Articles

Deepen your understanding of quality improvement and how to analyze your data with Minitab. Topics include Six Sigma Project Management, General, Basic Statistics, Regression & ANOVA, DOE, Control Charts, and Quality Tools.

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General

Doing Monte Carlo simulations in Minitab Statistical Software
Doing Monte Carlo simulations in Minitab Statistical Software is very easy. This article illustrates how to use Minitab for Monte Carlo simulations using both a known engineering formula and a DOE equation. Paul Sheehy and Eston Martz, April 2012
  • Minitab Statistical Software
  • General
Weather Forecasts: Just How Reliable Are They?
Given all of the factors that influence it, the weather is an undeniably complex process—and like any process, it can exhibit a lot of variation. However, if you’re going to make any big plans based on weather and you want to minimize the variation, the data we collected suggest it’s best to rely on the next-day forecast. Michelle Paret and Eston Martz, September 2011
Running a Monte Carlo Simulation
This article explains how you can use Minitab Statistical Software to run a Monte Carlo Simulation, a method often used in Design For Six Sigma (DFSS). Bruno Scibilia, Minitab News, November 2009. 
Tumbling Dice and Birthdays: Understanding the Central Limit Theorem
One of the most important statistical concepts to understand is the central limit theorem. This article explains the central limit theorem and how to demonstrate it using common examples, including the roll of a die and the birthdays of Major League Baseball players. Michelle Paret and Eston Martz, Minitab News, August 2009. 
Sweetening Statistics: What M&M's Can Teach Us
Not everyone enjoys learning about statistics. But adding M&M’s to the lesson is a fun way to make it more appealing. This article reveals how M&M’s can give students hands-on experience with statistics and Minitab Statistical Software. Michelle Paret and Eston Martz, Minitab News, August 2008.
Exhibit A: Minitab Balances the Scales of Justice
Read this entertaining account of how one customer used our statistical software to win his day in court and save himself $5,000 when the buyer he sold his house to sued him for damages after closing. Tony Coray, Minitab News, June 2002
How to Make Statistics Your Friend
Learn how people who find statistics intimidating can build the skills and confidence they need to analyze quality improvement data with Minitab's e-learning tool. Michelle Paret and Eston Martz, Quality Magazine, March 2009.
Tough Fantasy Football Decision? Minitab Can Help!
Are you ready for some football? We’re going to show you some ways you can use Minitab Statistical Software to make those tough decisions about who to start and who to sit each week. You’ll see how Minitab can give you the edge you need over your fantasy football competition this season! Kevin Rudy, September 2010.
  • Minitab Statistical Software
  • General

