# Statistics

Blog posts and articles about statistical principles in quality improvement methods like Lean and Six Sigma.

As we start off 2018, our eyes are on the winter weather, specifically low temperatures and snowfall. After 2015-2016's warmest winter on record and Chicago breaking records in 2017 with no snow sticking to the ground in January or February, our luck might have run out. We shall see, though. The Old Farmer's Almanac is reporting that 2017-2018 winter temperatures will be colder than last winter. If... Continue Reading
Are you going to be a witch today? Batman? Jedi? You're not alone according to National Retail Federation statistics on top costumes and Halloween spending trends. Last-minute candy shopping? Check out kidzworld.com’s list of the top 10 candies for Halloween. And of course, you have to plan your daily candy consumption to match the limits on free sugar recommended by the World Health Organization... Continue Reading

### 7 Deadly Statistical Sins Even the Experts Make

Do you know how to avoid them?

On the heels of Healthcare Quality Week last week, we wanted to share our conversation with Dr. Sandy Fogel, the surgical quality officer at Carilion Clinic in Roanoke, VA. Although he has been a practicing surgeon for nearly 40 years, Dr. Fogel’s enthusiasm for quality improvement makes it sound as if he is just getting started. After medical school at Washington University in St. Louis and... Continue Reading
Wildfires in California have killed at least 40 people and burned more than 217,000 acres in the past few weeks. Nearly 8,000 firefighters are trying to contain the blazes with the aid of more than 800 firetrucks, 70 helicopters and 30 planes. In remote areas difficult to access by firetruck, smokejumpers may be needed to parachute in to fight the fires. But danger looms before a smokejumper even... Continue Reading
Overfitting a model is a real problem you need to beware of when performing regression analysis. An overfit model result in misleading regression coefficients, p-values, and R-squared statistics. Nobody wants that, so let's examine what overfit models are, and how to avoid falling into the overfitting trap. Put simply, an overfit model is too complex for the data you're analyzing. Rather than... Continue Reading
Maybe you're just getting started with analyzing data. Maybe you're reasonably knowledgeable about statistics, but it's been a long time since you did a particular analysis and you feel a little bit rusty. In either case, the Assistant menu in Minitab Statistical Software gives you an interactive guide from start to finish. It will help you choose the right tool quickly, analyze your data... Continue Reading
Control charts take data about your process and plot it so you can distinguish between common-cause and special-cause variation. Knowing the difference is important because it permits you to address potential problems without over-controlling your process.   Control charts are fantastic for assessing the stability of a process. Is the process mean unstable, too low, or too high? Is observed... Continue Reading
In statistics, as in life, absolute certainty is rare. That's why statisticians often can't provide a result that is as specific as we might like; instead, they provide the results of an analysis as a range, within which the data suggest the true answer lies. Most of us are familiar with "confidence intervals," but that's just of several different kinds of intervals we can use to characterize the... Continue Reading
by Matthew Barsalou, guest blogger At the end of the first part of this story, a group of evil trouble-making chickens had convinced all of their fellow chickens to march on the walled city of Wetzlar, where, said the evil chickens, they all would be much happier than they were on the farm. The chickens marched through the night and arrived at Wetzlar on the Lahn as the sun came up. “Let us in!”... Continue Reading
by Matthew Barsalou, guest blogger Once upon a time, in the Kingdom of Wetzlar, there was a farm with over a thousand chickens, two pigs, and a cow. The chickens were well treated, but a few rabble-rousers among them got the rest of the chickens worked up. These trouble-making chickens looked almost like the other chickens, but in fact they were evil chickens.  By HerbertT - Eigenproduktion, CC... Continue Reading
The Six Sigma quality improvement methodology has lasted for decades because it gets results. Companies in every country around the world, and in every industry, have used this logical, step-by-step method to improve the quality of their processes, products, and services. And they've saved billions of dollars along the way. However, Six Sigma involves a good deal of statistics and data analysis,... Continue Reading
Six Sigma is a quality improvement method that businesses have used for decades—because it gets results. A Six Sigma project follows a clearly defined series of steps, and companies in every industry in every country around the world have used this method to resolve problems. Along the way, they've saved billions of dollars. But Six Sigma relies heavily on statistics and data analysis, and many... Continue Reading
In April 2017, overbooking of flight seats hit the headlines when a United Airlines customer was dragged off a flight. A TED talk by Nina Klietsch gives a good, but simplistic explanation of why overbooking is so attractive to airlines. Overbooking is not new to the airlines; these strategies were officially sanctioned by The American Civil Aeronautics Board in 1965, and since that time complex... Continue Reading
Can you trust your data?  That's the very first question we need to ask when we perform a statistical analysis. If the data's no good, it doesn't matter what statistical methods we employ, nor how much expertise we have in analyzing data. If we start with bad data, we'll end up with unreliable results. Garbage in, garbage out, as they say. So, can you trust your data? Are you positive?... Continue Reading
We had solar panels fitted on our property in 2011. Last year, we had a few problems with the equipment. It was shutting down at various times throughout the day, typically when it was very sunny, resulting in no electricity being generated. In summer 2016, I completed a statistical analysis in Minitab to confirm my suspicions that my solar panels were not working as well as they did when they were... Continue Reading
All processes have variation, some of which is inherent in the process, and isn't a reason for concern. But when processes show unusual variation, it may indicate a change or a "special cause" that requires your attention.  Control charts are the primary tool quality practitioners use to detect special cause variation and distinguish it from natural, inherent process variation. These charts graph... Continue Reading
There may not be a situation more perilous than being a character on Game of Thrones. Warden of the North, Hand of the King, and apparent protagonist of the entire series? Off with your head before the end of the first season! Last male heir of a royal bloodline? Here, have a pot of molten gold poured on your head! Invited to a wedding? Well, you probably know what happens at weddings in the show. ... Continue Reading
How many samples do you need to be “95% confident that at least 95%—or even 99%—of your product is good? The answer depends on the type of response variable you are using, categorical or continuous. The type of response will dictate whether you 'll use: Attribute Sampling: Determine the sample size for a categorical response that classifies each unit as Good or Bad (or, perhaps, In-spec or... Continue Reading
Have you ever had a probability plot that looks like this? The probability plot above is based on patient weight (in pounds) after surgery minus patient weight (again, in pounds) before surgery. The red line appears to go through the data, indicating a good fit to the Normal, but there are clusters of plotting points at the same measured value. This occurs on a probability plot when there are many... Continue Reading
Previously, I’ve written about when to choose nonlinear regression and how to model curvature with both linear and nonlinear regression. Since then, I’ve received several comments expressing confusion about what differentiates nonlinear equations from linear equations. This confusion is understandable because both types can model curves. So, if it’s not the ability to model a curve, what isthe... Continue Reading