Question

When do we use discrete, continuous, nominal, ordinal​

Answer 1

Data Types are an important concept of statistics, which needs to be understood, to correctly apply statistical measurements to your data and therefore to correctly conclude certain assumptions about it. This blog post will introduce you to the different data types you need to know, to do proper exploratory data analysis (EDA), which is one of the most underestimated parts of a machine learning project.

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Table of Contents:

Introduction to Data Types

Categorical Data (Nominal, Ordinal)

Numerical Data (Discrete, Continuous, Interval, Ratio)

Why Data Types are important?

Statistical Methods

Summary

Introduction to Data Types

Having a good understanding of the different data types, also called measurement scales, is a crucial prerequisite for doing Exploratory Data Analysis (EDA), since you can use certain statistical measurements only for specific data types.

You also need to know which data type you are dealing with to choose the right visualization method. Think of data types as a way to categorize different types of variables. We will discuss the main types of variables and look at an example for each. We will sometimes refer to them as measurement scales.

Categorical Data

Categorical data represents characteristics. Therefore it can represent things like a person’s gender, language etc. Categorical data can also take on numerical values (Example: 1 for female and 0 for male). Note that those numbers don’t have mathematical meaning.

Nominal Data

Nominal values represent discrete units and are used to label variables, that have no quantitative value. Just think of them as „labels“. Note that nominal data that has no order. Therefore if you would change the order of its values, the meaning would not change.

The left feature that describes if a person is married would be called „dichotomous“, which is a type of nominal scales that contains only two categories.

Ordinal Data

Ordinal values represent discrete and ordered units. It is therefore nearly the same as nominal data, except that it’s ordering matters.

Answer 2

•Use discrete and continuous when counting both integers and fractions. Both are quantitative subsets.

•Nominal and ordinal are qualitative constructs that are used to classify groups of topics (nominal) and place them in a particular hierarchy (ordinal).

Use of discrete , continuous, nominal, ordinal -

1) The terms discrete and continuous refer to the size of the set of values a variable can have.

2) Discrete variables have a finite or countably infinite set of ranges. (An important subtype of discrete variables is the binary or dichotomous variable.)

3) Continuous variables have an innumerable infinite set of ranges. (Usually you would include a range set here that could theoretically be counted, but not in practice.)

4) The terms nominal and ordinal refer to measurement scales. There are actually four scale types.

5) A nominal scale simply distinguishes between different values. The only meaningful comparison between values is whether they are equal.

6) An ordering scale introduces an ordering of objects. Values can be distinguished not only by equality, but also by the notion of greater than or less than.

7) An interval scale is an ordinal scale in which the difference in values is also meaningful. A ratio scale is an interval scale with a suitable zero point.

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