Tips and Techniques for Statistics and Quality Improvement

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

José Padilla

After graduating from Penn State with B.S. degrees in Mathematics and Statistics, José joined Minitab in 2007 and began sharing his expertise in quality statistics and Minitab Statistical Software as a sales representative. As a Minitab trainer, José draws on his experience presenting statistical concepts to professionals in numerous public and on-site training courses, webinars and workshops. José ensures customer satisfaction with his ability to communicate technical and business concepts both in Spanish and English. “My goal is to help customers achieve success,” he says. “Applying statistical concepts to their businesses can lead to good decision making, but only if they understand the tools they are working with.”
José Padilla

Control charts are specialized time series plots that help you determine if a process is in statistical control. Although some of the most widely used ones like Xbar-R and Individuals charts are great at detecting relatively large shifts in the process (1.5+ sigma shifts), you will need something different for smaller shifts. Enter the Exponentially Weighted Moving Average (EWMA) chart.

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Data analysts don’t count sheep at night. We look for a nice, bell-shaped curve in their arc as they leap over the fence. That’s normal distribution, and it’s a starting point to understanding one of the most important concepts in statistical analysis: the central limit theorem.

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Length of stay, defined as the time between hospital admission and discharge measured in days, is an aspect of care that can be costly for most healthcare systems if not approached properly. Optimizing patient flow, on the other hand, facilitates beneficial treatment, minimal waiting, minimal exposure to risks associated with hospitalization, and efficient use of resources such as hospital beds, medical equipment and...

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In hypothesis testing, we use data from a sample to draw conclusions about a population. First, we make an assumption called the null hypothesis (denoted by H0). As soon as you make a null hypothesis, you also define an alternative hypothesis (Ha), which is the opposite of the null. Sample data will be used to determine whether H0 can be rejected. If it is rejected, the statistical conclusion is that the alternative...

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