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...

3 Things Baseball Can Teach Us About Control Charts

Control charts are some of the most useful tools in statistical science. They track process statistics over time and detect when the mean or standard deviation change from what they have been. The signals that control charts send about special causes can help you zero in on the fastest ways to improve any process, whether you’re making tires, turbines, or trying to improve patient care.

I’ve mentioned before that I’m a baseball fan. For the past several years, I’ve been noticing articles about the Year of the Pitcher in Major League Baseball (2010, 2011, 2012, 2013, 2014). That repetition...

Two-Way ANOVA in Minitab 17

After upgrading to the latest and greatest version of our statistical software, Minitab 17, some users have contacted tech support to ask "Wait a minute, where is that Two-Way ANOVA option in Minitab 17?" 

The answer is that it’s not there. That’s right! The 2-Way ANOVA option that was available in Minitab 16 and prior versions was removed from Minitab 17. Why would this feature be removed from the new version?  Shouldn’t the new version have more features instead of less? 

Two-Way ANOVA was removed from Minitab 17 because you can get the same output by using the General Linear Model option in...

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...

Blind Wine Part II: The Survey

In Blind Wine Part I, we introduced our experimental setup, which included some survey questions asked ahead of time of each participant. The four questions asked were:

  • On a scale of 1 to 10, how would you rate your knowledge of wine?
  • How much would you typically spend on a bottle of wine in a store?
  • How many different types of wine (merlot, riesling, cabernet, etc.) would you buy regularly (not as gifts)?
  • Out of the following 8 wines, which do you think you could correctly identify by taste?
  • Merlot
  • Cabernet Sauvignon
  • Pinot Noir
  • Malbec
  • Chardonnay
  • Pinot Grigio
  • Sauvignon Blanc
  • Riesling

Today, we'd like to...

Do the Data Really Say Female-Named Hurricanes Are More Deadly?

A recent study has indicated that female-named hurricanes kill more people than male hurricanes. Of course, the title of that article (and other articles like it) is a bit misleading. The study found a significant interaction between the damage caused by the storm and the perceived masculinity or femininity of the hurricane names. So don’t be confused by stories that suggest all female-named hurricanes are deadlier than male-named hurricanes. The study actually found no effect of masculinity/femininity for less severe storms. It was the more severe storms where the gender of the name had a...

The Five Coolest Things You Can Do When You Right-click a Graph in Minitab Statistical Software

Minitab graphs are powerful tools for investigating your process further and removing any doubt about the steps you should take to improve it. With that in mind, you’ll want to know every feature about Minitab graphs that can help you share and communicate your results effectively. While many ways to modify your graph are on the Editor menu, some of the best features become available when you right-click your graph.

Here are the five coolest things you can do when you right-click a graph in Minitab Statistical Software.

Send graph to...

Once your graph is ready for your report or presentation,...

Multiple Regression Analysis and Response Optimization Examples using the Assistant in Minitab 17

In Minitab, the Assistant menu is your interactive guide to choosing the right tool, analyzing data correctly, and interpreting the results. If you’re feeling a bit rusty with choosing and using a particular analysis, the Assistant is your friend!

Previously, I’ve written about the new linear model features in Minitab 17. In this post, I’ll work through a multiple regression analysis example and optimize the response variable to highlight the new features in the Assistant.

Choose a Regression Analysis

As part of a solar energy test, researchers measured the total heat flux. They found that heat...

Can I Just Delete Some Values to Reduce the Standard Variation in My ANOVA?

We received the following question via social media recently:

I am using Minitab 17 for ANOVA. I calculated the mean and standard deviation for these 15 values, but the standard deviation is very high. If I delete some values, I can reduce the standard deviation. Is there an option in Minitab that will automatically indicate values that are out of range and delete them so that the standard deviation is low?

In other words, this person wanted a way to automatically eliminate certain values to lower the standard deviation.

Fortunately, Minitab 17 does not have the functionality that this person was...

Using Probability Plots to Understand Laser Games Scores

There is more than just the p value in a probability plot—the overall graphical pattern also provides a great deal of useful information. Probability plots are a powerful tool to better understand your data.

In this post, I intend to present the main principles of probability plots and focus on their visual interpretation using some real data.

