This issue includes the following:
· Comparing Three Approaches to Robust Design by Dr. Wayne Taylor
· Feature by Neelam Nakadi: Examine the Distribution of Data using R Graphs
· Columns by Donald J. Wheeler, guest author Grant Reinman, and Jim Frost.
Comparing Three Approaches to Robust Design:
Robust Design, Parameter Design, Dual Response, Inner/Outer Arrays, Taguchi Methods, Statistical Tolerance Analysis, Tolerance Design
Robustness is a key strategy for achieving high-quality, low-cost products and processes. Three different approaches to robust design are commonly used: the inner/outer array approach advocated by Taguchi, the dual response approach using response surfaces and the tolerance analysis approach which also uses response surfaces. Each of these approaches will be explained. The three approaches will then be contrasted.
Feature: Examine the Distribution of Data using R Graphs:
Exploratory Data Analysis, R Programming Language, Data Presentation
A graph is worth a thousand words. Because a graph can reveal a pattern in the data which we cannot see by looking at the tables. However, the interpretation of the graphs can be subjective. Graphs are inherently influenced by scale size, bin size, etc., which are subjective and can vary from person to person. Many times, we are tempted to think of a histogram as being close to normal or truncated normal distribution. Normally distributed data opens up our toolbox to a large number of tried and tested statistical tools. But the results and predictions based on an assumption of normality when it is not true will be misleading and would result in wasted efforts.