Over on the Indium Corporation's blog, Dr. Ron Lasky has been sharing some interesting ideas about using the Weibull distribution in electronics manufacturing. For instance, check out this discussion of how dramatically an early first-failure can affect an analysis of a part or component (in this case, an alloy used to solder components to a circuit board). 

This got me thinking again about all the different situations in which the Weibull distribution can help us make good decisions. The main reason Weibull is so useful is that it's very flexible in fitting different types of data, because it...

When I talk to quality professionals about how they use statistics, one tool they mention again and again is design of experiments, or DOE. I'd never even heard the term before I started getting involved in quality improvement efforts, but now that I've learned how it works, I wonder why I didn't learn about it sooner. If you need to find out how several factors are affecting a process outcome, DOE is the way to go. 

Somewhere in school you probably learned, like I did, that when you do an experiment you need to hold all the factors constant except for the one you're studying. That seems simple...

Life is full of choices. Some are simple, such as what shirt to put on in the morning (although if you’re like me, it’s not so much of a “choice” as it is throwing on the first thing you grab out of the closet). And some choices are more complex. In the quality world, you might have to determine which distribution to choose for your capability analysis or which factor levels to use to bake the best cookie in a design of experiments. But all of these choices pale in comparison* to the most important decision you have to make each year: which college basketball teams to pick during March...

New to confidence intervals?  Here are some important things to keep in mind!

Confidence Intervals:

• are used to estimate population parameters (commonly the process mean, standard deviation, % of defective units, or even capability indices). 
• provide more meaningful information than any random sample statistic for characterizing the population.

MINI-TIP:
See “Tip 1: Every sample statistic is a little bit wrong.”

When your 95% confidence interval for the mean is (μlow, μhigh), you can be 95% confident that the population (process) mean, μ, is between μlow and μhigh …and 5% confident that μ is not betwe...

Serving cat food? I sure hope you've set your alpha
level high enough.

"Bad kitty!" That's a phrase you almost never hear, but even we cats make the occasional mistake. I was reminded of this recently as I watched my human trying to analyze some data. People frequently make mistakes when they test a hypothesis with data analysis. Specifically, they can make either Type I or Type II errors.   

When I first started reading my human's statistics textbooks a few years ago, this idea seemed awfully silly to me. We cats appreciate being direct, and you either get the answer correct or you don't. I...

When Alex Smith travels to Seattle, he has to go up against 67,000 screaming Seahawk fans that make Seattle one of the loudest stadiums in football. When Joe Flacco goes into Pittsburgh, he has to overcome 65,000 Steelers fans clad in black and gold and waving Terrible Towels. And when Matt Schaub plays in Jacksonville he has to, well...people do go to football games in Jacksonville, right?

Either way, all three scenarios have one thing in common. The home field advantage is exactly the same.

Whether you have a sold out stadium full of rambunctious fans, or the stadium is half full, the home...

I love talking to people who use data and analysis to improve processes and quality. As I've worked with customers to tell their stories, my definition of "quality" has expanded. In some cases, data has been used not just to improve quality in terms of reducing defects, but even to demonstrate to regulators that a company is already meeting or exceeding regulatory requirements. 
A few years ago, I spoke with some of the quality experts at a large energy company. This company's business included delivering natural gas to 1.2 million customers in a midwestern state.   Remote systems on about...

Marlowe the Stats Cat here. That guy I share my house with left his laptop unattended again, and I spent the evening doing searching the web for news about one of my favorite subjects: salmon. Yum. But I wound up getting more than a collection of cool salmon pictures...I also got a better understanding of the role the size of a dataset plays when you're doing a hypothesis test.  

You see, my search led me to this paper that summarized a 2009 analysis of neuroimaging data collected from a frozen salmon. Yes, you did read that correctly: some people with Ph.D.'s actually ran an MRI on a dead...

Since we introduced new control charts in Minitab 16.2, I’ve been waiting to come across some real data I could use to showcase their awesome power. My friends, this day has come! I am about to reveal a perhaps unconventional use of the Laney P' chart to investigate national cycling data in the UK. So we’re not looking at any real process here, which is how the P' chart is usually used, just data from a national study about cycling habits. Why? Because this dataset gives us a prime example (see what I did there…?) of overdispersion issues that can cause problems when we use standard P control...

Last time, we talked about center points and replicates in design of experiments. It turns out that both are tools that you can use to increase the probability of finding a statistically significant difference. But what we really want to know is, how many center points and replicates should be in the gummi bear experiment? To answer that question, we have to estimate the standard deviation of the distances the gummi bears go.

Estimating the Standard Deviation When You Do Design of Experiments

I do have an old data set from some students launching gummi bears that I can use. Historical data is a...

