Attribute charts directly measure the rejects in the production operation，as opposed to measuring a particular value of the quality characteristic as in variable processes. They are more common in manufacturing because of the following:
1. Attribute or pass—fail test data are easier to measure than actual variable measurement. They can be obtained by devices or tools such as go/no-go gauges, calibrated for only the specification measurements, as opposed to measuring the full operating spectrum of parts.
2. Attribute data require much less operator training, since they only have to observe a reject indicator or light, as opposed to making several measurements on gauges or test equipment.
3. Attribute data can be directly collected from the manufacturing equipment, especially if there is a high degree of automation.
4. Storage and dissemination of attribute data is also much easier, since there is only the reject rate to store versus the actual measurements for variable data.
Attribute charts use different probability distributions than the normal distribution used in variable charts, depending on whether the sample size is constant or changing, as shown in Figure 3*1. For C and U charts, the Poisson distribution is used, whereas the P and nP charts use the binomial distribution.