Discrete vs. Attribute Data

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• #56008

griffaus
Participant

On the Six Sigma page, “Attribute Data,” it states:
“No analysis can be performed on attribute data. Attribute data must be converted to a form of Variable data called discrete data in order to be counted or useful.”

On the Six Sigma page, “Discrete Data,” it states:
“Attribute data (aka Discrete data) is data that can’t be broken down into a smaller unit and add additional meaning. It is typically things counted in whole numbers.”

How can both of these statements be true? I’m attempting to understand the difference between discrete and attribute data, or if there even is a difference. From the various “Intro to Stats” videos that I have viewed, Discrete Data is always quantitative and Attribute Data is always qualitative. Therefore, it appears to me that they should be mutually exclusive and both would require the “conversion” described above to correspond with one another.

Please clarify how they are similar,different, etc!

Thanks.

Attribute Data – https://www.isixsigma.com/dictionary/attribute-data/
Discrete Data – https://www.isixsigma.com/dictionary/discrete-data/

Intro to Stats videos:

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#202582

Robert Butler
Participant

I think the problem is the wording of the part of the second statement which says “…Attribute data (aka Discrete data)…” It is true that in order to run a statistical analysis on attribute data you will typically have to assign numeric values to the attribute categories which, because of the nature of the assignment, are discrete numeric identifiers. However, just because data is discrete does not mean it is attribute data.

I also don’t like the part of the second statement that says “It is typically things counted in whole numbers.” rather I think it should should read “typically whole numbers are assigned to attribute values for purposes of statistical analysis. The assignment of numbers may be purely random or, if there is some logical ranking/ordering of the attribute, assigned as a function of rank/order.”

Examples:
Attribute – nominal – no obvious ranking – gender, pill type, geographical location, work centers, make of automobile, ice cream flavors, etc.
Attribute – ordinal – ranking – age group, education level, ratings of any kind (poor, fair, good), (strongly disagree, disagree, neither agree or disagree, agree, strongly agree), etc.
Discrete – no attributes – count

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#202584

Chris Seider
Participant

Reminds me of the day when someone tries to tell me I can’t do capability analysis on “counts” since it’s not variable data. I’m like really? They said yea…it has to have a natural decimal form.

It’s amazing what “rules of thumb” are taught without context.

I pointed out that count data CAN very well be treated as continuous, variable data–just consider it a lack of precision.

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