There are mainly four different data types in statistics. Let’s go over the four data types.
First, let’s look at quantitative data type.
1. Quantitative: data types that can be added, subtracted to gain useful insight
ex) height, weight, asset
Quantitative data types can be further broken into 1) quantitative continuous and 2) quantitative discrete data types.
1–1. Continuous: data types that can be split into smaller and smaller units, and still a smaller unit exists.
ex) age of the dog — we can measure the units of the age in years, months, days, hours, seconds, but there are still smaller units that could be associated with the dog.
1–2. Discrete: data only takes on countable values.First, let’s look at quantitative data type.
1. Quantitative: data types that can be added, subtracted to gain useful insight
ex) height, weight, asset
Quantitative data types can be further broken into 1) quantitative continuous and 2) quantitative discrete data types.
1–1. Continuous: data types that can be split into smaller and smaller units, and still a smaller unit exists.
ex) age of the dog — we can measure the units of the age in years, months, days, hours, seconds, but there are still smaller units that could be associated with the dog.
ex) number of dogs we interact with
Now, let’s look at categorical data type.
2. Categorical: data types that can be thought of as labels for a group of items/ individuals.
ex) zip code, letter grade(A+, A, B…etc.), location, country, breakfast type, gender
The categorical data type can be further broken into 1) categorical ordinal, and 2) categorical nominal data types
2–1. Categorical Ordinal: data type that takes on a ranked ordering
ex) survey rating(very positive, positive, neural, negative, very negative), letter grade(A+, A, A, B+, B, B- … etc.)
2–2. Categorical Nominal: data do not have an order or ranking
ex) dog breed(poodle, pug, chihuahua, greyhound), gender, martial status, zip code, breakfast items
The below table sums up the four data types we covered in this post.
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