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Design of Experiments

How to apply the statistical method called Design of Experiments (DOE) for quality improvement and research.

Lean Six Sigma and process excellence leaders are often asked to “remove defects” from products and processes. This can be quite a challenge! Lou Johnson, senior Minitab technical trainer and mentor, has some tips that might help if you’re faced with this situation. I had the chance to talk with Lou, and here’s what he shared with me about how to first approach a DOE. How to Approach a DOE Before... Continue Reading
It’s the most wonderful time of the year – the time for holiday bakers and cookie monsters to unite! So what’s a quality improvement professional to do when his favorite sugar cookie recipe produced cookies that failed to hold their festive holiday shapes after being baked? Run a Design of Experiment (DOE), of course! A Fractional Factorial Experiment Bill Howell, an avid baker and... Continue Reading
Design of experiments, experimental design, or just "gathering some data." Whatever you want to call it, your approach to doing it will affect the results you get. Have you ever wondered about all those contradictory studies in the news, especially regarding what's good and bad for you? Coffee is good for you, one headline says. It's bad for you, says the next. And if you read beyond the headlines,... Continue Reading
We’ve used design of experiments to look at the data. We’ve seen that the center points are statistically insignificant. We’ve seen that blocks help account for the unstable conditions during the collection of the data. Now for the exciting part: let’s choose a model to use to predict where the gummi bears will land when we launch them. Various criteria exist for how to choose a model, so we’re... Continue Reading
Last time I used design of experiments to look at the gummi bear data, I interpreted the center point data. The data say that I won’t need any square or cubic terms to get a good fit to the data. Traditionally, the next effect to look at in design of experiments is the block effect. I was worried that there would be a wearout effect acting on my catapult, so I changed popsicle sticks and rubber... Continue Reading
When I chose a full factorial design for my gummi bear experiment, I was using traditional design of experiments practice to try to learn the most from the least amount of data. I wanted to see if I could save myself the 10 or more data points I would need to add to the design to estimate nonlinear effects. Now that I have some data, the first thing I’m going to learn is: Do I need to collect... Continue Reading
Back when I chose the factors to study for my gummi bear design of experiments, I was thinking about the fact that something like the position of the gummi bear and the position of the fulcrum would probably interact. When I finished collecting the data, I was eager to see if that effect showed up in my analysis. Before we look at the distance parallel to the catapult, let's look at the distance... Continue Reading
I collected my first block of data for the gummi bear design of experiments this week. Why not all of it? Well, there’s lots you can learn when you start collecting data for real. Here are some of my thoughts: Enter data quickly and accurately for design of experiments If you’re going to do anything with your data, it’s a lot easier to have it in Minitab. If you followed my lead for doing design... Continue Reading
The Minitab Fan section of the Minitab blog is your chance to share with our readers! We always love to hear how you are using Minitab products for quality improvement projects, Lean Six Sigma initiatives, research and data analysis, and more. Today we learn how an avid gamer used design of experiments to boost his performance in his favorite driving game. If our software has helped you, please sha... Continue Reading
by Manikandan Jayakumar, guest blogger In an earlier post, I discussed how to collect data in a Design of Experiments (DOE) to optimize the value of an attribute or categorical response (Pass/Fail, Accept/Reject, etc.).  I then showed how to convert the collected data into proportions and apply the arcsine transformation using built-in calculator in Minitab Statistical Software.    Now we’re ready... Continue Reading
Recently, we’ve discussed how to do the design and factor setup for design of experiments in Minitab Statistical Software. We’re almost ready to launch some gummi bears. But there’s something else to consider. When we produce the data for design of experiments, how does the data get from the measuring device to Minitab? If you’re lucky, you have an electronic thingamajig that takes measurements... Continue Reading
by Manikandan Jayakumar, guest blogger We use Design of Experiments (DOE) to optimize the value of a response (Y) by simultaneously changing the values of several factors (X’s). The response will often be a continuous variable, but in some scenarios you need to optimize an attribute or categorical response (Pass/Fail, Accept/Reject, etc.).  Collecting the Data for an Attribute Response DOE Let’s see... Continue Reading
Now that we've explored all of the DOE design choices in Minitab Statistical Software, it's time to think about the levels of the factors. I chose these 5 factors previously: Position of catapult on the launch ramp Angle of catapult Number of rubber band windings Position of gummi bear on the catapult Position of fulcrum in the catapult What Is an Effect in DOE? In DOE, we're trying to detect the effect... Continue Reading
Last time, we talked about center points and replicates in design of experiments. It turns out that both are tools that you can use to increase the probability of finding a statistically significant difference. But what we really want to know is, how many center points and replicates should be in the gummi bear experiment? To answer that question, we have to estimate the standard deviation of... Continue Reading
Last time, we talked about what resolution means in design of experiments (DOE). After you choose your resolution in Minitab Statistical Software, you need to choose the number of center points and the number of replicates for corner points. We can consider these two questions together because they’ll help determine the total size of the experiment. Using center points to check your model I alluded... Continue Reading
Now that we’ve settled on a 2-level factorial design, we’ll take a look at some of the different 2-level designs that we can run with 5 factors. Minitab gives us 3 options in design of experiments: a full factorial, a half fraction and a quarter fraction. In the statistical world of DOE, we say these designs offer different "resolutions" to an experiment. You can think of choosing a statistical... Continue Reading
Now that we’ve learned enough about design of experiments to understand the experimental designs that Minitab offers, it’s time  to consider which we should use to study the gummi bears. We'll consider the designs in the same order that we did in the earlier blog posts: General full factorial designs Split-plot designs Plackett-Burman designs What Type of Design do I Need? Here's a series of... Continue Reading
Design of experiments (DOEs) is a very effective and powerful statistical tool that can help you understand and improve your processes, and design better products. DOE lets you assess the main effects of a process as well as the interaction effects (the effect of factor A, for example, may be much larger when factor B is set at a specific level, leading to an interaction). In science and in... Continue Reading
Suppose that on your way to a summer holiday resort (a very distant place), your car breaks down. You might just call the roadside assistance and wait for your car to be towed to a repair shop. But suppose that you think you are smarter than that, and you would like to solve the issue by yourself—or at least evaluate the repair cost. Vehicle breakdowns can occur for a large number of reasons. Intuit... Continue Reading
In previous posts on design of experiments, or DOE, we’ve covered: General full factorial designs 2-level designs Plackett-Burman designs Next on the list are split-plot experiments. Split-plot designs are extremely popular in design of experiments because they cover a common case in the real world: when you have a factor that you want to study but can’t change as often as your other factors. If you... Continue Reading