Creating a Custom Report using Minitab, part 2

Now that you’ve seen how to automatically import data and run analyses in my previous post, let’s create the Monthly Report!

I will be using a Microsoft Word Document (Office 2010) and adding bookmarks to act as placeholders for the Graphs, statistics, and boilerplate conclusions.

Let’s go through the steps to accomplish this:

  • Open up an existing report that you have previously created in Microsoft Word.
  • Highlight a section of the document where you would like to place the created Minitab graph or statistic.
  • Go to the Insert tab, click the Bookmark link, and type in the name of what you will be...

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

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...

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...

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...

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...

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...

Gauging Gage Part 2: Are 3 Operators or 2 Replicates Enough?

In Part 1 of Gauging Gage, I looked at how adequate a sampling of 10 parts is for a Gage R&R Study and providing some advice based on the results.

Now I want to turn my attention to the other two factors in the standard Gage experiment: 3 operators and 2 replicates.  Specifically, what if instead of increasing the number of parts in the experiment (my previous post demonstrated you would need an unfeasible increase in parts), you increased the number of operators or number of replicates?

In this study, we are only interested in the effect on our estimate of overall Gage variation. Obviously,...

Gauging Gage Part 1: Is 10 Parts Enough?

"You take 10 parts and have 3 operators measure each 2 times."

This standard approach to a Gage R&R experiment is so common, so accepted, so ubiquitous that few people ever question whether it is effective.  Obviously one could look at whether 3 is an adequate number of operators or 2 an adequate number of replicates, but in this first of a series of posts about "Gauging Gage," I want to look at 10.  Just 10 parts.  How accurately can you assess your measurement system with 10 parts?

Assessing a Measurement System with 10 Parts

I'm going to use a simple scenario as an example.  I'm going to...

Histograms are Even Easier to Compare in Minitab 17

Minitab 17 came out yesterday and it’s got quite a few neat features in it. You can check some of them out on the What’s New in Minitab 17 page. But one of my very favorite things is related to one of my previous blog posts that showed how to make histograms that are easy to compare. Turns out, you don’t need those steps anymore. You can do it all with Minitab’s Assistant.

Here’s how to open the data that I’m using if you want to follow along.

  • Choose File > Open Worksheet.
  • Click Look in Minitab Sample Data Folder.
  • Select Cap.MTW and click Open.

You can still rearrange a paneled histogram to make...

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...

Is There a World Cup "Group of Death"?

Much is made following the World Cup draw every four years over which group is the “group of death.”  This is generally considered to be a really difficult group that is tough to advance from, although there is no true definition (more on that below).

First, for readers not familiar with World Cup groups, a brief explanation of how teams are “grouped” in the World Cup is in order.  Thirty-two teams qualify to compete, and they are placed into eight different groups labeled A-H, with each having a predetermined “top” team.  In the group stage of the World Cup, each team plays the other three...

Normality Tests and Rounding

All measurements are rounded to some degree. In most cases, you would not want to reject normality just because the data are rounded. In fact, the normal distribution would be a quite desirable model for the data if the underlying distribution is normal since it would smooth out the discreteness in the rounded measurements.

Some normality tests reject a very high percentage of time due to rounding when the underlying distribution is normal (Anderson-Darling and Kolmogorov-Smirnov), while others seem to ignore the rounding (Ryan-Joiner and chi square).

As an extreme example of how data that is...

Understanding ANOVA by Looking at Your Household Budget

by Arun Kumar, guest blogger

One of the most commonly used statistical methods is ANOVA, short for “Analysis of Variance.” Whether you’re analysing data for Six-Sigma styled quality improvement projects, or perhaps just taking your first statistics course, a good understanding of how this technique works is important.

A lot of concepts are involved in any analysis using ANOVA and its subsequent interpretation. You’re going to have to grapple with terms such as Sources of Variation, Sum of Squares, Mean Squares, Degrees of Freedom, and F-ratio—and you’ll need to understand what statistical...

Explaining the Central Limit Theorem with Bunnies & Dragons

When I think about the Central Limit Theorem (CLT), bunnies and dragons are just about the last things that come to mind. However, that’s not the case for Shuyi Chiou, whose playful CreatureCast.org animation explains the CLT using both fluffy and fire-breathing creatures.

Per the article that accompanied this video in The New York Times:

“Many real-world observations can be approximated by, and tested against, the same expected pattern: the normal distribution. In this familiar symmetric bell-shaped pattern, most observations are close to average, and there are fewer observations further from...

The Three Coolest Things You Didn't Know about Histograms in Minitab

Not too long ago, I observed that one number is rarely adequate to describe data. Means and medians can disagree, and it’s important to know whether different groups of data have similar spreads. A great tool for displaying a more complete representation of the data is the histogram. Histograms are an easy way to summarize a lot of statistics. If you’re not convinced, take a minute to explore some. For example, Katherine Roswell can give you an example of how to use a histogram to identify the best opportunity for improvement in hospital patient readmissions. Histograms are great.

And the...

Factorial Analysis to better understand data on social progress

In this post I'll show how we can use a multivariate statistical analysis (in this case, a factorial analysis) to better understand data on social progress and economic development. This is a very simple and practical example of a factorial analysis performed using Minitab Statistical Software.

Factorial analysis is often considered to be a complex and advanced statistical technique, but I hope that this example will show that it can also be intuitive, easy to interpret, and accessible -- although it is obviously computationally intensive, this difficult and tedious part of the task will be...

Avoiding a Lean Six Sigma Project Failure, part 2

In a previous post, I discussed how to avoid a Lean Six Sigma project failure, specifically if the reason behind the failure is that the project solution never gets implemented.

In this post, let's discuss a few other project roadblocks that our customers cited when we asked them about the challenges they come across in completing projects. I’ll also go into detail about suggestions our industry-seasoned trainers at Minitab offer to avoid these failures.

Is the project scope too large?

One common reason quality improvement projects get started on the wrong foot is that their scope is too large.