Data Science / Engineering Statistics / Minitab / Minitab / Statistical Quality Control using Minitab
| ID | 2858917 |
|---|---|
| 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 |
This 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).
The Meaning of Quality and Quality Improvement - Statistical Methods for Quality Control and Improvement - Management Aspects of Quality Improvement - The DMAIC Problem Solving Process
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
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)
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
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
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
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
The Acceptance-Sampling Problem - Single-Sampling Plans for Attributes - Double, Multiple, and Sequential Sampling - Acceptance Sampling by Variables - Chain Sampling - Continuous Sampling
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.
- 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.
