Data Science / Engineering Statistics / Minitab / Minitab / Design and Analysis of Experiments using Minitab
| ID | 2858911 |
|---|---|
| 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 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.
650 EUR +VAT
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
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
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
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
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
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
Notation and Motivation for the 3k Design - Confounding in the 3k Factorial Design - Fractional Replication of the 3k Factorial Design
Introduction to Response Surface Methodology - The Method of Steepest Ascent - Analysis of a Second-Order Response Surface - Experimental Designs for Fitting Response Surfaces