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A Different Look at the New Medicare Data

It’s been an exciting week to be interested in Medicare data. On April 9th,  the American government opened up data from the Centers for Medicare and Medicaid Services (CMS) that show charges made to Medicare and payments received by over 880,000 entities. If you went to Bing on Monday, April 14, at about 12:30, chose to look at news stories, and typed Medicare money into the search box, here’s a sampling of what you got:

Medicare doctors: Who gets the big bucks & for what
The Medicare Data’s Pitfalls
Medicare Data Shines Light on Billions Paid to TX Doctors
Political Ties of Top Billers for...

Re-analyzing Wine Tastes with Minitab 17

In April 2012, I wrote a short paper on binary logistic regression to analyze wine tasting data. At that time, François Hollande was about to get elected as French president and in the U.S., Mitt Romney was winning the Republican primaries. That seems like a long time ago…

Now, in 2014, Minitab 17 Statistical Software has just been released. Had Minitab 17, been available in 2012, would have I conducted my analysis in a different way?  Would the results still look similar?  I decided to re-analyze my April 2012 data with Minitab 17 and assess the differences, if there are any.

There were no...

What Can Classical Chinese Poetry Teach Us About Graphical Analysis?

A famous classical Chinese poem from the Song dynasty describes the views of a mist-covered mountain called Lushan.

The poem was inscribed on the wall of a Buddhist monastery by Su Shi, a renowned poet, artist, and calligrapher of the 11th century.

Deceptively simple, the poem captures the illusory nature of human perception.
 

   Written on the Wall of West Forest Temple

                                      --Su Shi
 
  From the side, it's a mountain ridge.
  Looking up, it's a single peak.
  Far or near, high or low, it never looks the same.
  You can't know the true face of Lu Mountain
  When...

What if the NCAA tournament wasn’t single elimination?

Connecticut just defeated Kentucky to win the NCAA Men's Basketball Championship. The game had the highest combined seeding of any championship game in NCAA tournament history. This shows that while a single elimination tournament can be very entertaining, it doesn’t always determine who the “best” team is. In fact, despite winning the championship, Connecticut is still ranked 8th in the Pomeroy Ratings and 10th in the Sagarin Predictor Rankings. Though Connecticut played the best basketball the past 3 weeks, it would be folly to ignore the 30 games they played before that!

But although I’d...

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...

Introducing the Bubble Plot

When you're evaluating a dataset, graphical analysis can be very important. While an analysis like a regression or ANOVA can be backed up by numbers, being able to visualize how your dataset is behaving can be even more convincing than a group of p-values—especially to those who aren’t trained in statistics.

For example, let’s look at a few variables we think may be correlated. In this specific example, we will take the Unemployment Rate and the Crime Rate for each state in the U.S. We have 3 columns of data in Minitab: C1, which contains the State Name; C2, which contains the Crime Rate; and...

Control Chart Tutorials and Examples

The other day I was talking with a friend about control charts, and I wanted to share an example one of my colleagues wrote on the Minitab Blog.  Looking back through the index for "control charts" reminded me just how much material we've published on this topic.

Whether you're just getting started with control charts, or you're an old hand at statistical process control, you'll find some valuable information and food for thought in our control-chart related posts. 

Different Types of Control Charts

One of the first things you learn in statistics is that when it comes to data, there's no...

Did Welch’s ANOVA Make Fisher's Classic One-Way ANOVA Obsolete?

One-way ANOVA can detect differences between the means of three or more groups. It’s such a classic statistical analysis that it’s hard to imagine it changing much.

However, a revolution has been under way for a while now. Fisher's classic one-way ANOVA, which is taught in Stats 101 courses everywhere, may well be obsolete thanks to Welch’s ANOVA.

In this post, I not only want to introduce you to Welch’s ANOVA, but also highlight some interesting research that we perform here at Minitab that guides the implementation of features in our statistical software.

One-Way ANOVA Assumptions

Like any...

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...

Equivalence Testing for Quality Analysis (Part II): What Difference Does the Difference Make?

My previous post examined how an equivalence test can shift the burden of proof when you perform hypothesis test of the means. This allows you to more rigorously test whether the process mean is equivalent to a target or to another mean.

Here’s another key difference: To perform the analysis, an equivalence test requires that you first define, upfront, the size of a practically important difference between the mean and the target, or between two means.

Truth be told, even when performing a standard hypothesis test, you should know the value of this difference. Because you can’t really evaluate...

Equivalence Testing for Quality Analysis (Part I): What are You Trying to Prove?

With more options, come more decisions.

With equivalence testing added to Minitab 17, you now have more statistical tools to test a sample mean against target value or another sample mean.

