Tips and Techniques for Statistics and Quality Improvement

Blog posts and articles about using Minitab software in quality improvement projects, research, and more.

Statistics can be challenging, especially if you're not analyzing data and interpreting the results every day. Statistical software makes things easier by handling the arduous mathematical work involved in statistics. But ultimately, we're responsible for correctly interpreting and communicating what the results of our analyses show.

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To make objective decisions about the processes that are critical to your organization, you often need to examine categorical data. You may know how to use a t-test or ANOVA when you’re comparing measurement data (like weight, length, revenue, and so on), but do you know how to compare attribute or counts data? It easy to do with statistical software like Minitab. 

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In Parts 1 and 2 of this blog series, I wrote about how statistical inference uses data from a sample of individuals to reach conclusions about the whole population. That’s a very powerful tool, but you must check your assumptions when you make statistical inferences. Violating any of these assumptions can result in false positives or false negatives, thus invalidating your results. 

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In Part 1 of this blog series, I wrote about how statistical inference uses data from a sample of individuals to reach conclusions about the whole population. That’s a very powerful tool, but you must check your assumptions when you make statistical inferences. Violating any of these assumptions can result in false positives or false negatives, thus invalidating your results. 

The common data assumptions are:  random...

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If you’re not a statistician, looking through statistical output can sometimes make you feel a bit like Alice in Wonderland. Suddenly, you step into a fantastical world where strange and mysterious phantasms appear out of nowhere.  

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Data mining can be helpful in the exploratory phase of an analysis. If you're in the early stages and you're just figuring out which predictors are potentially correlated with your response variable, data mining can help you identify candidates. However, there are problems associated with using data mining to select variables.

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I watched an old motorcycle flick from the 1960s the other night, and I was struck by the bikers' slang. They had a language all their own. Just like statisticians, whose manner of speaking often confounds those who aren't hep to the lingo of data analysis.

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There may be huge potential benefits waiting in the data in your servers. These data may be used for many different purposes. Better data allows better decisions, of course. Banks, insurance firms, and telecom companies already own a large amount of data about their customers. These resources are useful for building a more personal relationship with each customer.

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In 2011 we had solar panels fitted on our property. In the last few months we have noticed a few problems with the inverter (the equipment that converts the electricity generated by the panels from DC to AC, and manages the transfer of unused electric to the power company). It was shutting down at various times throughout the day, typically when it was very sunny, resulting in no electricity being generated.

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So the data you nurtured, that you worked so hard to format and make useful, failed the normality test.

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