Basic Statistics

Normal Probability Plots and Tests for Normality
Normal probability plots are often used as an informal means of assessing the non-normality of a set of data. One problem confronting persons inexperienced with probability plots is that considerable practice is necessary before one can learn to judge them with any degree of confidence. Some objective measure of the straightness of a probability plot would be helpful, especially for students just beginning their statistical education.
Some Misconceptions about the Normal Distribution
This article discusses three misconceptions regarding the use of the normal distribution in theory and practice. Keith M. Bower, Six Sigma Forum - American Society for Quality, May 2003
Why We Don't "Accept" the Null Hypothesis
This article discusses a frequently encountered mistake when using hypothesis tests. Keith M. Bower and James A. Colton, Six Sigma Forum - American Society for Quality, July 2003
Some Misconceptions about Confidence Intervals
This article discusses three misconceptions regarding the use of confidence intervals. Keith M. Bower, Six Sigma Forum - American Society for Quality, July 2003
One Sample T-Test Using Minitab
Looks at the assumptions and interpretation of results using the one-sample t-procedure. A case study is used to show the relationship between the P-Value and Confidence Intervals. Keith M. Bower, KeepingTAB #33, October 2000
The Two-Sample T-Test and Randomization Test
In this article, the author investigates situations in which the two-sample t-test may be considered robust to certain assumptions, including normality. Illustrates the randomization test procedure using a hypothetical example. Keith M. Bower, Six Sigma Forum - American Society for Quality, June 2003
The Paired T-Test Using Minitab
Addresses the paired t-test procedure, to be used when some dependency exists between two populations. This is illustrated by an experiment involving measurements of tire wear using two distinct methods (hence each tire in the study meets both measurement methods.) Assumptions, results, and the conclusions from this actual study are illustrated using Minitab output. Keith M. Bower, Scientific Computing & Instrumentation, February 2001
Sample Size Determination for the Test of One Proportion
Learn how to use Minitab's Power and Sample Size functionality for the test of one proportion. Keith M. Bower, EXTRAOrdinary Sense (ISSSP Newsletter), February 2002
When To Use Fisher's Exact Test
This article explores the relationship between Karl Pearson's Chi-Square test and R.A. Fisher's Exact test. A situation when the Chi-Square test may not be appropriate is discussed. Keith M. Bower, ASQ Six Sigma Forum Magazine, August 2003, Vol. 2, No. 4
  • Minitab Statistical Software
  • Basic Statistics
Minitab's Power and Sample Size Tools
Minitab Statistical Software’s Power and Sample Size tools can tell you how much data you need to be sure about your results. Eston Martz and Michelle Paret, Minitab News, January 2011
  • Minitab Statistical Software
  • Basic Statistics
Analyzing Survey Data with Minitab: Frequency Distributions, Cross Tabulation and Hypothesis Testing
This article highlights several basic tools in Minitab Statistical Software that will help you interpret your survey data accurately. Eston Martz and Michelle Paret, Minitab News, February 2011
Delve Deeper into Survey Data with Minitab: 2-Sample t-Tests, Proportion Tests, ANOVA and Regression
This article highlights more sophisticated techniques, including 2-sample t-tests, proportion tests, ANOVA and regression, to dig deeper into survey data. Eston Martz and Michelle Paret, Minitab News, March 2011
 

Regression & ANOVA

Minitab's Nonlinear Regression Tool
Nonlinear regression can describe complicated relationships between a response variable and predictor variables. Chemists, engineers, and scientists often need nonlinear regression to model complex functions, and Minitab Statistical Software can help you harness this powerful statistical technique. Eston Martz and Michelle Paret, Minitab News, December 2010
  • Minitab Statistical Software
  • Regression & ANOVA
Advantages of Minitab's General Regression Tool
Regression isn’t new—but by making it easy to include continuous and categorical variables, specify interaction and polynomial terms, and transform response data with the Box-Cox transformation, Minitab 16’s General Regression tool makes the benefits of this powerful statistical technique easier for everyone. Eston Martz and Michelle Paret, Minitab News, September 2010
  • Minitab Statistical Software
  • Regression & ANOVA
Some Misconceptions about R2
This article investigates some frequently encountered misconceptions involving the important R2 statistic. The Minitab datasets (MTW files) illustrate concept and support examples from the article. James A. Colton and Keith M. Bower, EXTRAOrdinary Sense (ISSSP Newsletter), August 2002
On The Use of Indicator Variables in Regression Analysis
Addresses the use of indicator variables in simple and multiple linear regression analysis. Keith M. Bower, EXTRAOrdinary Sense (ISSSP Newsletter), November 2001
Modeling and Interpreting Interactions in Multiple Regression
A method of constructing interactions in multiple regression models is described which produces interaction variables that are uncorrelated with their component variables and with any lower-order interaction variables. The method is, in essence, a partial Gram-Schmidt orthogonalization that makes use of standard regression procedures, requiring neither special programming nor the use of special-purpose programs before proceeding with the analysis.
Analysis of Variance (ANOVA) Using Minitab
Provides guidelines for performing ANOVA and walks through a detailed example of an analysis using Minitab. Keith M. Bower, Scientific Computing & Instrumentation, February 2000

