Analyzing NFL Ticket Prices: How Much Would You Pay to See the Green Bay Packers?

The 2014-15 NFL season is only days away, and fans all over the country are planning their fall weekends accordingly. In this post, I'm going to use data analysis to answer some questions related to ticket prices, such as:

  • Which team is the least/most expensive to watch at home? 
  • Which team is the least/most expensive to watch on the road? 
  • If you are thinking of a road trip, which stadiums offer the largest ticket discount for your team?

For dedicated fans, this is far from a trivial matter.  As we'll see, fans of one team can get an average 48% discount on road-game tickets, while fans of two...

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...

Use a Line Plot to Show a Summary Statistic Over Time

If you’re already a strong user of Minitab Statistical Software, then you’re probably familiar with how to use bar charts to show means, medians, sums, and other statistics. Bar charts are excellent tools, but traditionally used when you want all of your categorical variables to have different sections on the chart. When you want to plot statistics with groups that flow directly from one category to the next, look no further than Minitab’s line plots. I particularly like line plots when I want to use time as a category, because I prefer the connect line display to separated bars.

I like to...

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...

Angst Over ANOVA Assumptions? Ask the Assistant.

Do you suffer from PAAA (Post-Analysis Assumption Angst)? You’re not alone.

Checking the required assumptions for a statistical  analysis is critical. But if you don’t have a Ph.D. in statistics, it can feel more complicated and confusing than the primary analysis itself.

How does the cuckoo egg data, a common sample data set often used to teach analysis of variance, satisfy the following formal assumptions for a classical one-way ANOVA (F-test)?

  • Normality
  • Homoscedasticity
  • Independence

Are My Data (Kinda Sorta) Normal?

To check the normality of each group of data, a common strategy is to display...

How Accurate are Fantasy Football Rankings? Part II

Previously, we looked at how accurate fantasy football rankings were for quarterbacks and tight ends. We found out that rankings for quarterbacks were quite accurate, with most of the top-ranked quarterbacks in the preseason finishing in the top 5 at the end of the season. Tight end rankings had more variation, with 36% of the top 5 preseason tight ends (over the last 5 years) actually finishing outside the top 10!

Now it’s time to move our attention to the running backs and wide receivers. Just like before, I went back the previous 5 seasons and found ESPN’s preseason rankings. For each...

“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...

How Could You Benefit from Between / Within Control Charts?

Choosing the right type of subgroup in a control chart is crucial. In a rational subgroup, the variability within a subgroup should encompass common causes, random, short-term variability and represent “normal,” “typical,” natural process variations, whereas differences between subgroups are useful to detect drifts in variability over time (due to “special” or “assignable” causes). Variation within subgroup is therefore used to estimate the natural process standard deviation and to calculate the 3-sigma control chart limits.

In some cases, however, identifying the correct rational subgroup is...

Taking the Training Wheels Off: Rethinking How Lean Six Sigma is Taught

Learning to ride a bike is a rite of passage for any kid, so much so that we even use the expression "taking the training wheels off" for all kinds of situations. We say it to mean that we are going to let someone perform an activity on their own after removing some safeguard, even though we know they will likely experience failures before becoming proficient at it.

You see, riding a bike requires one to learn three skills—how to pedal the bike, how to brake, and how to balance on the bike—and training wheels allow the child to master two of those three without the dangers associated with...

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.


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...

Quality Improvement in Healthcare: Showing if process changes actually improve the patient experience

Via Christi Health, the largest provider of healthcare in Kansas, operates a Center for Clinical Excellence that's made up of a team of quality practitioners, all who have Lean and Six Sigma training. I recently had the opportunity to talk with the team about the types of projects they're working on.

I learned not only about the areas of patient care where they are targeting improvements, but about one particular project the team completed to determine if process changes put into place in the hospital's emergency department actually improved the patient experience.

I thought it was an...

Is the Risk of an Ebola Pandemic Even Worth Worrying About?

In his post yesterday, my colleague Jim Colton applied binary logistic regression to data on the current ebola virus outbreak in Guinea, Liberia, and Sierra Leone, and revealed that, horrific as it is, this outbreak actually appears to have a lower death rate than some earlier ones. 

He didn't address the potential for a global ebola pandemic, but over the last few days more than enough leading publications have featured extremely scary headlines about this extremely remote possibility. Less reputable organizations have promulgated even more exaggerated stories, usually with some ludicrous...

How Deadly Is this Ebola Outbreak?

The current Ebola outbreak in Guinea, Liberia, and Sierra Leone is making headlines around the world, and rightfully so: it's a frightening disease, and last week the World Health Organization reported its spread is outpacing their response. Nearly 900 of  the more than 1,600 people infected during this outbreak have died, including some leading medical professionals trying to stanch the outbreak's spread. And yesterday, one of the American doctors who contracted the disease arrived back in the U.S. for treatment.

Many sources state that Ebola virus outbreaks have a case fatality rate of up to...

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...

Cuckoo for Quality: A Birdseye View of a Classic ANOVA Example

If you teach statistics or quality statistics, you’re probably already familiar with the cuckoo egg data set.

The common cuckoo has decided that raising baby chicks is a stressful, thankless job. It has better things to do than fill the screeching, gaping maws of cuckoo chicks, day in and day out.

So the mother cuckoo lays her eggs in the nests of other bird species. If the cuckoo egg is similar enough to the eggs of the host bird, in size and color pattern, the host bird may be tricked into incubating the egg and raising the hatchling. (The cuckoo can then fly off to the French Riviera, or...

How Accurate are Fantasy Football Rankings?

The calendar just flipped to August, meaning it’s time to get ready for fantasy football season! As you prepare for your draft, you will no doubt be looking at all sorts of rankings. But when the season is over, do you ever go back and see how accurate those rankings were? And are rankings for some positions more accurate than others? Well that’s exactly what we’re going to find out!

I went back the previous 5 seasons and found ESPN’s preseason rankings for quarterbacks, running backs, wide receivers, and tight ends. I know that different publications will have slightly different rankings, but...

Investigating Starfighters with Bar Charts: Function of a Variable

Last time, we went over Bar Charts you could create from Counts of Unique Values. However, sometimes you want to convey more information than just simple counts. For example, you could have a number of parts from different models. The number of occurrences themselves don't offer much value, so you may want a chart displaying the means, sums, or even standard deviations of the different parts. It's this case that we're after when we go to Graph > Bar Charts > Function of a Variable.

To illustrate this, I have a small data set investigating starfighters built in the Star Wars galaxy:


Blind Wine Part IV: The Participants

In Part I, Part II, and Part III we shared our experiment, the survey results, and the experimental results. To wrap things up, we're going to see if the survey results tied to the experimental results in any meaningful way...

First, we look at whether self-identified knowledge correlated to the total number of correct appraisals:

We have no evidence of a relationship (p = 0.795).  So we'll look at the number correct by how much each participant usually spends:

Again, no evidence of a relationship (p = 0.559).  

How about how many types each regularly buys?

There appears to be something here,...

Blind Wine Part III: The Results

In Part I and Part II we learned about the experiment and the survey, respectively. Now we turn our attention to the results...

Our first two participants, Danielle and Sheryl, enter the conference room and are given blindfolds as we explain how the experiment will proceed.  As we administer the tasting, the colors of the wine are obvious but we don't know the true types, which have been masked as "A," "B," "C," and "D." 

As Danielle and Sheryl proceed through each tasting, it is easy to note that they start off correctly identifying the color of each wine; it is also obvious that tasting...