- Best at discerning whether or not we have a defective product or service.
- “Pass/fail” is better for failure analysis: (failure analysis is opposite to the philosophy of Six Sigma. Preventing defects, not trying to figure what went wrong later.)
- Counted data is discrete.
- # of dimples on a golf ball.
- # of people in a stadium.
- 80 / 100 to discrete – it is out of a finite set.
Discrete Data Notes:
Operational definition – When would you start your watch for a room service delivery; when would you stop the stop watch.
If you do this for a project – each team member is gonna have a different set of numbers; because we have not accurately defined the operational definition
If data is different, the data will get attacked; resistance
- An example of qualitative data is color. It cannot be expressed as a number
- Anything that can be classified as either/or
- Very binary
- Pass / fail, go / no-go, good / bad.
- Ex. paint chips per unit, percent of defective units in a lot, audit points.
- Also see: Attribute Charts
Continuous Data / Variable Data
- Anything that can be measured on a continuous basis.
- Can always be divided into smaller increments.
- Exists on a continuum
- Preferred over Discrete.
- Use continuous data where possible because it tells us the magnitude of the issue.
- Helpful for controlling the process & providing enough discrimination.
- Length (inches, half inch, hundredths of an inch …)
- Anything you can measure: torque, tension, length, volume.
Teaching Discrete & Continuous Data
Imagine you have a young child who says that he is sick. As aparent, the first thing you do is to touch their forehead to see if they feel warm – that is collecting discrete data.
If it feels like he has a fever, you’re likely to use a thermometer to take his temperature – Another type of data collection. You need to know magnitude of the fever because that will determine the course of action; 105 – ER, 101 – TYLENOL. That temperature reading is continuous data – data that exist on a continuum.
- You could record on a measles diagram.
- Example: Determining root cause of paint blemishes occurring on a car production line.
Difficult to translate after-the-fact attribute (go / no go) data to variable. But in most cases, you can find a way during measuring to convert attribute to variable. Ex. how far out of tolerance.
Always easy to convert variable data to attribute if you have a standard. Ex. Water is too cold to swim at less than 75 degrees. No go is < 75. Then put all of the data set that is less than 75 to “no go” and all above “go.”
Six Sigma Black Belt Certification Types of Data Questions:
Question: One characteristic of attributes data is that it is always: (Taken from ASQ sample Black Belt exam.)
(C) expensive to collect
(D) read from a scale of measurement
Answer: Discrete. Attribute data is discrete as it takes the form of a binary condition – go/ don’t go or pass/fail. Continuous data cannot be expressed as an either / or statement so (a) is not the correct answer. Attribute data does not have to be expensive to collect. And since it is an either/or value, attribute data is not read from a scale of measurement.