Tips and Techniques for Statistics and Quality Improvement

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

 

Do you have an insurance policy that will pay out if your car gets damaged? Do you pay the premium because you know your car will be damaged? No, you pay it so that if you do damage your car you will get a payment to cover the damage.

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How do you commit to realistic forecasts and timelines when resources are limited or gathering real data is too expensive or impractical? Can simulated data be trusted for accurate predictions? That’s when Monte Carlo Simulation comes in.  

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As we start off 2018, our eyes are on the winter weather, specifically low temperatures and snowfall. After 2015-2016's warmest winter on record and Chicago breaking records in 2017 with no snow sticking to the ground in January or February, our luck might have run out. We shall see, though. The Old Farmer's Almanac is reporting that 2017-2018 winter temperatures will be colder than last winter.

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Wildfires in California have killed at least 40 people and burned more than 217,000 acres in the past few weeks. Nearly 8,000 firefighters are trying to contain the blazes with the aid of more than 800 firetrucks, 70 helicopters and 30 planes.

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Overfitting a model is a real problem you need to beware of when performing regression analysis. An overfit model result in misleading regression coefficients, p-values, and R-squared statistics. Nobody wants that, so let's examine what overfit models are, and how to avoid falling into the overfitting trap.

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In statistics, as in life, absolute certainty is rare. That's why statisticians often can't provide a result that is as specific as we might like; instead, they provide the results of an analysis as a range, within which the data suggest the true answer lies.

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by Matthew Barsalou, guest blogger

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by Matthew Barsalou, guest blogger

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Can you trust your data? 

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We had solar panels fitted on our property in 2011. Last year, we had a few problems with the equipment. 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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