Referenced from lesson Introduction to Computational Data Science (Spring, 2021)
For each piece of data below, circle whether it is Categorical or Quantitative data.
1 |
Hair color |
categorical quantitative |
2 |
Age |
categorical quantitative |
3 |
ZIP Code |
categorical quantitative |
4 |
Year |
categorical quantitative |
5 |
Height |
categorical quantitative |
6 |
Sex |
categorical quantitative |
7 |
Street Name |
categorical quantitative |
For each question, circle whether it will be answered by Categorical or Quantitative data.
8 |
We’d like to find out the average price of cars in a lot. |
categorical quantitative |
9 |
We’d like to find out the most popular color for cars. |
categorical quantitative |
10 |
We’d like to find out which puppy is the youngest. |
categorical quantitative |
11 |
We’d like to find out which cats have been fixed. |
categorical quantitative |
12 |
We want to know which people have a ZIP code of 02907. |
categorical quantitative |
13 |
We’d like to sort a list of phone numbers by area code. |
categorical quantitative |
These materials were developed partly through support of the National Science Foundation,
(awards 1042210, 1535276, 1648684, and 1738598). 
Bootstrap:Data Science by the Bootstrap Community is licensed under a Creative Commons 4.0 Unported License. This license does not grant permission to run training or professional development. Offering training or professional development with materials substantially derived from Bootstrap must be approved in writing by a Bootstrap Director. Permissions beyond the scope of this license, such as to run training, may be available by contacting contact@BootstrapWorld.org.