Process Capability for Attributes Data
overview      

You can produce process capability studies for either binomial or Poisson data.

Binomial data

Binomial data is usually associated with recording the number of defective items out of the total number of items sampled. For example, if you are a manufacturer, you might have a go/no-go gauge that determines whether an item is defective or not. You could then record the number of items that were failed by the gauge and the total number of items inspected. If you are an assembler, you could record the number of parts sent back due to poor fit in the assembly process and the total number of parts purchased. Or, you could record the number of people who call in sick on a particular day, and the number of people scheduled to work that day. These examples could be modeled by a binomial distribution if the following conditions are met:

·    Each item is the result of identical conditions.

·    Each item can result in one of two possible outcomes ("success/failure", "go/no-go," etc.).

·    The probability of a success (or failure) is constant for each item.

·    The outcomes of the items are independent of each other.

Poisson data

Poisson data is usually associated with the number of defects observed in an item, where the item occupied a specified amount of time or a specified space. Because the sizes of the items may vary, you may want to keep track of each item's size. Here are some examples:

·    If you manufacture electrical wiring, you may want to record the number of breaks in a piece of wire. The lengths of the pieces may vary, so you may also want to record the length for each piece of wire.

·    Or, if you manufacture appliances, you may want to record the number of scratches on one of the surfaces of the appliance. The size of the surfaces may be different for different appliances, so you may want to record the size of each surface sampled.

·    Or, you may record the number of errors in billing statements. The billing statements may be considered to be the "same size," because they have the same number of opportunities for errors, so you do not have to record the "size" of the invoices sampled.

A Poisson distribution could model these examples if the following conditions are met:

·    The rate of defects per unit of space or time is the same for each item.

·    The number of defects observed in the items are independent of each other.