The phrase, “Are we there yet?” has long been associated with boring summer road trips. However, a new study shatters that myth, as it shows that 69 percent of people say traveling to their destination is often as fun as the actual vacation destination. The Summer Drive Study by the NACS, a trade association that represents the convenience store industry, provides a glimpse into the typical American’s car during a summer road trip—including what they eat, why they argue, and how they spend time.

The survey reveals that most vacationers will travel by cars (85%), followed by airplanes (36%) and trains (8%). Long hours don’t deter road trippers; of those traveling by car, the highest percentage of Americans plan to travel 12 or more hours round trip (32%), followed by 4–6 hours (24%), 7–11 hours (23%) and 0–3 hours (21%).

I recently got hold of the set of data shown in figure 1. What can be done to analyze and make sense of these 65 data values is the theme of this article. Read on to see what is exceptional about these data, not only statistically speaking.

A good start?

While I was attending a training class several years ago, a recommended starting point in an analysis was to use the “Graphical Summary” in the statistical software, which is in the options for “Basic Statistics.” The default output for figure 1’s data set is shown in figure 2.

The Atlantic hurricane season is now upon us, and the National Oceanic and Atmospheric Administration (NOAA) has just released its 2018 seasonal hurricane outlook which calls for a slightly above average season. The potential range of activity indicates that we could expect 10 to 16 named storms, with five to nine becoming hurricanes and one to four becoming major hurricanes.

But remember, it only takes one bad storm to wreak havoc, or in the case of the 2017 hurricane season, three.

Wait a minute. Why are you reading about hurricanes and NOAA in a NIST blog post? Let’s back up for a moment and start at the beginning.

Quality is related to processes. A process is “a series of actions or steps taken in order to achieve a particular end.” It doesn’t matter whether the process is the handling of invoices, customers in a bank, the manufacture or assembly of parts, insurance claims, the sick passing through a hospital, or any one of thousands of other examples. A process involves movement and action in a sequential fashion.

Every quality professional is concerned about the improvement of processes. By making processes better, we get less waste, lower costs, and happier customers.

Photo used with permission of Samuel Ferrara.

The image above depicts two opposed states: a dynamic, changing state and a static state. The lake is static, unchanging. We might take temperature measurements in the lake at different depths and come back tomorrow to find no difference. We might take samples of the lake water to compare with other lakes at a later date when we travelled to them.

Confidence intervals show the range of values we can be fairly, well, confident, that our true value lies in, and they are very important to any quality practitioner. I could be 95-percent confident the volume of a can of soup will be 390–410 ml. I could be 99-percent confident that less than 2 percent of the products in my batch are defective.

Demonstrating an improvement to the process often involves proving a significant improvement in the mean, so that’s what we tend to focus on—the center of a distribution.

Defects don’t fall in the center, though. They fall in the tails. You can find more beneficial insights in many situations through examining the tails of the distribution rather than just focusing on the mean.

Let’s take a look at some nontraditional confidence intervals that are particularly useful in estimating the percent that fall inside or outside the specification limits.