Course Navigator

Back To Course

McDougal Littell Pre-Algebra: Online Textbook Help13 chapters | 144 lessons

Watch short & fun videos
**Start Your Free Trial Today**

Instructor:
*Cathryn Jackson*

Cat has taught a variety of subjects primarily in Communication, Mathematics, and Technology. Cat has a Master's degree in Education and is currently working on her Ph. D.

Categorical data is often used in mathematical and scientific data collection. In this lesson, you will learn the definition of categorical data and analyze examples. When you've finished, review what you've learned with a short quiz.

**Data**, in mathematical and scientific speak, is a group of information collected. This information could be anything, and can be used to prove or disprove a hypothesis (or scientific guess) during an experiment. Data that can be collected can be height, weight, a person's opinion on a political issue, the number of people that catch a certain cold over a year, and so much more. Data is usually grouped into two different types of information: **categorical** and **numerical**. In this lesson, we'll talk about categorical data.

All data collected is collected in the form of numbers, but we don't often know what those numbers mean. Categorical data puts a meaning to those numbers. If I gave you the numbers 4, 10, and 12 you wouldn't know what to do with them or what they mean. However, if I said there are 4 blondes in a class, 10 redheads, and 12 brunettes you would have a better understanding of what those numbers mean. That's because I grouped those numbers into categories.

What to look for when identifying categorical data:

**Categories:** Categorical data, as the name implies, is grouped into some sort of category or multiple categories. For example, if I were to collect information about a person's pet preferences, I would have to group that information by the type of pet. Categorical data is also data that is collected in an either/or or yes/no fashion. For example, if I were to ask the people in my office to check yes/no on whether they had children, then I can display that information in a bar graph or a pie chart comparing co-workers that had children versus co-workers that do not have children.

Look at the example below. Can you identify the categorical data?

Jill is collecting information about her restaurant's pizza sales. On the left, she has created a chart showing the number of pizzas sold, divided by the type of pizza. On the right, she has the amount of money made from the pizza sales that month. Which information is categorical data?

How did you do? In this example the categorical data is the number of pizzas sold, grouped by types.

Let's try another one:

Look at the example below. Can you identify the categorical data?

Michael is collecting information about television programming. He sent out a survey asking the following questions:

- Which TV programs do you watch the most often?
- Approximately how many hours of television do you watch each day?
- How many people live in your household?
- What are the age groups of the people in your household?

(0-5) (6-10) (11-15) (16-20) (21-30) (31-40) (41-50) (51-60) (61+)

Which questions asked for information that could be collected as categorical data?

How did you do? In this example, the categorical data is questions one and four. You can make a bar graph or a pie graph out of the information that is gathered from either question. Both questions require you to separate the information by categories. For question number one, you would have to group the information by the television program named or by the genre of television programming. For question number four, you would have to group the information by which age group was selected.

Data that is collected can be either categorical or numerical data. Numbers often don't make sense unless you assign meaning to those numbers. Categorical data helps you do that. Categorical data is when numbers are collected in groups or categories. Categorical data is also data that is collected in an either/or or yes/no situation.

To unlock this lesson you must be a Study.com Member.

Create your account

Did you know… We have over 100 college courses that prepare you to earn credit by exam that is accepted by over 2,900 colleges and universities. You can test out of the first two years of college and save thousands off your degree. Anyone can earn credit-by-exam regardless of age or education level.

To learn more, visit our Earning Credit Page

Not sure what college you want to attend yet? Study.com has thousands of articles about every imaginable degree, area of study and career path that can help you find the school that's right for you.

Course Navigator

Back To Course

McDougal Littell Pre-Algebra: Online Textbook Help13 chapters | 144 lessons

- Creating & Reading Stem & Leaf Displays 4:27
- Creating & Interpreting Histograms: Process & Examples 5:43
- Creating & Interpreting Box Plots: Process & Examples 6:29
- Understanding Bar Graphs and Pie Charts 9:36
- Scatterplots and Line Graphs: Definitions and Uses 7:17
- Simple Random Samples: Definition & Examples 5:10
- What is Random Sampling? - Definition, Conditions & Measures 5:55
- Stratified Random Samples: Definition, Characteristics & Examples 6:25
- Systematic Random Samples: Definition, Formula & Advantages 8:37
- Issues in Probability & Non-Probability Sampling 7:50
- The Relationship Between Population, Sample & Generalizability 7:09
- What Is a Factorial? 5:24
- How to Calculate a Permutation 6:58
- Math Combinations: Formula and Example Problems 7:14
- The Addition Rule of Probability: Definition, Examples & Quiz 10:57
- Probability of Independent and Dependent Events 12:06
- The Multiplication Rule of Probability: Definition, Examples & Quiz 8:37
- Categorical Data: Definition, Analysis & Examples
- Go to McDougal Littell Pre-Algebra Chapter 11: Data Analysis and Probability

Browse by subject