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		Values have any meaningful order	Distance between values is defined	Mathematical operations make sense (Values can be used to perform mathematical operations)			There is a meaning ful zero-point	Values can be used to perform statistical computations					Example
				Comparison operators	Addition and subtrac tion	Multiplica tion and division		"Counts", aka, "Fre quency of Distribu tion"	Mode	Median	Mean	Stn
Nominal	Values serve only as labels	✘ ✘ ✘ ✘ ✘ ✘ ✔ ✔ ✘ ✘ ✘ Values don't have any meaningful order No distance between values is defined Values don't carry any mathematical meaning Values cannot be used to perform many statistical computations, such as mean and standard deviation Even if the values are numbers.  For example, if we want to categorize males and females, we could use a number of 1 for male, and 2 for female. However, the values of 1 and 2 in this case don't have any meaningful order or carry any mathematical meaning. They are simply used as labels. https://www.statisticssolutions.com/data-levels-and-measurement/											For an «outlook» attribute from weather data, potential values could be "sunny", "overcast", and "rainy".
Ordinal	Distinction between nominal and ordinal not always clear (e.g., attribute "outlook")	✔ ✘ ✔ ✘ ✘ ✘ ✔ ✔ ✔ ✘ ✘ Values have a meaningful order No distance between values is defined Only comparison operators make sense Mathematical operations such as addition, subtraction, multiplication, etc. do not make sense For example, an «Education level» attribute with possible values of «high school», «undergraduate degree», and «graduate degree». There is a definitive order to the categories (i.eº., graduate is higher than undergraduate, and undergraduate is higher than high school), but we cannot make any other arithmetic assumption.  For instance, we cannot assume that the difference in education level between undergraduate and high school is the same as the difference between graduate and undergraduate.											A «temperature» attribute in weather data with potential values fo: "hot" > "warm" > "cool"
Interval		✔ ✔ ✔ ✔ ✘ ✘ ✔ ✔ ✔ ✔ ✔ Distance between values is defined. In other words, we can quantify the difference between values Comparison operators make sense Addition, subtraction, make sense Multiplication, and division do not make sense Interval variables often do not have a meaningful zero-point. (not sure) An example of an interval variable would be a «Temperature» attribute.  We can correctly assume that the difference between 70 and 80 degrees is the same as the difference between 80 and 90 degrees.  However, the mathematical operations of multiplication and division do not apply to interval variables.  For instance, we cannot accurately say that 100 degrees is twice as hot as 50 degrees.  Additionally, interval variables often do not have a meaningful zero-point.  For example, a temperature of zero degrees (on Celsius and Fahrenheit scales) does not mean a complete absence of heat. An interval variable can be used to compute commonly used statistical measures such as the average (mean), standard deviation, and the Pearson correlation coefficient. https://www.statisticssolutions.com/data-levels-and-measurement/											a «Temperature» attribute composed by numeric measures of such property
Ratio		✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ All arithmetic operations are possible on a ratio variable Ratio variables have a meaningful zero-point An example of a ratio variable would be weight (e.g., in pounds).  We can accurately say that 20 pounds is twice as heavy as 10 pounds.  Additionally, ratio variables have a meaningful zero-point (e.g., exactly 0 pounds means the object has no weight). A ratio variable can be used as a dependent variable for most parametric statistical tests such as t-tests, F-tests, correlation, and regression. https://www.statisticssolutions.com/data-levels-and-measurement/											The «weight» (e.g., in pounds) Other examples: gross sales of a company, the income of a company, etc.