Introduction

General Remarks

Application in Industry

Historical Sidelights

Aim and Scope

Procedure for Conducting Experimental Investigations: Basic Principles

System Analysis and Clear Definition of Objectives

Response Variables and Experimental Factors

Replication, Blocking, and Randomization

Interactions

Different Experimental Strategies

Drawback of the One-Factor-at-a-Time Method

Factorial Designs

Basic Concepts

The 2² Factorial Design

The 2³ Factorial Design

Fractional Factorial Designs

Response Surface Designs

The Idea of Using Basic Empirical Models

The Class of Models Used in DoE

Standard DoE Models and Corresponding Designs

Using Regression Analysis to Fit Models to Experimental Data

Methods for Assessing, Improving, and Visualizing Models

R² Regression Measure and Q² Prediction Measure

ANOVA (Analysis of Variance) and Lack-of-Fit Test

Analysis of Observations and Residuals

Heuristics for Improving Model Performance

Graphical Visualization of Response Surfaces

Optimization Methods

Basic EVOP Approach Using Factorial Designs

Model-Based Approach

Multi-Response Optimization with Desirability Functions

Validation of Predicted Optima

Designs for Special Purposes

Mixture Designs

Designs for Categorical Factors

Optimal Designs

Robust Design as a Tool for Quality Engineering

Software