This looks like a typical Mythbusters experiment!

Statistics can be unintuitive. What’s a large difference? What’s a large sample size? When is something statistically significant? You might think you know, based on experience and intuition, but you really don’t know until you actually run the analysis. You have to run the proper statistical tests to know what the data are telling you!

Even experts can get tripped up by their hunches, as we'll see.

In my family, we’re huge fans of the Mythbusters. This fun Discovery Channel show mixes science and experiments to prove or disprove various myths,...

Last time, we talked about what resolution means in design of experiments (DOE). After you choose your resolution in Minitab Statistical Software, you need to choose the number of center points and the number of replicates for corner points. We can consider these two questions together because they’ll help determine the total size of the experiment.

Using center points to check your model

I alluded to center points when we talked about 2-level designs previously. Center points are experimental runs with the all of the continuous factor settings set halfway between the low level and the high...

The 2012 U.S. presidential campaign is kicking into high gear. And you know what that means.

Political memes will soon be hatching from their electronic eggs, flying through myriad channels of the media, and buzzing annoyingly in your ears.

Memes are kernels of content that spread rapidly across the internet. Love them or hate them, you can’t deny their proliferation or their impact on our mass consciousness.

Remember the 2008 campaign? Lipstick on a pig? Joe the Plumber?

To explore the dynamic life cycle of memes, researchers at Cornell and Stanford tracked the top memes from the 2008...

It’s election year, and the Presidential campaign is picking up! These are exciting times for my buddy and I who are political junkies. Bring on the banners, slogans, rhetoric, and debates. Our TVs will be filled with ads about everything from financial policy to energy prices. Barack Obama and Mitt Romney may be in our living rooms more often than many family members!

We not only follow all of the races but we make small, friendly bets about the outcomes. However, the winnings pale in importance to the bragging rights! Each bet takes on a life of its own and winning the bet almost becomes more...

The other day I stopped at a fast food joint for the first time in a while.

After I ordered my food, the cashier asked me if I wanted to upgrade to the “Super-Smiley-Savings-Meal” or something like that, and I said, “Sure.”  

When it came, I was astounded to see the gargantuan soda cup. I don’t know how many ounces it was, but you could have bathed a dachshund in it.

If I drank all the Coke that honkin' cup could hold, the megadose of sugar and caffeine would launch me into permanent orbit around Earth.

That huge cup made me think of sample size.

Generally, having more data is a good thing. But if...

Collecting information for data analysis is like tasting fine wine—you want the right amount. Take too small a sip and you won't be able to assess it properly: you won't have enough information! But if you take a giant swig, your palate will be overwhelmed. That amount is just way more than you really need to make a solid recommendation.

So, how big a sip should you take? I'm no wine expert, so don't ask me. But when you need to figure out how much data you need to collect in order to answer a question with some degree of reliability, you need to look at statistical power and sample size. ...

I love all data, whether it’s normally distributed or downright bizarre. However, many people are more comfortable with the symmetric, bell-shaped curve of a normal distribution. It is not as intuitive to understand a Gamma distribution, with its shape and scale parameters, as it is to understand the familiar Normal distribution with its mean and standard deviation.

However, it's a fact of life that not all data follow the Normal distribution. Hey, a lot of stuff is just abnormal...er...non-normally distributed. How to understand and present the practical implications of your non-normal...

Groundhog Day is apparently a pretty big deal in Punxsutawney, Pennsylvania. According to one CBS article, organizers expected over 15,000 people to see the United States’ most popular groundhog last week.

For those unfamiliar with the legend, here's the idea: If Punxsutawney Phil sees his shadow after coming out of his hole, he will retreat and we will be forced to endure six more weeks of winter. If he doesn’t see his shadow, then we will be graced with an early spring. Sounds far-fetched, right? Let’s look at a 2-sample t-test to see if Phil has what it takes to be Punxsutawney’s chief...

I recently read an article that talked about the randomness of this year's shortened NBA season. Because of the lockout, the season will be only 66 games long, instead of 82. The article says that having a sample size that's 16 games fewer than normal means there's a lot of uncertainty about how the season will play out. But just how much more uncertainty will there be?

Fortunately, Minitab Statistical Software has an entire set of Power and Sample Size tools that can answer that question!

We want to investigate how the sample size affects the margin of error around the proportion of games a...

For my last several posts, I’ve been writing about the problems associated with variability. First, I showed how variability is bad for customers. Next, I showed how variability is generally harder to control than the mean. In this post, I’ll show yet one more way that variability causes problems!

Variability can dramatically reduce your statistical power during hypothesis testing. Statistical power is the probability that a test will detect a difference (or effect) that actually exists.

It’s always a good practice to understand the variability present in your subject matter and how it impacts...