Equivalence testing is extensively used in the biomedical field. Pharmaceutical manufacturers often need to test whether the biological activity of a generic drug is equivalent to that of a brand name drug that has already been through the regulatory approval process.

But in the field of quality improvement, why might you want to use an equivalence test instead of a standard t-test?

Interpreting Hypothesis...

The Best European Football League: What the CTQ’s and Minitab Can Tell Us

by Laerte de Araujo Lima, guest blogger

In a previous post (How Data Analysis Can Help Us Predict This Year's Champions League), I shared how I used Minitab Statistical Software to predict the 2013-2014 season of the UEFA Champions league. This involved the regression analysis of main critical-to-quality (CTQ) factors, which I identified using the “voice of the customer” suggestions of some friends.

Since that post was published, my friends have stopped discussing the UEFA Champions league—they were convinced by the results I shared.

But now they’ve challenged me to use Six Sigma tools to...

Using Statistics to Show Your Boss Process Improvements

Ughhh... your process is producing some parts that don't meet your customer's specifications! Fortunately, after a little hard work, you find a way to improve the process.

However, you want to perform the appropriate statistical analysis to back up your findings and make it easier to explain the process improvements to your boss. And it's important to remember that your boss is much like the boss in Eston's posts -- he's not too familiar with statistics, so you'll have to take it slow and show lots of "visual aids" in your explanation. How should you begin? 

Enter before-and-after process...

Got Good Judgment? Prove It with Attribute Agreement Analysis

Many Six Sigma and quality improvement tools could be applied in other areas. For example, I wonder whether my son's teachers could benefit from a little attribute agreement analysis. 

He seemed frustrated the other day when I picked him up at school. He'd been working on a presentation that needed to be approved by his teachers. (My son attends a charter school, and each class is taught by a two-person teaching team.)

"What's wrong?" I asked when he clambered into the car with a big sigh.

My son explained that he'd given the presentation to teacher Jennifer that morning. A few minor suggestions...

Five Ways to Make Your Control Charts More Effective

Have you ever wished your control charts were better?  More effective and user-friendly?  Easier to understand and act on?  In this post, I'll share some simple ways to make SPC monitoring more effective in Minitab.

Common Problems with SPC Control Charts

I worked for several years in a large manufacturing plant in which control charts played a very important role. Virtually thousands of SPC (Statistical Process Control) charts were used to monitor processes, contamination in clean rooms, monitor product thicknesses and shapes as well as critical equipment process parameters. Process engineers...

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...

Predicting the 2014 NCAA Tournament

Once again it’s time for the madness of March to begin! Which teams have the best shot of going to the final four? Is there a team that might become this year’s Florida Gulf Coast? And do any of the 16 seeds have a realistic shot of beating a 1 seed? Well sit back, because we’re going to answer all of that and more!  Somebody tell Cinderella to get her glass slippers, it’s time to go dancing!

Which Ranking System to Use

Before we get to the bracket, we need to decide on which ranking system to use. Because we want to use these rankings for predicting future outcomes, we want a system that uses...

Who's More (or Less) Irish?

B'gosh n' begorrah, it's St. Patrick's Day today!

The day that we Americans lay claim to our Irish heritage by doing all sorts of things that Irish people never do. Like dye your hair green. Or tell everyone what percentage Irish you are.

Despite my given name, I'm only about 15% Irish. So my Irish portion weighs about 25 pounds. It could be the portion that hangs over my belt due to excess potatoes and beer.

Today, many American cities compete for the honor of being "the most Irish." Who deserves to take top honors? Data from the U.S. Census Bureau can help us decide.

The Minitab bar chart below...

Analyzing “Luck” in College Basketball: Part II

Two months ago, I used Ken Pomeroy’s luck statistic to analyze the “luckiest” and “unluckiest” teams in college basketball. What Ken’s luck statistic is really looking at is close games. If you win most of your close games, you'll have a high luck statistic in the Pomeroy Ratings. Lose most of your close games, and your luck statistic will be low.

I looked at the winning percentages in close games of the 20 luckiest teams, 20 unluckiest teams, and 20 teams right in the middle. Sure enough the lucky group won most of their close games, the unlucky group lost most, and the middle group won just...

How to Handle Extreme Outliers in Capability Analysis

Transformations and non-normal distributions are typically the first approaches considered when the when the Normality test fails in a capability analysis. These approaches do not work when there are extreme outliers because they both assume the data come from a single common-cause variation distribution. But because extreme outliers typically represent special-cause variation, transformations and non-normal distributions are not good approaches for data that contain extreme outliers.

As an example, the four graphs below show distribution fits for a dataset with 99 values simulated from a...