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Creating a Shatterproof Process: Students Use Six Sigma to Improve Window Manufacturing

I had the opportunity to speak with a great group of students from the New Jersey Governor’s School of Engineering and Technology—a summer program for high-achieving high school students. Students in the program complete a set of challenging courses while working in small groups on real-world research and design projects that relate to the field of engineering. Governor’s School students are mentored by professional engineers as well as Rutgers University honors students and professors, and they often work with companies and organizations to solve real engineering problems.

The team of students...

Doggy DOE Part III: Analyze This!

What factors significantly affect how quickly my couch-potato pooch obeys the “Lay Down” command?

The cushiness of the floor surface? The tone of voice used? The type of reward she gets? How hungry she is?

I created a 1/8 fraction Resolution IV design for 7 factors and collected response data for 16 runs. Now it’s time to analyze the data in Minitab, using  Stat > DOE > Factorial > Analyze Factorial Design.

After removing insignificant terms from the model, one at a time, starting with the highest-order interaction, here's the final model:

Of the original 7 factors in the screening experiment,...

Four Tips on How to Perform a Regression Analysis that Avoids Common Problems

In my previous post, I highlighted recent academic research that shows how the presentation style of regression results affects the number of interpretation mistakes. In this post, I present four tips that will help you avoid the more common mistakes of applied regression analysis that I identified in the research literature.

I’ll focus on applied regression analysis, which is used to make decisions rather than just determining the statistical significance of the predictors. Applied regression analysis emphasizes both being able to influence the outcome and the precision of the predictions.

Tip...

Doggy DOE Part II: Create Your Design

Nala, our 6-year-old golden retriever, loves her dogma. That's her sitting in front of church on Sunday morning.

But she's not crazy about her catechism. For example, she doesn't always dutifully follow the "Lay Down" commandment.  

What factors may be influencing her response? We're performing a DOE screening experiment to find out.

In this post, we'll use Minitab Statistical Software to

  • Create the design for the experiment
  • Determine the confounding pattern for this design
  • Set up the data collection worksheet

Create the Design for the Experiment

In the previous post, we used the Display Design dialog...

Making a Difference in How People Use Data

A colleague of mine at Minitab, Cheryl Pammer, was recently featured in "A Statistician's Journey," a monthly feature that appears in the print and online versions of the American Statistical Association's AMSTAT News magazine.  

Each month, the magazine asks ASA members to talk about the paths they took to get to where they are today. Cheryl is a "user experience designer" at Minitab. In other words, she's one of the people who help determine how our statistical softwaredoes what it does, and tries to make it as helpful, useful, and beneficial as possible. Cheryl is always looking for ways to...

Doggy DOE Part I: Design on a Dime

Design of experiments (DOE) is an extremely practical and cost-effective way to study the effects of different factors and their interactions on a response.

But finding your way through DOE-land can be daunting when you're just getting started. So I've enlisted the support of a friendly golden retriever as a guide dog to walk us through a simple DOE screening experiment.

Nala, the golden retriever, is shown at right. Notice how patiently she sits as her picture is being taken. She's a  true virtuoso with the "Sit" command.

But "Lay Down" is another story...

Formulate the Objective

Although Nala know...

Using Prediction Intervals to Define Process Windows

Making parts that are truly interchangeable is a critical aspect of modern manufacturing. The same parts may be manufactured in different plants spread around the globe or by suppliers located far away. Parts need to be manufactured to specifications to ensure that they are almost identical to allow an easy assembly of new products.

Interchangeability is increasingly important in the service industry as well. Because customers expect similar standards from a service company wherever it does business around the globe, best practices need to be deployed throughout a company and...

Itchy, Sneezy, Stuffy: Delivering Relief with Nasal Spray and DOE

Recently, a customer called our Technical Support team about a Design of Experiment he was performing in Minitab Statistical Software. After they helped to answer his question, the researcher pointed our team to an interesting DOE he and his colleagues conducted that involved using nasal casts to predict the drug delivery of nasal spray.

The study has already been published, and you can read more about it here, but I wanted to highlight this use of the DOE tools in Minitab in this blog post.

Using Nasal Casts to Predict Nasal Spray Drug Delivery

The nose is a convenient route of administration...

Real-World Applications of Statistics

I received my B.S. in applied statistics in 1992 from Rochester Institute of Technology (R.I.T) and my master's in applied statistics from R.I.T's Center for Quality and Applied Statistics in 1993. I also completed Ph.D. coursework at The University of Washington and The Ohio State University.

While working towards my Statistics degrees, I further developed my industrial skills working at Xerox, IBM, and Kellogg's. Following my education, I spent two years in West Palm Beach, FL working as an engineering statistician at Pratt & Whitney, where I specialized in simulation, reliability, and...

Using Multi-Vari Charts to Analyze Families of Variations

When trying to solve complex problems, you should first list all the suspected variables identify the few critical factors and separate them from the trivial many, which are not essential to understanding the cause.

 

    

 

Many statistical tools enable you to efficiently identify the effects that are statistically significant in order to converge on the root cause of a problem (for example ANOVA, regression, or even designed experiments (DOEs)). In this post though, I am going to focus on a very simple graphical tool, one that is very intuitive, can be used by virtually anyone, and does not...

