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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...

Unleash the Power of Linear Models with Minitab 17

We released Minitab 17 Statistical Software a couple of days ago. Certainly every new release of Minitab is a reason to celebrate. However, I am particularly excited about Minitab 17 from a data analyst’s perspective. 

If you read my blogs regularly, you’ll know that I’ve extensively used and written about linear models. Minitab 17 has a ton of new features that expand and enhance many types of linear models. I’m thrilled!

In this post, I want to share with my fellow analysts the new linear model features and the benefits that they provide.

New Linear Model Analyses in Minitab 17

We’ve added...

Revisiting the Relationship between Rushing and NFL Wins with Binary Fitted Line Plots

Back in November, I wrote about why running the football doesn’t cause you to win games in the NFL. I used binary logistic regression to look at the relationship between rush attempts (both by the lead rusher and by the team) and wins. The results showed that the model for rush attempts by the lead rusher and wins fit the data poorly. But the model for team rush attempts and wins did fit the data well (although we went on to show that the team rushing attempts wasn’t causing the winning).

We were able to conclude this by looking at the p-value and goodness-of-fit tests. But what if we wanted...

Say "I Love You" with Data on Valentine's Day

When we think about jobs with a romantic edge to them, most of us probably think of professions that involve action or danger.  Spies, soldiers, cops, criminals -- these are types of professions romantic leads have. Along with your occasional musician, reporter, or artist, who don't have the action but at least bring drama.

But you know who never shows up as a romantic lead?  Quality improvement professionals, that's who.  Can you name just one movie that features a dedicated data analyst or quality practitioner as the love interest...just one?  No, you can't.  Doesn't exist.

Love of Quality:...

Is Your Statistical Software FDA Validated for Medical Devices or Pharmaceuticals?

We're frequently asked whether Minitab has been validated by the U.S. Food and Drug Administration (FDA) for use in the pharmaceutical and medical device industries.

Minitab does extensive testing to validate our software internally, but Minitab’s statistical software is not—and cannot be—FDA-validated out-of-the-box.

Nobody's can.

It is a common misconception that software vendors can go through a certification process to achieve FDA software validation. It's simply not true.

Software vendors who claim their products are FDA-validated should be scrutinized. It is up to the software purchaser to...

Busting a Myth about the NHL and Olympic Games

In the sports world, it is generally accepted that the NHL players who participate in the Olympics (approximately 20%) put their NHL team at a disadvantage for the remainder of the season.

The NHL season does stop during the Olympic Games, but the thought is that the best NHL players leave their team to play the extra games, which will tire them out for the remainder of the NHL regular season and playoffs.

But do the data really support that conventional wisdom?

Relationship Between NHL Performance and Olympic Selection

First, I want to mention there is a relationship between the number of players...

Are Atlanta's Winters Getting Colder and Snowier?

Atlanta was a mess on January 28th, 2014.  Thousands were trapped on the roads overnight while others managed to get to roadside stores to camp out. Thousands of students were forced to spend the night in their schools and the National Guard was called in to get them home. Many wondered how less than three inches of snow could cripple the city, particularly when Atlanta had experienced a similar storm in 2011?

This traumatic event, the recollection of recent snow storms, and now the current storm prompted some to wonder whether Atlanta has been experiencing more cold and snow than before. How...

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...

(We Just Got Rid of) Three Reasons to Fear Data Analysis

Today our company is introducing Minitab 17 Statistical Software, the newest version of the leading software used for quality improvement and statistics education.    So, why should you care? Because important people in your life -- your co-workers, your students, your kids, your boss, maybe even you -- are afraid to analyze data.    There's no shame in that. In fact, there are pretty good reasons for people to feel some trepidation (or even outright panic) at the prospect of making sense of a set of data.

I know how it feels to be intimidated by statistics. Not long ago, I would do almost...

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...

Using nonparametric analysis to visually manage durations in service processes

My main objective is to encourage greater use of statistical techniques in the service sector and present new ways to implement them.

In a previous blog, I presented an approach you can use  to identify process steps that may be improved in the service sector (quartile analysis). In this post I'll show how nonparametric distribution analysis may be implemented in the service sector to analyze durations until a task is completed.

Knowing how much time you need to complete a task may be very useful when assessing process efficiency, and is an important factor in many businesses.

