Blog

Learn about the most common pitfalls in Design of Experiments and practical strategies to avoid them in your next study.

In this 'Ask Statman' article, Dr. Whitman will address several questions about the Weibull Distribution.

Learn the fundamental principles of Design of Experiments and how it can transform your product development process.

Discover why validating your measurement system is crucial before collecting data, and how MSA can save time and prevent costly mistakes.

This paper suggests an alternate way forward—a lean approach that lets us use data collected for an experiment to also do a measurement sanity check.

Typical Gage R&R studies rely on the assumption that a part can be measured without changing the characteristics of that part. This paper tells you how to perform a Measurement System Analysis for a Destructive Measurement System.

This paper will show how a combination of Design of Experiments and Monte Carlo techniques can be used to set robust process specifications in a straightforward, objective manner.

In this paper you will learn how to use JMP to analyze Experiment Designs with censored data.

Two examples from the Pharmaceuticals Industry. In this paper you will learn how to use the JMP Custom Designer to create Experiment Designs with non-linear constraints.

A case study in the effective use of Design of Experiments from US Synthetic.

Gage Performance Curves provide a way to communicate the results of your MSA so that others can easily understand your results.

If your data is important, then you should perform a Measurement System Analysis (MSA) to ensure its accuracy. Three real-world examples describe measuring surface tension for inks, copper thickness for printed circuit boards and diamond cutter wear.

Reliability analysis is essential to developing and manufacturing quality products. The Weibull Distribution is a powerful tool for measuring variations in production reliability and predicting product life, so you can make more informed decisions.

Engineers can now measure product reliability and life, to design more reliable products. This article features three industry examples of Reliability Analysis.

One-Factor-at-a-Time is the most common experimental methodology, but it has significant weaknesses: it's subjective, restricted and ignores factor interactions. Using a model corrects these shortcomings.

Design of Experiments gives you the most information at the lowest cost with the least amount of work. This article presents three industry examples, from Hewlett Packard, Lexmark International and Monsanto.

Invention and innovation are different. Using invention methods to innovate is ineffective. Learning Effective Innovation has been difficult in the past, but Objective Experiments makes it easy.