Duration
1 day
Overview
Learn how to handle common DOE scenarios where modifications to the analysis of classic factorial and response surface designs are necessary due to the nature of the response variable or the data collection process. Develop techniques for experimental situations often encountered in practice such as missing data and hard-to-change factors. Understand how to account for variables (covariates) that may affect the response but cannot be controlled in the experiment. Explore the opportunities to minimize costs or variability while simultaneously optimizing an important product or process characteristic. Learn how to find and quantify the effect that factors have on the probability of a critical event, such as a defect, occurring.
Topics covered in the training material include: ANCOVA, Unbalanced Designs, Split-Plot Designs, Multiple Response Optimization, Binary Logistic Regression
Prerequisite
Minitab Essentials and Factorial Designs
Outline
Analyze Variability
Analysis of Covariance
Split-Plot Designs for Hard-to-Change Variables
Cost Optimization
Binary Response
Plackett-Burman Designs
D-Optimal Designs
Programme scope, delivery arrangements and suitability are confirmed during enquiry.