In probability plots, the data density distribution is transformed into a linear plot. To do this, the cumulative density function (the so-called CDF, cumulating all probabilities below a given threshold) is used (see the graph below). For a normal...

Common Statistical Mistakes You Should Avoid

It's all too easy to make mistakes involving statistics. Powerful statistical software can remove a lot of the difficulty surrounding statistical calculation, reducing the risk of mathematical errors—but  correctly interpreting the results of an analysis can be even more challenging. 

No one knows that better than Minitab's technical trainers. All of our trainers are seasoned statisticians with years of quality improvement experience. They spend most of the year traveling around the country (and around the world) to help people learn to make the best use of Minitab software for analyzing data...

Five Guidelines for Using P values

There is high pressure to find low P values. Obtaining a low P value for a hypothesis test is make or break because it can lead to funding, articles, and prestige. Statistical significance is everything!

My two previous posts looked at several issues related to P values:

In this post, I’ll look at whether P values are still helpful and provide guidelines on how to use them with these issues in mind.

Sir Ronald A Fisher

Are P Values Still Valuable?


Hypothesis Testing and P Values

by Matthew Barsalou, guest blogger

Programs such as the Minitab Statistical Software make hypothesis testing easier; but no program can think for the experimenter. Anybody performing a statistical hypothesis test must understand what p values mean in regards to their statistical results as well as potential limitations of statistical hypothesis testing.

A p value of 0.05 is frequently used during statistical hypothesis testing. This p value indicates that if there is no effect (or if the null hypothesis is true), you’d obtain the observed difference or more in 5% of studies due to random...

Not All P Values are Created Equal

The interpretation of P values would seem to be fairly standard between different studies. Even if two hypothesis tests study different subject matter, we tend to assume that you can interpret a P value of 0.03 the same way for both tests. A P value is a P value, right?

Not so fast! While Minitab statistical software can correctly calculate all P values, it can’t factor in the larger context of the study. You and your common sense need to do that!

In this post, I’ll demonstrate that P values tell us very different things depending on the larger context.

Recap: P Values Are Not the Probability of...

How to Correctly Interpret P Values

The P value is used all over statistics, from t-tests to regression analysis. Everyone knows that you use P values to determine statistical significance in a hypothesis test. In fact, P values often determine what studies get published and what projects get funding.

Despite being so important, the P value is a slippery concept that people often interpret incorrectly. How do you interpret P values?

In this post, I'll help you to understand P values in a more intuitive way and to avoid a very common misinterpretation that can cost you money and credibility.

What Is the Null Hypothesis in Hypothesis...

Did Welch’s ANOVA Make Fisher's Classic One-Way ANOVA Obsolete?

One-way ANOVA can detect differences between the means of three or more groups. It’s such a classic statistical analysis that it’s hard to imagine it changing much.

However, a revolution has been under way for a while now. Fisher's classic one-way ANOVA, which is taught in Stats 101 courses everywhere, may well be obsolete thanks to Welch’s ANOVA.

In this post, I not only want to introduce you to Welch’s ANOVA, but also highlight some interesting research that we perform here at Minitab that guides the implementation of features in our statistical software.

One-Way ANOVA Assumptions

Like any...

Equivalence Testing for Quality Analysis (Part II): What Difference Does the Difference Make?

My previous post examined how an equivalence test can shift the burden of proof when you perform hypothesis test of the means. This allows you to more rigorously test whether the process mean is equivalent to a target or to another mean.

Here’s another key difference: To perform the analysis, an equivalence test requires that you first define, upfront, the size of a practically important difference between the mean and the target, or between two means.

Truth be told, even when performing a standard hypothesis test, you should know the value of this difference. Because you can’t really evaluate...

Equivalence Testing for Quality Analysis (Part I): What are You Trying to Prove?

With more options, come more decisions.

With equivalence testing added to Minitab 17, you now have more statistical tools to test a sample mean against target value or another sample mean.

Equivalence testing is extensively used in the biomedical field. Pharmaceutical manufacturers often need to test whether the biological activity of a generic drug is equivalent to that of a brand name drug that has already been through the regulatory approval process.

But in the field of quality improvement, why might you want to use an equivalence test instead of a standard t-test?

Interpreting Hypothesis...