Difference between revisions of "Statistics notes  types of data"
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m (→Interval data (ordered, no meaningful zero point, possible linear scale, (often) continuous)) 
m (→Discrete numeric data (ordered, linear scale, discrete)) 

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Think integers. Includes things like counts.  Think integers. Includes things like counts.  
+  
Examples:  Examples:  
−  *  +  * many counts, e.g. the amount of attendees to a daily event 
+  * the profit we make per week  
+  
Latest revision as of 16:55, 17 August 2022
This is more for overview of my own than for teaching or exercise.

This article/section is a stub — probably a pile of halfsorted notes, is not wellchecked so may have incorrect bits. (Feel free to ignore, or tell me) 
Contents
 1 One possible typology
 1.1 Ratio data (ordered, meaningful zero point, linear scale, (often) continuous)
 1.2 Interval data (ordered, no meaningful zero point, possible linear scale, (often) continuous)
 1.3 Discrete numeric data (ordered, linear scale, discrete)
 1.4 Ordinal data (ordered, but no obvious numbering so not linear; discrete)
 1.5 Nominal/categorical data (unordered; qualitative; discrete)
 2 More words
 3 Variables, dimensions, and measurement, and experiments
One possible typology
Ratio data (ordered, meaningful zero point, linear scale, (often) continuous)
This article/section is a stub — probably a pile of halfsorted notes, is not wellchecked so may have incorrect bits. (Feel free to ignore, or tell me) 
Properties:
 ordered/monotonous (larger value means larger represented thing)
 linearly comparable, e.g. twice the distance in these numbers means twice the amount of difference in represented thing)
 meaningful zero point
 the combination of the above imply numbers are proportional: twice the number directly means twice the amount of different thing
Examples:
 weight
 length
 time amount  reaction time, hours of study, time required to run a marathon
 age
 temperature in Kelvin
 number of responses (note: overlap with discrete numeric)
 many physical measurements in general
 though not all  can depends on units and their implied zeroing. For example, Farenheit and Celcius are not zeroed according to energy
Interval data (ordered, no meaningful zero point, possible linear scale, (often) continuous)
Properties:
 ordered/monotonous (larger value means larger represented thing)
 comparability often not linear, though could be for any given case
 no particularly meaningful zero point
Interval data is quantitative data in a numbering system in which there is no sensible zero point.
This means the assumption of linear relationships may be incorrect (often the most important difference compared to ratio data)
 ...because of the zero point
 ...because the scale is arbitrary
 ...and/or because of other reasons
Discrete numeric data (ordered, linear scale, discrete)
Ordinal data (ordered, but no obvious numbering so not linear; discrete)
Examples:
 highest level of education
 questionnaire items of the 'strongly disagree to strongly agree in five steps' sort
 age groups ('age up to 20', 'age 2029', 'age 3039') (note: in this case based on ratio data)
 socioeconomic status (sort of)
 most any ranking
Asking people to rate on a fewpoint scale is often seen as ordinal, while there is overlap with continuous interval data.
Nominal/categorical data (unordered; qualitative; discrete)
Examples:
 labels, such as {T,A,G,C}
 choosing from multiple choice options
 distinguishers such as {green,blue} or {true,false}, (discrete) gender, blood type
 brand names
 left/righthandedness
 political affiliation
More words
More complex cases
Implications
Further terms that matter
Continuous data refers to valued numbering that is not restricted to be discrete/integer.
 so ratio or interval data in the above list.
Quantitative data  basically anything not categorical, so referring to nominal/categorical.