DOE

Avoiding Mean Square Error Bias in Designed Experiments
This article discusses the causes and consequences of bias in the mean square error (MSE) term and provides suggestions for detecting and correcting MSE bias. James A. Colton, Scientific Computing & Instrumentation, April 2004
A Modified Path of Steepest Ascent for Split-Plot Experiments
This article investigates the path of steepest ascent used in response surface designs within a split-plot structure. It presents three methods for calculating the coordinates along the path. Scott M. Kowalski, Connie M. Borror, and Douglas C. Montgomery, Journal of Quality Technology, January 2005, Vol. 37, No. 1
How To Analyze a Split-Plot Experiment
This article describes how to correctly set up and analyze a split-plot experiment using a real-life example. Also discussed is how the two different estimates of experimental error required in split-plot designs are calculated and used to determine which factors are significant. Kevin J. Potcner and Scott M. Kowalski, Quality Progress, December 2004
How To Recognize a Split-Plot Experiment
This article describes split-plot experiments, commonly used with hard-to-change factors, as compared to completely randomized experiments. Three real-life examples are presented that describe when and how split-plot experiments can be used. The article also shows how the analysis of split-plot designs differs from completely randomized designs. Scott M. Kowalski and Kevin J. Potcner, Quality Progress, November 2003
Handling Hard-to-Change Factors with Split-Plot Designs in Minitab
Split-plot designs are experimental designs that include at least one hard-to-change factor that is difficult to completely randomize due to time or cost constraints. By making the creation of split-plot experiment designs simple, Minitab 16 makes the benefits of this powerful statistical technique accessible to everyone. Eston Martz and Michelle Paret, October 2010.
  • Minitab Statistical Software
  • DOE

Control Charts

The T Chart in Minitab Statistical Software
This article provides background, use cases, and technical information about the implementation of the T chart in Minitab 16 Statistical Software. Dr. Terry Ziemer, SIXSIGMA Intelligence, December 2011
The G Chart in Minitab Statistical Software
This article provides background, use cases, and technical information about the implementation of the G chart developed by James Benneyan in Minitab 16 Statistical Software. Dr. Terry Ziemer, SIXSIGMA Intelligence, November 2011
On the Charts: A Conversation with David Laney
David Laney applied the teachings of Fisher, Deming, Wheeler, and others and ended up changing how we think about P charts and U charts. In the course of integrating these new features into Minitab, we spoke with Laney about his inspirations and about the P’ Charts and U’ Charts that bear his name. Greg Fox and Eston Martz, October 2011
  • Minitab Statistical Software
  • Control Charts
Being in Control
Read this case study to find out how improperly constructed subgroups impacted a manufacturer's control charts and thus the assessment of their process. Michelle Paret and Paul Sheehy, Quality Canada, June 2009
Using CUSUM Charts for Small Shifts
In this article, the author investigates the use of the Cumulative Sum (CUSUM) chart, which is a useful tool to detect small changes in a process. This paper makes use of a Minitab macro which may be downloaded from the Macro Catalog. Keith M. Bower, EXTRAOrdinary Sense, May 2001
Using Exponentially Weighted Moving Average (EWMA) Charts
Investigates the Exponentially Weighted Moving Average (EWMA) control chart. After discussing some theory behind the EWMA chart, he provides an example of using this statistical procedure to detect a small process shift. Keith M. Bower, Asia Pacific Process Engineer, October 2000

