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ARIMA: How to Avoid the Herd When Analyzing Time Series Data

Getting the right information out of time series data requires skill and experience, and perhaps inspiration and intuition, too. This article discusses how to analyze time series data using some more sophisticated tools which are often not covered in basic statistical training programs.

Playing Games with a Purpose: Teaching Two-Sample Hypothesis Tests

This article highlights the teaching of hypothesis tests with an online computer game, and how statistical software such as Minitab can help analyze the data students create.

How to Design, Analyze and Interpret the Results of an Expanded Gage R&R Study

This article by Lou Johnson, technical training specialist at Minitab, LLC., explains why a standard Gage R&R cannot adequately assess the capability of many measurement systems. The article also demonstrates that when a standard study is not enough, an Expanded Gage R&R is an ideal tool to comprehensively characterize your measurement system. Lou Johnson, Minitab News, October 2012

Olympic Judging: Fair or Biased?

Bothered by the idea of judges’ subjective opinions determining the best performances in Olympic events, a Minitab statistician analyzes the results of two events to evaluate whether or not the judges were consistent and fair. Joel Smith, Minitab News, July 2012

Wine-Tasting by Numbers: Using Binary Logistic Regression to Reveal the Preferences of Experts

When you need to understand situations that seem to defy data analysis, you may be able to use techniques such as binary logistic regression. This article details how wine-tasting data and binary logistic regression yielded insight into factors that were important to a panel of experienced wine-tasters. The analysis illustrates that even factors that seem hard to measure, such as taste preferences, can be assessed with statistics if you choose the right analysis.

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