Statistics How To

Univariate Analysis: Definition, Examples

Statistics Definitions > Univariate Analysis

What is Univariate Analysis?

Univariate analysis is the simplest form of analyzing data. “Uni” means “one”, so in other words your data has only one variable. It doesn’t deal with causes or relationships (unlike regression) and it’s major purpose is to describe; it takes data, summarizes that data and finds patterns in the data.

What is a variable in Univariate Analysis?

A variable in univariate analysis is just a condition or subset that your data falls into. You can think of it as a “category.” For example, the analysis might look at a variable of “age” or it might look at “height” or “weight”. However, it doesn’t look at more than one variable at a time otherwise it becomes bivariate analysis (or in the case of 3 or more variables it would be called multivariate analysis).

The following frequency distribution table shows one variable (left column) and the count in the right column.

univariate

A frequency chart.

You could have more than one variable in the above chart. For example, you could add the variable “Location”or “Age” or something else, and make a separate column for location or age. In that case you would have bivariate data because you would then have two variables.

Univariate Descriptive Statistics

Some ways you can describe patterns found in univariate data include central tendency (mean, mode and median) and dispersion: range, variance, maximum, minimum, quartiles (including the interquartile range), and standard deviation.

You have several options for describing data with univariate data. Click on the link to find out more about each type of graph or chart:

Questions? Please leave a comment. I will be happy to help!

Check out our Statistics YouTube channel for hundreds of videos on elementary statistics, probability and more.

------------------------------------------------------------------------------

If you prefer an online interactive environment to learn R and statistics, this free R Tutorial by Datacamp is a great way to get started. If you're are somewhat comfortable with R and are interested in going deeper into Statistics, try this Statistics with R track.

Comments are now closed for this post. Need help or want to post a correction? Please post a comment on our Facebook page and I'll do my best to help!
Univariate Analysis: Definition, Examples was last modified: October 21st, 2017 by Stephanie Glen

10 thoughts on “Univariate Analysis: Definition, Examples

  1. Florence Wabomba

    this has really assisted me to understand what uni varried analysis means. Am a student at Maseno Univerity undertaking a masters degree course in Public Health and now doing my thesis.

  2. MUKTI

    Dear Sir,
    Please if you can guide me for the application of one of the tool used by me in my study. I have used mean score to know the employees aspirations and management attitude regarding pay for performance in selected private sector banks of Haryana (ICICI, HDFC, Kotak Mahindra and Axis Bank). For employees total numbers of respondents were 475 and management 25. Employee’s aspirations and management attitude has been checked for following parameters:
    Parameters for measuring employees aspirations for Pay for Performance…

  3. vibkaush

    Hi,
    I am using univariate analysis for my project. My research question is: What are the specific needs of unaccompanied refugee minors in Germany that are not addressed by the existing refugee resettlement support? This is a descriptive research. Do you think this research question needs a hypothesis?

  4. Andale Post author

    If you’re planning to run any hypothesis tests on your data (sounds like you are, as you mention univariate analysis), then yes, you need a hypothesis.