ABSTRACT
Discrete versus continuous data is a key concept in statistical quality control with regard to quality control charts. The “type” of data that one is collecting has a major impact on how the data are analyzed and the Six Sigma tools to be used. A simple method to distinguish between the two is that discrete data are always whole numbers, such as the number of red candies in a dish, the number of customer complaint calls received at a call center, or the number of defective headlights coming off an automotive assembly line. On the other hand, continuous data are not whole numbers, typically involve decimals, and can be measured to different levels of accuracy, such as someone’s drive to work every day; the drive distance could be reported as 10 miles, 10.125 miles, or a very accurate 10.2554512 miles. Other examples of continuous data include part dimensions (such as the exterior dimension of a piston), the wait times (such as the time to get your food at a fast-food restaurant drive-through), or the temperature outside. Once the type of data (continuous versus discrete) has been determined, the appropriate methods to collect and statistically analyze the data can be established as discussed in the following sections of this chapter.
