ID 2858914
Classroom 2 days
Webinar 4 days
Method Lecture with examples and exercises.
Prequisite General knowledge of math
Audience Engineers, Quality Assurance


  • Lunch / Catering
  • Assistance for hotel / travel bookings
  • Comelio certificate
  • Flexible: Free cancellation up until 10 days before the training


This training shows engineers and other members of the quality-assurance department to design and analyze experiments for improving the quality, efficiency and performance of working systems. It covers basic statistical methods which are useful for the analysis of experimental data, presents the Analysis of Variance (ANOVA), and teaches how to use factorial experiments, two-level factorial designs, blocking and confounding systems for two-level factorials, two-level fractional factorial designs, regression modeling, and and overview of the Response Surface Methodology.

Training Dates

  • 2020-Nov-09 - Nov-12
  • 2021-Jan-18 - Jan-21
  • 2021-Mar-29 - Apr-01
  • 2021-Jun-07 - Jun-10

650 EUR +VAT

Location | Enrollment


Basic Statistical Methods

Basic Statistical Concepts - Sampling and Sampling Distributions - Inferences About the Differences in Means, Randomized Designs: Hypothesis Testing, Confidence Intervals, Choice of Sample Size, Comparing a Single Mean to a Specified Value - Inferences About the Differences in Means, Paired Comparison Designs - Inferences About the Variances of Normal Distributions

Analysis of Variance (ANOVA)

The Analysis of Variance - Analysis of the Fixed Effects Model: Decomposition of the Total Sum of Squares, Statistical Analysis, Estimation of the Model Parameters - Model Adequacy Checking - Determining Sample Size - The Random Effects Model - The Regression Approach to the Analysis of Variance

Experiments with Blocking Factors

The Randomized Complete Block Design: Statistical Analysis of the RCBD, Model Adequacy Checking, Estimating Model Parameters and the General Regression Significance Test - The Latin Square Design - The Graeco-Latin Square Design - Balanced Incomplete Block Designs

Factorial Experiments

The Two-Factor Factorial Design: Statistical Analysis of the Fixed Effects Model, Model Adequacy Checking, Estimating the Model Parameters, Choice of Sample Size - The General Factorial Design - Fitting Response Curves and Surfaces - Blocking in a Factorial Design

Two-Level Factorial Designs

The 2² Design - The 2³ Design - The General 2k Design - A Single Replicate of the 2k Design - 2k Designs are Optimal Designs - The Addition of Center Points to the 2k Design - Blocking and Confounding Systems for Two-Level Factorials

Two-Level Fractional Factorial Designs

Process Capability Analysis Using a Histogram or a Probability Plot - Process Capability Ratios - Process Capability Analysis Using a Control Chart - Process Capability Analysis with Attribute Data - Gauge and Measurement System Capability Studies

The 3k Factorial Design

Notation and Motivation for the 3k Design - Confounding in the 3k Factorial Design - Fractional Replication of the 3k Factorial Design

Response Surface Methodology

Introduction to Response Surface Methodology - The Method of Steepest Ascent - Analysis of a Second-Order Response Surface - Experimental Designs for Fitting Response Surfaces