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Using the G-Chart Control Chart for Rare Events to Predict Borewell Accidents

by Lion "Ari" Ondiappan Arivazhagan, guest blogger

In India, we've seen this story far too many times in recent years:

Timmanna Hatti, a six-year old boy, was trapped in a 160-feet borewell for more than 5 days in Sulikeri village of Bagalkot district in Karnataka after falling into the well. Perhaps the most heartbreaking aspect of the situation was the decision of the Bagalkot district administration to stop the rescue operation because the digging work, if continued further, might lead to collapse of the vertical wall created by the side of the borewell within which Timmanna had struggled for...

How Could You Benefit from Plackett & Burman Experimental Designs ?

Screening experimental designs allow you to study a very large number of factors in a very limited number of runs. The objective is to focus on the few factors that have a real effect and eliminate the effects that are not significant. This is often the initial typical objective of any experimenter when a DOE (design of experiments) is performed.

Table of Factorial Designs

Consider the table below. In Minitab, you can quickly access this table of factorial designs by selecting Stat > DOE > Factorial > Create Factorial Design... and clicking "Display Available Designs." The table tells us the...

A Fun ANOVA: Does Milk Affect the Fluffiness of Pancakes?

by Iván Alfonso, guest blogger

I'm a huge fan of hot cakes—they are my favorite dessert ever. I’ve been cooking them for over 15 years, and over that time I’ve noticed many variation in textures, flavor, and thickness. Personally, I like fluffy pancakes.

There are many brands of hotcake mix on the market, all with very similar formulations. So I decided to investigate which ingredients and inputs may influence the fluffiness of my pancakes.

Potential factors could include the type of mix used, the type of milk used, the use of margarine or butter (of many brands), the amount of mixing time, the...

“You’ve got a friend” in Minitab Support

I caught the end of Toy Story over the weekend, which is definitely one of my all-time favorite children’s movies. Now—unfortunately or fortunately—I can’t get Randy Newman's theme song,“You’ve Got a Friend in Me,” out of my head!

It's also got me thinking about the nature of friendship, and how "best friends forever" are supposed to always be there when you need them. And, not to get too maudlin about it, but just like Woody and Buzz eventually realize their friendship, all of us hope the professionals who use our software also realize that “you’ve got a friend” in Minitab.

Now what do I mean...

Making the Office Coffee Better with a Designed Experiment for Optimization

NOTE: This story will reveal how easy it can be to optimize settings using the statistical method called Design of Experiments, but it won't provide easy answers for making your own office coffee any better.

After her team’s ultimatum about the wretched office coffee, Jill used the design-of-experiments (DOE) tool in Minitab 17’s Assistant to design and analyze a screening study. Jill now knew that three of the factors she screened—the type of beans used, the number of cups brewed per pot, and the amount of grinding time the beans received—had a significant impact on the bitterness of coffee.

No...

Why Is the Office Coffee So Bad? A Screening Experiment Narrows Down the Critical Factors

NOTE: This story reveals how easy it can be to identify important factors using the statistical method called Design of Experiments. It won't provide easy answers for making your own office's coffee any better, but it will show you how you can begin identifying the critical factors that contribute to its quality.

At their weekly meeting, her team gave Jill an ultimatum: Make the coffee better.

The office coffee was terrible. Drinking it was like playing a game of chicken with your taste buds. Jill’s practice was to let someone else get the first cup of the day; if gagging and/or swearing soon...

Using Probability Plots to Understand Laser Games Scores

There is more than just the p value in a probability plot—the overall graphical pattern also provides a great deal of useful information. Probability plots are a powerful tool to better understand your data.

In this post, I intend to present the main principles of probability plots and focus on their visual interpretation using some real data.

In probability plots, the data density distribution is transformed into a linear plot. To do this, the cumulative density function (the so-called CDF, cumulating all probabilities below a given threshold) is used (see the graph below). For a normal...

Five Guidelines for Using P values

There is high pressure to find low P values. Obtaining a low P value for a hypothesis test is make or break because it can lead to funding, articles, and prestige. Statistical significance is everything!

My two previous posts looked at several issues related to P values:

In this post, I’ll look at whether P values are still helpful and provide guidelines on how to use them with these issues in mind.

Sir Ronald A Fisher

Are P Values Still Valuable?

Given...

When Will I Ever See This Statistics Software Again?

Minitab Statistical Software was born out of a desire to make statistics easier to learn: by making the calculations faster and easier with computers, the trio of educators who created the first version of Minitab sought to free students from intensive computations to focus on learning key statistical concepts. That approach resonated with statistics instructors, and today Minitab is the standard for teaching and learning statistics at more than 4,000 universities all over the world.

But many students seem to believe Minitab is used only in education. Search Twitter for "Minitab," and you're...

