Data type
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 This topic has 18 replies, 9 voices, and was last updated 19 years, 7 months ago by YF Gao.

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April 22, 2003 at 7:08 am #32047
I have a basic statistic concept question afflicting me.
According to different criteria: data can be catergorized as “attribute” and “variable” data, on the other hand, data can also be classified as “discrete” and “continuous” data.
Some say: “attribute data” = “discrete data” and “variable data”=”continuous data”.
Anyone can help in clarifying these concepts? Many thanks!0April 22, 2003 at 12:17 pm #85122
Ed Van HauteParticipant@EdVanHaute Include @EdVanHaute in your post and this person will
be notified via email.Attribute data has only two possible outcomes: yes/no, go/no go, etc.
Variable data can be either discrete or continuous.
Discrete data us measured in units, and is countable. # of people, 3 of items passing test, number of calls arriving, etc.
Continuous data can assume any numerical value, and is dependent on the sensitivity of the measuring device.0April 22, 2003 at 12:26 pm #85123
Ed Van HauteParticipant@EdVanHaute Include @EdVanHaute in your post and this person will
be notified via email.Miskeyed part of my reply. Part of the third line should read “# of items passing test”.
0April 22, 2003 at 1:27 pm #85128So if I had a marble factory and was producing colored marbles, say white, red, and black. What kind of data would color be? It is attribute. Just because it has more than two possibilities doesn’t make it not attribute. Attribute = Discrete, Variable = Continuous. The difference between having two discrete categories versus more than two just means you no longer have binomial data.
0April 22, 2003 at 4:44 pm #85138
Ed Van HauteParticipant@EdVanHaute Include @EdVanHaute in your post and this person will
be notified via email.Color is an example of “Qualitative” data. Attribute data by definition has only two possible outcomes. Discrete data is Quantitative, variable data that can be counted, and can have mathmatical operations performed on it.
0April 22, 2003 at 5:22 pm #85141So Qualitative data can’t have mathematical operations performed on it? There are whole courses on analyzing qualitative data, and it can be both discrete and continuous.
0April 22, 2003 at 6:26 pm #85143
Ed Van HauteParticipant@EdVanHaute Include @EdVanHaute in your post and this person will
be notified via email.Qualitative data, in it’s raw form, cannot by it’s nature, have mathmetical operations performed on it. To use your example, how do you add or multiply “red” and “black”. What you can do is perform operations on the frequencies of occurrences. Those frequencies of occurrences are variable data, quantitative by nature. Quantitative data can be either continuous or discrete.
As to my statement that Attribute data can only assume one ot two outcomes, I refer you to the glossary of IMPLEMENTING SIX SIGMA (Breyfogle), and page 53 of the same text. If this is a matter of interpretationso be it.
I think our discussion is becoming pedantic. Out here.
0April 22, 2003 at 9:11 pm #85150I’ll agree…
0April 23, 2003 at 12:02 am #85152After reading these threads, I am still confusing about it. Anyone there can give a clear explanation? Thanks
0April 23, 2003 at 7:16 am #85156Maybe I can help you clear this up a little.
Attribute data=discrete=count data
Variable data= continuous=measured
Attribute data catagorizes the data. To use the marble example used earlier, you may have marbles in a jar that can be 3 colors (red, green, & black). You can’t really do anything with this data except to count it (which makes it by definition discrete data). It is also true that there are only 2 outcomes to attribute data. For example: You count the number of red marbles to be 10 in a jar of 100 marbles. Your two outcomes are either a red marble or not a red marble (some other color). Other examples of attribute data would include yield (pass/fail), # of defects on a part, etc. There are equations that use the frequencies which you can use to statistically determine if you’ve made a significant change or not. In general though, these are not as powerful or as extensive as the equations used for variable data.
Variable data is always measured and the outcome (measurement) can be any numerical value. Discrete data is finite and therefore cannot be considered continuous (infinite).
In general, if all you can do is count the numbers of something, it’s attribute data. If you measure it to get some value (e.g. time, pressure, length), then it’s variable data.0April 23, 2003 at 7:43 am #85157Thanks Jim:
I understand that attribute data is “discrete data”. However, are all “discrete data” belonging to “attribute data”?
can I say “attribute data” is one subset of “discrete data”?
