Data Science / Engineering Statistics / Minitab / Statistics / Statistical Quality Control

Statistics - Statistical Quality Control


ID 2858921
Classroom 2 days 9:00-16:30
Webinar 4 days 9:00-12:30
Method Lecture with examples and exercises.
Prequisite General knowledge of math
Audience Engineers, Quality Assurance

Statistics Statistical Quality Control Training


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


Statistics Statistical Quality Control TrainingThis training provides a comprehensive treatment of the major aspects of using statistical methodology for quality control and improvement. Both traditional and modern methods are presented, including state-of-the-art techniques for statistical process monitoring and control and statistically designed experiments for process characterization and optimization. The training focuses on DMAIC (define, measure, analyze, improve, and control--the problem-solving strategy of six sigma).

Statistics Statistical Quality Control Training

Training Dates

  • 2022-May-23 - May-26
  • 2022-Aug-01 - Aug-04

650 EUR +VAT

Location | Enrollment


Statistics Statistical Quality Control Seminar
Modern Quality Management And Improvement

The Meaning of Quality and Quality Improvement - Statistical Methods for Quality Control and Improvement - Management Aspects of Quality Improvement - The DMAIC Problem Solving Process

Data Summary and Presentation

Describing Variation: The Stem-and-Leaf Plot, The Histogram, Numerical Summary of Data, The Box Plot, Probability Distributions - Important Discrete Distributions - Important Continuous Distributions - Probability Plots

Statistical Inference In Quality Control and Improvement

Statistics and Sampling Distributions - Point Estimation of Process Parameters - Statistical Inference for a Single Sample - Statistical Inference for Two Samples - The Analysis of Variance (ANOVA)

Variables Control Charts

Control Charts for –x and R: Statistical Basis of the Charts, Development and Use of –x and R Charts, Charts Based on Standard Values, Interpretation of –x and R Charts, The Operating-Characteristic Function, The Average Run Length for the –x Chart - Control Charts for –x and s: Construction and Operation of –x and s Charts, The –x and s Control Charts with Variable Sample Size, The s² Control Chart - The Shewhart Control Chart for Individual Measurements

Attribute Control Charts

The Control Chart for Fraction Nonconforming: Development and Operation of the Control Chart, Variable Sample Size, Applications in Transactional and Service Businesses, The Operating-Characteristic Function and Average Run Length Calculations - Control Charts for Nonconformities (Defects): Procedures with Constant Sample Size, Procedures with Variable Sample Size, Demerit Systems, The Operating-Characteristic Function, Dealing with Low Defect Levels - Choice Between Attributes and Variables Control Charts

Determining Process And Measurement Systems Capability

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

Designed Experiments In Process and Product Improvement

Factorial Experiments: Statistical Analysis, Residual Analysis - The 2k Factorial Design: The 2² Design, The 2k Design for 3 and more Factors, Blocking and Confounding in the 2k Design - Fractional Replication of the 2k Design - Fractional Replication of the 2k: The One-Half Fraction of the 2k Design, The 2k–p Fractional Factorial Design

Sampling Procedures

The Acceptance-Sampling Problem - Single-Sampling Plans for Attributes - Double, Multiple, and Sequential Sampling - Acceptance Sampling by Variables - Chain Sampling - Continuous Sampling


Statistics Statistical Quality Control Trainer

Marco Skulschus (born in Germany in 1978) studied economics in Wuppertal (Germany) and Paris (France) and wrote his master´s thesis about semantic data modeling. He started working as a lecturer and consultant in 2002.

  • Grundlagen empirische Sozialforschung ISBN 978-3-939701-23-1
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  • MS SQL Server - T-SQL Programmierung und Abfragen ISBN 978-3-939701-69-9

- He works as an IT-consultant and project manager. He developed various Business Intelligence systems for industry clients and the public sector. For several years now, he is responsible for a BI-team in India which is mainly involved in BI and OLAP projects, reporting systems as well as statistical analysis and Data Mining.


He led several research projects and was leading scientist and project manager of a publicly funded project about interactive questionnaires and online surveys.

Statistics Statistical Quality Control Trainer