“Hello, How Can I Help You?”- A Look at Quality Improvement in Financial Services

It’s common to think that process improvement initiatives are meant to cater only to manufacturing processes, simply because manufacturing is where Lean and Six Sigma began. However, many other industries, in particular financial services and banking, also rely on data analysis and Lean Six Sigma tools to improve processes.

Rod Toro is a business process improvement manager at Edward Jones, and I recently got the chance to talk with him about a Lean Six Sigma project the service division at his company completed to improve customer satisfaction.

Edward Jones has been increasing the number of...

Analyze a DOE with the Assistant in Minitab 17

By now, you probably know that Minitab 17 includes Design of Experiments (DOE) in the Assistant. We already spent some time looking at 5 highlights when you create a screening experiment with the Assistant in Minitab 17.

But the Assistant can also help you make sense of the data you collect for your experiment. After you create a design with the Assistant, choose Assistant > DOE > Analyze and Interpret and you’re on your way. Exactly what you get depends on which type of design you’re analyzing, but there’s some really neat stuff to help you get the most out of your data. Here are 3...

ITEA Sneak-Peek: The Great Escape from Foam Defects

The 2014 ASQ World Conference on Quality and Improvement is coming up in early May in Dallas, and this year’s International Team Excellence Award Process (ITEA) will also come to a close at the conference, as winners from the finalist teams will be chosen for ASQ gold, silver, or bronze-level statuses.

What’s ITEA?

The annual ASQ ITEA process celebrates the accomplishments of quality improvement teams from a broad spectrum of industries from around the world. The ITEA is the only international team recognition process of its kind in the United States, and since 1985, more than 1,000 teams from...

Create a DOE Screening Experiment with the Assistant in Minitab 17

If you’ve been looking at Minitab 17, you’ve noticed a lot of new enhancements. For me, the biggest of these is the addition of Design of Experiments (DOE) to the Assistant. DOE in the Assistant has so many exciting aspects it’s hard to take it all in at once, but here are 5 highlights for when you plan and create a screening experiment:

1. Just-in-time guidance

If you’re lucky, you’ve had the chance to study DOE with an expert. If not, even the flow chart that opens with the Assistant to plan an experiment might seem intimidating. Fortunately, you don’t have to go scouring the thrift store for...

Opening Ceremonies for Bubble Plots and Poisson Regression

By popular demand, Release 17 of Minitab Statistical Software comes with a new graphical analysis called the Bubble Plot.

This exploratory tool is great for visualizing the relationships among three variables on a single plot.

To see how it works, consider the total medal count by country from the recently completed 2014 Olympic Winter Games. Suppose I want to explore whether there might be a possible association between the number of medals a country won and its maximum elevation. For that, I could use a simple scatterplot, right?

But say I want to throw a third variable into the mix, such as...

Histograms are Even Easier to Compare in Minitab 17

Minitab 17 came out yesterday and it’s got quite a few neat features in it. You can check some of them out on the What’s New in Minitab 17 page. But one of my very favorite things is related to one of my previous blog posts that showed how to make histograms that are easy to compare. Turns out, you don’t need those steps anymore. You can do it all with Minitab’s Assistant.

Here’s how to open the data that I’m using if you want to follow along.

  • Choose File > Open Worksheet.
  • Click Look in Minitab Sample Data Folder.
  • Select Cap.MTW and click Open.

You can still rearrange a paneled histogram to make...

Gauging Gage Part 3: How to Sample Parts

In Parts 1 and 2 of Gauging Gage we looked at the numbers of parts, operators, and replicates used in a Gage R&R Study and how accurately we could estimate %Contribution based on the choice for each.  In doing so, I hoped to provide you with valuable and interesting information, but mostly I hoped to make you like me.  I mean like me so much that if I told you that you were doing something flat-out wrong and had been for years and probably screwed somethings up, you would hear me out and hopefully just revert back to being indifferent towards me.

For the third (and maybe final) installment, I...

Gauging Gage Part 2: Are 3 Operators or 2 Replicates Enough?

In Part 1 of Gauging Gage, I looked at how adequate a sampling of 10 parts is for a Gage R&R Study and providing some advice based on the results.

Now I want to turn my attention to the other two factors in the standard Gage experiment: 3 operators and 2 replicates.  Specifically, what if instead of increasing the number of parts in the experiment (my previous post demonstrated you would need an unfeasible increase in parts), you increased the number of operators or number of replicates?

In this study, we are only interested in the effect on our estimate of overall Gage variation. Obviously,...

Applying Six Sigma to a Small Operation, Part 2

In my previous post, I shared a case study of how a small bicycle-chain manufacturing company in India used the DMAIC approach to Six Sigma to reverse declining productivity.

After completing the Define, Measure, and Analysis phases, the team had identified the important factors in the bushing creation process. Armed with this knowledge, they were now ready to make some improvements.

The Improve Phase

In the Improve phase, the team applied a statistical method called Design of Experiments (DOE) to optimize the important factors they'd identified in the initial phases.

Most of us learn in school...