Just to make sure – here are some other examples of discrete and continuous data: To sum up, your weight can vary by incomprehensibly small amounts and is continuous, while the number of children you want to have is directly understandable and is discrete. Your exact weight is a continuous variable – it can take on an infinite amount of values no matter how many digits there are after the dot. The process of losing and gaining weight occurs all the time. Every drop of sweat reduces your weight by the weight of that drop, but once again, a scale is unlikely to capture that change. If you gain 0.01 pound, the figure on the scale is unlikely to change, but your new weight will be 150.01 pounds or 68.0434 kilograms. Say you get on the scale and the screen shows 150 pounds or 68.0389 kilograms. A Case in Pointįor instance, your weight can take on every value in some range. Continuous data is infinite, impossible to count, and impossible to imagine. It’s easier to understand discrete data by saying it’s the opposite of continuous data. Therefore, the numerical variable is discrete. So, we can imagine and go through all possible values in our head. Moreover, 10 points separate all possible scores that can be obtained. We know that SAT scores range from 600 to 2400. What is important for a variable to be defined as discrete is that you can imagine each member of the dataset. So a number like 0, 1, 2, or even 10.Īnother instance is grades on the SAT exam. Even if you don’t know exactly how many, you are absolutely sure that the value will be an integer. Take the number of children that you want to have. It is further divided into two subsets: discrete and continuous.ĭiscrete data can usually be counted in a finite matter. Numerical data, on the other hand, as its name suggests, represents numbers. This is what you should know about categorical variables. Yes and no would be the two groups of answers that can be obtained. One example would be car brands like Mercedes, BMW and Audi – they show different categories.Īnother instance of categorical variables is answers to yes and no questions.Īre you currently enrolled in a university?
![numeric variable numeric variable](https://ph-static.z-dn.net/files/d6b/654668f90c1c8b2450a93a9aabcda9d6.jpg)
Let’s start with the types of data we can have: numerical and categorical.Ĭategorical data describes categories or groups. We can do this in two main ways – based on its type and on its measurement levels.
![numeric variable numeric variable](https://cdn.atomisystems.com/uploads/2018/06/using-number-variables-12-1.png)
Now, let’s focus on classifying the data. Numerical and Categorical Types of Data in Statistics
![numeric variable numeric variable](https://d1ka0itfguscri.cloudfront.net/FeJ/2016/04/01/07/18/cDfViF1vMP/preview.jpg)
#NUMERIC VARIABLE HOW TO#
Author's note: If you're wondering how to make data science your professional path, check out our articles: The Data Scientist Profile, How to Get a Data Science Internship, 5 Business Basics for Data Scientists, and, of course, Data Scientist Career Path: How to find your way through the data science maze.