# Tips and Techniques for Statistics and Quality Improvement

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

Histograms are great for summarizing several statistics. But whether you have enabled the Data Analysis Toolpak or you’re forging a path of formulas (COUNTIFs, AVERAGEs and STDEVs oh my!), creating a histogram in Excel isn’t always that great. We have developed Minitab Statistical Software to be your go-to histogram maker.

Let’s look at three ways you can do more with histograms in Minitab.

How the taste of wine is described often reads like a poem: “full-bodied and rich but not heavy, high in alcohol, yet neither acidic nor tannic, with substantial black cherry flavor despite its delicacy...” Flowers and fruits are commonly used as descriptors, meant to help drinkers understand the flavors in a glass of wine. This poetry reflects that some consider the conversion of fruit to wine be an art form.

Yet...

Most people who have taken a statistics class, whether it be Six Sigma or a college course or elsewhere, learned about the assumptions from which each test was developed. And boy are there a lot of assumptions!

Chances are a rather large and complicated flowchart was presented to help students navigate to the right hypothesis test – in my original course the chart was so big it had to be printed on 11x17...

In this day and age, it’s not uncommon that data entry errors occur in data sets that are so large that looking for and correcting the errors by hand is impractical. Fortunately, Minitab includes tools that make it easy to get your data into shape, so that you can proceed to getting the answers you need.

On the Minitab Blog, we’ve often discussed getting data into Minitab from Excel. Here's a small sampling, in case you currently have data in Excel:

Previously in our designed experiment on driving the golf ball as far as possible from the tee, we tested our four experimental factors and determined how many runs we needed to produce a complete data set.

Now let’s analyze the data and interpret the covariates and blocking variables.

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.