.
The t-distribution.
Poisson distribution.
Confidence intervals.
Testing for differences and similarity.
Paired tests.
Other types of confidence intervals.
Statistical tables for the normal- and t-distribution.
Exercises.
Process Monitoring.
Process monitoring in context.
References and readings.
What is process monitoring about?
Shewhart charts.
CUSUM charts.
EWMA charts.
Other types of monitoring charts.
Process capability.
The industrial practice of process monitoring.
Industrial case study.Exercises.
Least Squares Modelling Review.
Least squares modelling in context.
References and readings.
Covariance.
Correlation.
Some definitions.
Least squares models with a single x-variable.
Least squares model analysis.
Investigating an existing linear model.
Summary of steps to build and investigate a linear model.
More than one variable: multiple linear regression (MLR).
Outliers: discrepancy, leverage, and influence of the observations.
Enrichment topics.
Exercises.
Design and Analysis of Experiments.
Design and analysis of experiments in context.
Terminology.
Usage examples.
References and readings.
Why learning about systems is important.
Experiments with a single variable at two levels.
Changing one single variable at a time (COST).
Full factorial designs.
Fractional factorial designs.
Blocking and confounding for disturbances.
Response surface methods.
Evolutionary operation.
General approach for experimentation in industry.
Extended topics related to designed experiments.
Exercises.
Latent Variable Modelling.
In context.
References and readings.
Extracting value from data.
What is a latent variable?
Principal Component Analysis (PCA).
Principal Component Regression (PCR).
Introduction to Projection to Latent Structures (PLS).
Applications of Latent Variable Models.