Categorical Variable in statistics is a variable that can take on one of a limited, and usually fixed number of possible values, assigning each unit of observation to a particular group or nominal category on the basis of some qualitative property. In computer science and some branches of mathematics, categorical variables are referred to as enumerations or enumerated types. Commonly, each of the possible values of a categorical variable is referred to as a level. The probability distribution associated with a random categorical variable is called a categorical distribution. Categorical data is the statistical data type consisting of categorical variables or of data that has been converted into that form, for example as grouped data. A categorical variable that can take on exactly two values is termed a binary variable or dichotomous variable an important special case is the Bernoulli variable. Categorical variables with more than two possible values are called polytomous variables, categorical variables are often assumed to be polytomous unless otherwise specified.
Was the above useful? Please share with others on social media.
Recommended reading list:
|Data Science from Scratch: First Principles with Python
Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch.
If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out.
Get a crash course in Python
Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science
Collect, explore, clean, munge, and manipulate data
Dive into the fundamentals of machine learning
Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering
Explore recommender systems, natural language processing, network analysis, MapReduce, and databases