Quality Tools

Expanded Gage R&R Studies in Minitab
Gage R&R studies can tell you if your measurement system is producing data you can trust, but rigid data requirements and other limitations can make Gage R&R studies difficult to conduct, and may not account for all important factors. The Expanded Gage R&R tool in Minitab 16 Statistical Software makes these roadblocks a thing of the past. Patrick North, November 2010
  • Minitab Statistical Software
  • Quality Tools
Determining if Your Measurement System is Adequate
To improve a process, you need reliable data. Is your measurement system providing data you can depend on? Find out with Minitab’s Gage R&R Study and Gage Run Chart. Michelle Paret, Quality Magazine, March 2008
Identifying the Distribution of Data is Key to Analysis
Knowing the distribution of your data is essential to choosing the right statistical method. Learn how Minitab’s Individual Distribution Identification can help you quickly determine the distribution of your data so you can make this critical choice. Asia Pacific Engineer, December 2007
Modeling Non-Normal Data Using Statistical Software
Using a supplier of tantalum as an example, this article discusses how to demonstrate process stability and capability for quality characteristics that do not follow the normal distribution. Louis Johnson, R&D Magazine, August 2007, Vol. 49, No. 8
Breakthrough Improvement for Your Inspection Process
This article presents “The Six Step Method for Inspection Improvement” – a process to increase the accuracy of pass/fail decisions made when visually evaluating manufactured parts for defects. The process is illustrated by a case study from Hitchiner Manufacturing Company, Inc., a manufacturer of precision metal parts who successfully implemented this method. Louis Johnson, Six Sigma Forum - American Society for Quality, May 2007
Non-Traditional MSA with Continuous Data
This article addresses the relationship between standard (manufacturing) measurement system analyses, and those encountered in the Service Quality/Transactional arena. Keith M. Bower, Six Sigma Forum - American Society for Quality, December 2003
Measurement System Analysis and Destructive Testing
This article explores the use of a nested design in a measurement system analysis with destructive testing. Includes a discussion of model assumptions and examines results from a practical example. Douglas Gorman and Keith M. Bower, ASQ Six Sigma Forum Magazine, August 2002, Vol. 1, No. 4
Measurement System Analysis with Attribute Data
Addresses two statistics for use in a measurement system study involving attribute responses, using Minitab Release 13.31. The important difference between Kappa and Kendall's Coefficient of Concordance is highlighted. Keith M. Bower, KeepingTAB #35 (Minitab Newsletter), February 2002
Evaluating the Usefulness of Data Using Gage R&R
Addresses Gage Repeatability and Reproducibility (Gage R&R) using the functionality in Minitab Release 13.3. After discussing some theory behind the ANOVA output, the associated results from an example are analyzed and conclusions drawn from a practical perspective, referencing several industry-wide guidelines. Keith M. Bower and Michelle E. Touchton, Asia Pacific Process Engineer, April 2001
Capability Analysis Using Minitab: Part 1
Two-part article: This part addresses the assumptions and interpretation of capability analyses, including Cp, Cpk as well as "Sigma" values. In this paper, the assumption of Normality is valid for the example under discussion. Keith M. Bower, EXTRAOrdinary Sense, January 2001
Capability Analysis Using Minitab: Part 2
Two-part article: This part addresses capability analysis when one is dealing with a process which may not be adequately modeled by a Normal distribution. He discusses data transformations using the Box-Cox technique, as well as the fitting of a Weibull distribution. The paper also includes a methodology for choosing between the two techniques in practice, based on probability plots and the Anderson-Darling statistic. Keith M. Bower, EXTRAOrdinary Sense, March 2001
Reduce Inspection Costs with Acceptance Sampling
Learn how Minitab's sampling plans can make your quality inspections more efficient by helping you determine the right number of units to inspect. Janet Gess and Michelle Paret, Asia-Pacific Engineer, December 2008

Six Sigma Project Management

Statistics and Belts: How Much? How Deep?
This article by Paul Sheehy, technical training specialist, Minitab. Inc., discusses the differences in how Six Sigma Green and Black Belts are taught statistics in different organizations, and identifies seven ways statistical software can help support quality professionals. Paul Sheehy, November 2011
The Three Keys to Six Sigma Success
A survey of more than 200 Six Sigma practitioners revealed three key principles for project success. Learn what they were and how Quality Companion by Minitab can help you address them. Lou Johnson and Cate Twohill, Industryweek.com, April 28, 2008.

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