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How to Categorize Statistic Record

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Types of Data & Measurement Scales: Nominal, Ordinal, Interval and Ratio. In statistics, there are four data measurement scales: nominal, ordinal, interval and ratio. These are simply ways to sub-categorize different types of data (here's an overview of statistical data types).
Introduction to Data Types. Categorical Data (Nominal, Ordinal) Numerical Data (Discrete, Continuous, Interval, Ratio) Why Data Types are important?
The four scales of measurement are nominal, ordinal, interval, and ratio. Nominal: Categorical data and numbers that are simply used as identifiers or names represent a nominal scale of measurement.
Jeff Bertolucci of Information Week has written a new article about what distinguishes the three types of Big Data analytics: descriptive, predictive, and prescriptive. He writes, The majority of raw data, particularly big data, doesn't offer a lot of value in its unprocessed state.
So year is a discretized measure of a continuous interval variable, so quantitative. Year can also be an ordinal variable.
Ratio data is interval data with a natural zero point. For example, time is ratio since 0 time is meaningful. Degrees Kelvin has a 0 point (absolute 0) and the steps in both these scales have the same degree of magnitude.
Length of stay (LOS) is the duration of a single episode of hospitalization. Inpatient days are calculated by subtracting day of admission from day of discharge.
Ordinal data is data which is placed into some kind of order or scale. (Again, this is easy to remember because ordinal sounds like order). An example of ordinal data is rating happiness on a scale of 1-10. In scale data there is no standardized value for the difference from one score to the next.
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