What Are Control Charts for Attributes?

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  • 0:00 Attribute Data
  • 1:10 Control Charts
  • 4:25 Lesson Summary
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Lesson Transcript
Instructor: Natalie Boyd

Natalie is a teacher and holds an MA in English Education and is in progress on her PhD in psychology.

Sometimes, businesses want to track how well something works over a period of time. When this need arises, many turn to control charts for attribute data. Watch this lesson to find out what types of control charts are good for attribute data.

Attribute Data

Johnny is the manager of a furniture factory. His factory makes dining room tables and chairs, and their work is generally very good, but sometimes a piece comes off the assembly line with a problem: one of the legs of the table or chair is shorter than the others. Johnny is worried about it and wants to monitor the problem. That is, he wants to figure out how often it happens that a piece of furniture from his factory is faulty.

To help him figure out how often his factory produces faulty furniture, Johnny can collect attribute data. Attribute data is non-continuous data. That is, attribute data can be clumped into buckets instead of being mapped on a continuum. Think about Johnny: he wants to know how often his factory produces short legs. That data will involve looking at each piece of furniture and marking it as either faulty or fine. The furniture won't be partially faulty; it will be faulty or not.

Many businesses like Johnny's look at attribute data by making control charts. To help Johnny out, let's look at what control charts for attributes are and the different types of control charts for attribute data.

Control Charts

Johnny wants to know how often his factory is producing tables and chairs with a short leg, and he knows he needs to use attribute data, but he's not sure what to do with the data. How can he see how his factory is doing?

One way to use attribute data is by making a control chart, or a graph that shows how something changes across time. For example, Johnny can map how many faulty pieces of furniture his factory ends up making every day for a month. Then he can create a control chart to show how many pieces per day were faulty.

Control charts are very good at helping businesses see what's going on. For example, if Johnny charts the attribute data for each day for a month, he might see that every day the factory is making more faulty pieces of furniture. That could be a problem!

So, how should Johnny approach making his control chart for attribute data? There are four major types of control charts for attribute data. To help Johnny figure out which one to make, let's look at all four.

An Np chart looks at how often something occurs with a fixed sample size when the choices are yes/no. For example, if Johnny is collecting information on faulty furniture pieces, he might ask of each piece of furniture 'Is this piece faulty?' That's a yes/no question, and he's just counting the number of pieces that have any type of fault.

In an Np chart, the sample size is fixed. That is, Johnny will need to measure the same number of pieces of furniture every day. If his factory puts out the exact same number of tables and chairs every day, then it's ok to use an Np chart.

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