The same question to the variable data and “continuous data”. Is “continuous data” one subset of “variable data” or on the reverse? or they are interchangeable?0April 23, 2003 at 9:11 am #85158Hi
i also feel that all atttribute data is discrete.so we can safely say that attribute data is subset of discrete data.0April 23, 2003 at 9:12 am #85159Discrete data us measured in units, and is countable. # of people, 3 of items passing test, number of calls arriving, etc.
Continuous data can assume any numerical value, and is dependent on the sensitivity of the measuring device.0April 24, 2003 at 6:35 am #85189Attribute and discrete data are used interchangeably as are the terms variable and continuous. In both cases, one is not a subset of the other.
0April 24, 2003 at 11:45 pm #85224All random variables are defined on one of four scales of measurement: nominal, ordinal, interval, and scale. Interval and scale are continous which means that in any arbitrarily small window of obervsation there exist uncountably infinite possible values. In quality, interval and scale data are referred to traditionally as variables data — one takes continous measurements on a variable. Attribute is a quality term for types of data and has come to generally apply to any data that is not continuous. The historic intent was that Attribute would apply to go/no go but its use has expanded over time to include all nominal and ordinal data. Nominal data is a scale of measurement consisting of unordered categories (statisticians often call this categorial data) like the colors of M&M’s. Ordinal data consists of ordered categories, there is a natural ordering to the categories and this type of data consists of ratings (like a Lichert scale) and rankings. The term discrete and continous are used by statisticians to classify random variables into two classes. Numeric observsations on discrete random variables can only take on integer values while continous random variables can take on infinite values. Discrete data consists of counts and proportions that are taken on nominal or ordinal random variables. As an example, the binomial distribution applies to discrete random variables that consists of the counts from two categories (e.g., pass or fail). The Poisson distribution applies to counts of defects per unit of observation and is the basis for DMPO calculations. The normal distribution applies to random variables on a continous scale of measurements. I hope this long winded discussion is not too confusing. But, don’t confuse the four scales of measurements, with the two broad categories of numeric data that define probability distributions. Unless one can turn nominal and ordinal data into numeric data (discrete counts), then a statistical analysis would not be possible.
0April 25, 2003 at 3:15 am #85231
HariprasadParticipant@Hariprasad Include @Hariprasad in your post and this person will
be notified via email.Variable Data(Continous Data): Measured ona Continous scale
Discrete Data/Attribute Data: These are like countables or classification. This further can be classified as Ordinal, Nominal and Binary data
Ordinal: Countables.. No. of Deaths, No. of defects etc
Nominal: High/Medium/Low or Red/Yellow/Blue/Green etc
Binary: Good/Bad, Pass/Fail0April 25, 2003 at 11:24 am #85240
TierradentroParticipant@john Include @john in your post and this person will
be notified via email.Philthe four scales you described are do not preclude the use of statistics. An example is using Contingency Tables for Nominal data or KruskalWallis Test on Ordinal data. Typically as the scale’s resolution decreases i.e. Ratio (you called Scale) >Interval > Ordinal > Nominal, then the amount of data that is needed to determine significance increases. Reliance on the distributions such as Normal, Students t, etc. cannot be used directly with anything other than Ratio data. JMP software does a nice job in allowing the user to define the data as Continuous, Ordinal or Nominal and allowing the appropriate test for the data type.
0April 28, 2003 at 1:33 am #85287Attribute data has clearly defined, two possible outcomes e.g YES/NO; HEAD/TAIL etcHowever, Variable data gives you the options to choose. It can be divided into continuos and discrete data also.Example of Discrete data: Scoring a Composition on 1/2/3/4 or 5Example of Continuos data: Radius of a circle being 1.5 cm.
0April 28, 2003 at 3:37 am #85288Sheetal:
Your understanding is similar to my original understanding. However, I was also told that “attribute=discrete” and “variable=continuous”.
Any “guru” can give a confirmation on this confusing? related reference is highly appreciated.0 
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