Consider a...

A Statistical History of the Super Bowl

Super Bowl Sunday is right around the corner! But instead of trying to break down and predict the outcome of the game (which will likely come down to turnovers, which are impossible to predict), I’m going to look at some different statistics from previous Super Bowls. How many close games have there been? How much has the price for a 30-second add increased over the years? Which state has hosted the Super Bowl the most times? I’ll look at them all!

The Actual Game

First, I’m going to look at different things that have happened in the game. Five of the last 6 Super Bowls have been decided...

Winning a Super Bowl Grid Pool: Frequency of Score Combinations in the NFL

It has come to my attention recently that amidst the fun of attending Super Bowl parties and watching the 2nd-most viewed sporting event on earth there are some people—seedy characters with questionable pasts, I'm sure—who are betting on the game! 

Now, as gambling on sporting events is highly regulated and illegal in almost every state, I'm confident that reports of this are overblown and that the fine, upstanding readers of this blog are not among those taking part in such an activity.

But if you happen to live in an area where such things are legal and you choose to participate, then you...

Analyzing “Luck” in College Basketball: Part 1

College basketball stat guru Ken Pomeroy uses advanced metrics to rank every NCAA Division I basketball team. Amongst the numerous statistics he tracks is one called "Luck."

This statistic is calculated as the difference between a team’s actual winning percentage, and what one would expect their winning percentage to be based on how many points they score and how many they allow.

What it really boils down to is close games. In theory, you should win about half of your close games and lose half. If you win most of your close games, you'll have a high luck statistic in the Pomeroy Ratings. Lose...

Minitab illustrates the need for fire safety

The January/February issue of Men’s Health includes an article by Michael Perry with photographs by Eric Ogden titled “Voices from the Flames.” The article contains a lot of statistics that I didn’t know about fires in contemporary America. As a statistician, I like articles with statistics. While this article included a satisfying number of statistics, graphs that would make them easy to understand were absent. So in the interests of communicating the importance of fire safety, I thought I’d take a minute to make some graphs myself, inspired by some of the statistics that Perry uses....

Setting the Stage: Accounting for Process Changes in a Control Chart

When looking at a control chart, it’s important to know that the data we are looking at is accurate. Let’s face it, if the control limits we are looking at don’t really reflect what’s actually happening in our process, what does it matter if our points fall within the limits, or a little bit outside?

Let’s take a trip down to the widget factory, where widgets are being produced in all shapes and sizes. We’re going to take a look at one particular widget, and the time it takes for that particular widget to be produced (in minutes).

In a Minitab Statistical Software datasheet, we have two months...

A Statistical Look at How Turnovers Impacted the NFL Season

“Turnovers are like ex-wives. The more you have, the more they cost you.” – Dave Widell, former Dallas Cowboys lineman

It doesn’t take witty insight from a former NFL player to realize how big an impact turnovers can have in a football game. Every time an announcer talks about “Keys to the Game,” winning the turnover battle is one of them. And as Cowboys fans know all too well, an ill-timed interception can ruin not only your chances of winning that game, but it can ruin your entire season, too.

But hold on a minute. A few weeks ago, Andrew Luck and the Colts proved that you could still win a...

Regression Analysis: How to Interpret S, the Standard Error of the Regression

R-squared gets all of the attention when it comes to determining how well a linear model fits the data. However, I've stated previously that R-squared is overrated. Is there a different goodness-of-fit statistic that can be more helpful? You bet!

Today, I’ll highlight a sorely underappreciated regression statistic: S, or the standard error of the regression. S provides important information that R-squared does not.

What is the Standard Error of the Regression (S)?

S becomes smaller when the data points are closer to the line.

In the regression output for Minitab statistical software, you can find...

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...

Applying Six Sigma to a Small Operation

Using data analysis and statistics to improve business quality has a long history. But it often seems like most of that history involves huge operations. After all, Six Sigma originated with Motorola, and became adopted by thousands of other businesses after it was adopted by a little-known outfit called General Electric.

There are many case studies and examples of how big companies used Six Sigma methods to save millions of dollars, slash expenses, and improve quality...but when they read about the big dogs getting those kind of results, a lot of folks hear a little voice in their heads...