Bivariate Categorical Data Fashion Industry Clothing Intustry Data

In the field of statistics and data management, it can be given a huge list of categorical data examples and applications.

Data, in scientific meaning, is a set of data gathered for a purpose. Data is typically divided into two unlike types: categorical (widely known as qualitative data) and numerical (quantitative).

On this page y'all will learn:

• What is categorical data?Definition and fundamental characteristics.
• List of 22 examples of chiselled data.
• Categorical data vs numerical data.
• Infographic in PDF

Permit'due south define it:

Equally you might guess, chiselled data is data that is divided into groups or categories. These categories are based on qualitative characteristics such as gender and colors or something else that doesn't have a number associated with it.

This doesn't mean that categorical data cannot take numerical values.

In fact, categorical information often takes numerical values, only those numbers don't have whatever mathematical meaning. They just represent the number of items in each group. For instance 12 blondes in a course.

This makes it possible to do categorical data assay and dissimilar manipulations, particularly in a spreadsheet application.

There is no lodge to categorical values and variables. To put information technology in some other style, they aren't ranked from highest to everyman.

For example, at that place is no order to the categories of blue, brown and green eyes.

How to brandish categorical variables graphically?

Bar charts and pie charts are nifty tools for comparing two or more categorical values confronting each other. They just stand for the number of things in a category.

For case, if y'all desire to display the number of workers in a company, the outcomes can be presented on a pie nautical chart or on a bar graph.

Graphical chiselled information examples:

Survey on "What Motivates Employees to Work Better?"

Before creating a pie or bar chart, yous should bank check if data are in counts or percentages. To make a graphical display of categorical data, it is a necessary condition.

Assay of chiselled data very often includes data tables. The values are represented as a ii-way table or contingency table by counting the number of items that are into each category.

Hither is an case of a categorical information two-mode tabular array for a group of fifty people.

The table shows the results of the groups formed by counting the hair and centre color of each person.

Two-style and contingency tables are dandy tools for seeing how two chiselled variables are related.

The tabular array represents the counts or percentages of persons who belong to a group for two or more quantitative variables. Information technology makes easier to find different relationships betwixt the data.

Allow's sum the key characteristics of categorical data we learned above:

• Categorical data is divided into groups or categories.
• The categories are based on qualitative characteristics.
• There is no social club to categorical values and variables.
• Categorical data tin can have numerical values, but those numbers don't accept any mathematical meaning.
• Categorical data is displayed graphically past bar charts and pie charts.

When it comes to categorical data examples, it can be given a broad range of examples. In our previous post nominal vs ordinal data, we provided a lot of examples of nominal variables (nominal information is the main type of categorical data).

Examples of categorical information:

• Gender (Male, Female)
• Brand of soaps (Dove, Olay…)
• Hair color (Blonde, Brunette, Brown, Cherry-red, etc.)
• Survey on a topic "Do yous have children?" (Yes or No)
• Motives for employees to work better: (Peer Motivation, To Be Recognized, Opportunities for Professional Growth, Work Civilisation, the Feeling To Be Involved and etc.)
• Motives for traveling (Leisure, Business organisation Travel, To Visit Friends and etc.)
• Checking account location (California, Texas, Colorado…)
• Educational level: (Associate's degree, Bachelor'southward caste, Master'south degree, Doctoral degree and etc.)
• Historic period group (under 12 years erstwhile, 12-17 years onetime, 18-24 years old, 25-34 years old, 35-44 years old and etc.)
• Ethnicity (Hispanic, African American, Native American, Asian, Other)
• Eye colour (Dark-green, Blue, Brownish, Black)
• Household Composition (Single, Married, Widowed, Divorced)
• Employment condition (Employed for wages, Self-employed, A homemaker, A student, Retired and etc.)
• Film Genres (Activeness, Adventure, Comedy, Crime, Mystery, Drama, Historical and etc)
• Dwelling country (Canada, USA, Australia, Republic of india, Germany).
• Auto color (Cerise, Green, Grayness, Black, White and etc.)
• Religion (Muslin, Buddhist, Christian).
• Reasons for buying a nowadays (Birthday, Anniversary, and etc.)
• Seasons (Wintertime, Spring, Summer, Autumn)
• Holidays (Thanksgiving, Halloween)
• Types of pet (Dog, True cat, Hamster)
• Claret groups (Group A, Group B, Group AB, Grouping O).

When categorical data has just 2 possible values, information technology is called binary. If we use the categorical data examples above, the results of gender survey (male person and female) and the survey on a topic "Do you take children?" (Yes or No) are examples of binary data.

Categorical and Quantitative (Numerical) Data: Difference

Sometimes, it is hard to distinguish between chiselled and quantitative data.

Quantitative information is measured and expressed numerically. It has numerical meaning and is used in calculations and arithmetic.

That is why the other name of quantitative data is numerical.

Examples of quantitative data are: weight, temperature, height, GPA, almanac income, number of hours spent working and etc. More examples you can run across on the ThoughtGo commodity "Quantitative Data".

In comparing, the categorical information does non accept whatsoever numerical or quantitative meaning. Information technology but describes qualitative characteristics of something.

Types of quantitative data are: ordinal, interval, and ratio. Chiselled data is always 1 type – the nominal type.

The distinction between categorical and quantitative variables is crucial for deciding which types of information analysis methods to use. Quantitative data are analyzed using descriptive statistics, time series, linear regression models, and much more. For categorical data, typically only graphical and descriptive methods are used.

Download the following infographic in PDF

Conclusion

Every bit you see a lot of chiselled data examples tin be given to understand the significant and purpose of the qualitative information.

When working with information management or statistical sciences, it'due south crucial to conspicuously understand some of the main terms, including quantitative and categorical data and what is their role.

It is of import to get the meaning on the terminology right from the get-go, so when information technology comes time to bargain with the real data bug, yous will be able to work with them in the right way.

Share this post