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Gummi Bear DOE: What Do the Center Points Show?

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 more data?

I hope I don't, because I would have to go buy more gummi bears. I already ate the bears I didn’t throw away.

I talked about the role of center points in design of experiments earlier. When we...

Gummi Bear DOE: Mystery Effects

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 perpendicular to the catapult. I didn’t change any factors with the express purpose of making the gummi bear go left or right, so I was hoping all of these factors would be statistically insignificant....

Gummi Bear DOE: First Lessons from Data Collection

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 of experiments, you have a piece of paper that looks like this:

Accuracy will be much easier if the same person who wrote the data also enters it in the computer, so they can figure out if that number in...

Design of Experiments for an Avid Gamer

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 share your Minitab story, too!

 

I've used Minitab Statistical Softwarepretty much every work day since being a Black Belt, but I've also used it at home. Back before the needs of family life took over,...

Optimizing Attribute Responses using Design of Experiments (DOE), Part 2

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 to analyze the data to see what effect our three factors have on our attribute response! 

Initial Model and Interaction Plot of Attribute DOE Results

We’ll do this by choosing Stat > DOE > Factorial...

Gummi Bear DOE: Printing a Data Collection Sheet

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 and beams them directly to a desktop computer where they’re stored in an analyzable format. But that’s Joan-Ginther-or-Jim-Frost-lucky. The rest of us are probably going to have to use a classic...

Optimizing Attribute Responses using Design of Experiments (DOE), Part 1

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 how we can use DOE to optimize an attribute response, using data from the manufacturing sector. We are looking at 3 factors with 2 levels each, which are coded as -1 and +1. The total number of...

Gummi Bear DOE: Choosing Factor Levels

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 of changing each variable from the low to high level. Usually, the way to make the difference most obvious is to make the levels as far apart as possible. Let me borrow some data from Carly Barry to...

Gummi Bear DOE: Replicates and Center Points, Part 2

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 the distances the gummi bears go.

Estimating the Standard Deviation When You Do Design of Experiments

I do have an old data set from some students launching gummi bears that I can use. Historical data is a...

Gummi Bear DOE: Replicates and Center Points, Part 1

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 to center points when we talked about 2-level designs previously. Center points are experimental runs with the all of the continuous factor settings set halfway between the low level and the high...

Gummi Bear DOE: Selecting Your Experimental Design Resolution

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 resolution in DOE as similar to choosing between cameras with 10 or 20 megapixels. In both designed experiments and with cameras, higher resolution is generally better. However, depending on your goal,...

Gummi Bear DOE: Choosing a Design

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:

What Type of Design do I Need?

Here's a series of questions to ask:

1. Is it reasonable to assume there is a linear relationship between the factors and the response?

If you answer "yes" to that question, a factorial design is probably a good choice.

Remember,...

Design of Experiments: "Fractionating" and "Folding" a DOE

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 business, we need to perform experiments to identify the factors that have a significant effect. The objective of DOE is to reduce experimental costs—the number of tests—as much as possible while studying as...

Why Is It Always Better to Perform a Design of Experiments (DOE) Rather than Change One Factor at a Time?

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.

Intuitively, when facing a complex problem, we tend to test different solutions as soon as they come to our mind. When we understand that one solution will not work, we will then look for the next...

What the Heck is a Split-Plot Design, and Why Would I Want It?

In previous posts on design of experiments, or DOE, we’ve covered:

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 have an agricultural mind, as many scientists did back when they were inventing the name of this method, you’ll appreciate the language about "splitting a plot." Suppose that you wanted to study a...

What the Heck is a Plackett-Burman Design, and Why Would I Want It?

Last time, we talked about why to use designs with just two levels. Now it’s time to discuss the two-level options in design of experiments, starting with Plackett-Burman designs.

Plackett-Burman designs exist so that you can quickly evaluate lots of factors to see which ones are important. Plackett-Burman designs are often called “screening designs” because they help you screen out unimportant factors.

This fits in with our previously-stated goal for design of experiments: learn as much as possible from the smallest amount of data. If we’re not sure a factor is important, we don’t want...

What the Heck is a 2-level Design and Why Would I Want It?

In the last post, we discussed how general full factorial designs let you study factors at more than two levels. The remaining 4 options that Minitab offers for factorial design of experiments are all 2-level designs, including the Plackett-Burman design.

Because there are 4 different kinds of 2-level designs, one of which is selected by default, you can probably guess that 2-level designs are quite popular. So what’s special about a 2-level design, and why would we use one?

One of the benefits of using design of experiments to plan data collection is to learn as much as possible from the...

Gummi Bear DOE: General Full Factorial Designs

Having spent some time figuring out what to do with the different variables for our gummi bear experiment, it’s time to get into Design of Experiments with Minitab. Opening Stat > DOE > Factorial > Create Factorial Design presents you with 5 options to choose from immediately:



The dialog box is helpfully explaining that some of these designs are for different numbers of factors. For example, if you want to create an experiment to study more than 15 factors in Minitab Statistical Software, you’re directed to a Plackett-Burman design.

This is somewhat helpful if you know what a factor is....

Evaluating Statistical Interactions with Ketchup and Soy Sauce

Do you prefer ketchup or soy sauce?

If someone asked you this question, your answer would likely depend upon what you were eating. You probably wouldn't dunk your spicy tuna roll in ketchup. And most people (pregnant moms-to-be excluded) don't seem to fancy eating soy sauce with hot French fries.

A Common Error When Using ANOVA or DOE to Assess Factors

Modeling techniques such as ANOVA or Design of Experiments (DOE) can determine if factors of interest impact a process. For example, you may want to evaluate how various time and temperature settings affect product quality. Or you may want to...

Gummi Bear DOE: The Importance of Randomization

If you pay close attention to this series on using gummi bears to understand design of experiments, you noticed that in my last post I mentioned pressure as a variable for the first time. Pressure wasn’t on the fishbone diagram that I used when planning variables, even though it’s just as obvious as temperature and humidity.

I’ve been referring to the fishbone diagram quite a bit, but I didn’t spend much time creating it nor did I gather anyone else’s input. Therefore, there are probably lots of unconsidered variables that could make a difference in how far the gummi bears traveled. I can...