The Gentleman Tasting Coffee: A Variation on Fisher’s Famous Experiment

by Matthew Barsalou, guest blogger

In the 1935 book The Design of Experiments, Ronald A. Fisher used the example of a lady tasting tea to demonstrate basic principles of statistical experiments. In Fisher’s example, a lady made the claim that she could taste whether milk or tea was poured first into her cup, so Fisher did what any good statistician would do—he performed an experiment.

The lady in question was given eight random combinations of cups of tea with either the tea poured first or the milk poured first. She was required to divide the cups into two groups based on whether the milk or...

A Brief Illustrated History of Statistics for Industry

by Matthew Barsalou, guest blogger

The field of statistics has a long history and many people have made contributions over the years. Many contributors to the field were educated as statisticians, such as Karl Pearson and his son Egon Pearson. Others were people with problems that needed solving, and they developed statistical methods to solve these problems.

The Standard Normal Distribution

One example is Karl Gauss and the standard normal distribution, which is a key element in statistics. The distribution was used by Gauss to analyze astronomical data in the early nineteenth century and is...

Using Design of Experiments to Minimize Noise Effects

All processes are affected by various sources of variations over time. Products which are designed based on optimal settings, will, in reality, tend to drift away from their ideal settings during the manufacturing process.

Environmental fluctuations and process variability often cause major quality problems. Focusing only on costs and performances is not enough. Sensitivity to deterioration and process imperfections is an important issue. It is often not possible to completely eliminate variations due to uncontrollable factors (such as temperature changes, contamination, humidity, dust etc…).

Fo...

Quality Improvement in Financial Services

Process improvement through methodologies such as Six Sigma and Lean has found its way into nearly every industry. While Six Sigma had its beginnings in manufacturing, we’ve seen it and other process improvement techniques work very well in the service industry—from healthcare to more service-oriented business functions, such as human resources.

However, Six Sigma seems to have had a slower rate of adoption in financial services. I recently came across a great article about the challenges faced in the financial industry when it comes to successfully implementing a process improvement...

Studying Old Dogs with New Statistical Tricks: Bone-Cracking Hypercarnivores and 3D Surface Plots

A while back my colleague Jim Frost wrote about applying statistics to decisions typically left to expert judgment; I was reminded of his post this week when I came across a new research study that takes a statistical technique commonly used in one discipline, and applies it in a new way. 

The study, by paleontologist Zhijie Jack Tseng, looked at how the skulls of bone-cracking carnivores--modern-day hyenas--evolved. They may look like dogs, but hyenas in fact are more closely related to cats. However, some extinct dog species had skulls much like a hyena's. 

Tseng analyzed data from 3D...

What Is a t-test? And Why Is It Like Telling a Kid to Clean Up that Mess in the Kitchen?

A t-test is one of the most frequently used procedures in statistics.

But even people who frequently use t-tests often don’t know exactly what happens when their data are wheeled away and operated upon behind the curtain using statistical software like Minitab.

It’s worth taking a quick peek behind that curtain.

Because if you know how a t-test works, you can understand what your results really mean. You can also better grasp why your study did (or didn’t) achieve “statistical significance.”

In fact, if you’ve ever tried to communicate with a distracted teenager, you already have experience with...

Regression Analysis: How Do I Interpret R-squared and Assess the Goodness-of-Fit?

After you have fit a linear model using regression analysis, ANOVA, or design of experiments (DOE), you need to determine how well the model fits the data. To help you out, Minitab statistical software presents a variety of goodness-of-fit statistics. In this post, we’ll explore the R-squared (R2 ) statistic, some of its limitations, and uncover some surprises along the way. For instance, low R-squared values are not always bad and high R-squared values are not always good!

What Is Goodness-of-Fit for a Linear Model?

Definition: Residual = Observed value - Fitted value

Linear regression...

Expanding the Role of Statistics to Areas Traditionally Dominated by Expert Judgment

Should this doctor consult a regression model?

In a previous post, I wrote about how the field of statistics is more important now than ever before due to the modern deluge of data. Because you’re reading Minitab's statistical blog, I’ll assume that we’re in agreement that statistics allows you to use data to understand reality. However, I’d also bet that you’re picturing important but “typical” statistical studies, such as studies where Six Sigma analysts determine which factors affect product quality. Or perhaps medical studies, like determining the effectiveness of flu shots.

In this post,...

How to “Expand” Your Gage Studies

As we said in yesterday’s post, it’s been exciting for Minitab to be a supporter of the ASQ World Conference on Quality and Improvement taking place this week in Indianapolis. There have been many great sessions and an abundance of case studies shared that highlight how quality teams worldwide are improving the performance of their businesses.

One session that generated a lot of interest from the conference participants was conducted by Minitab trainers Lou Johnson, Daniel Griffith and Jim Colton.

Their presentation, Sampling Plan for Expanded Gage R&R Studies, covered Gage R&R studies and how...

The Diversity (and Consistency) of Quality Improvement: the 2013 ASQ ITEA Presentations

I'm in the airport at Indianapolis, waiting to go home after three exciting days at the 2013 American Society for Quality World Conference.  As I write this, it's Wednesday evening after the conference has closed, and it turns out my flight has been delayed.

This could give me ample opportunity to muse about the quality issues that might keep me from reaching central Pennsylvania tonight. But I'm kind of pumped up, so I'm more interested in thinking about what I've experienced and seen over the past few days. This is the kind of event that makes you want to keep focusing on the positive, not...