A good introduction to dealing with variables in a data set is to start with univariate statistics. The analysis of this type of data is the simplest.

When analyzing variables in a data set, you will conduct either multivariate, bivariate, or univariate analysis. This will be dependent on the number of variables and how many you want to analyze at once.

## Overview: What is univariate?

Univariate literally means one variable. It is a term used in relation to the statistical analysis of data, where univariate analysis involves separate exploration of each variable that is part of a data set.

## 3 benefits of univariate

There are some definitive benefits to working with univariate data that should be considered:

### 1. Finding patterns

One main benefit of univariate analysis is its ability to help recognize patterns in data.

### 2. Pointing the way toward advanced tests

Working with univariate analysis provides descriptions of single variables that might be of interest for more advanced tests.

### 3. Narrowing focus

Working with univariate analysis helps to narrow the types of analyses that should be carried out using bivariate and multivariate analysis.

## Why is univariate important to understand?

Univariate analysis is important to understand for the following reasons:

### Easy

Understanding univariate analysis is important because it makes data extremely easy to interpret.

### Distribution

Having a clear grasp of univariate analysis provides you with the ability to understand the manner in which data is distributed throughout the study of a population or sample.

### Know when to move on to other types of data analysis

Having an understanding of univariate analysis lets you know its limitations and when it is time to move over to bivariate or multivariate analysis.

## An industry example of univariate

A manufacturer wants to give an award to the worker in its plant who has had the fewest number of errors. It gathers the number of errors for every employee at the plant and finds that there is an employee who has only had three errors during their time at the organization. The analysis of the number of employee errors is an example of univariate analysis since the only variable being looked at is the number of errors.

## 3 best practices when thinking about univariate

Here are some key practices for when you are thinking about univariate data

### 1. Just one variable

Keep an eye out for data sets that have only one variable or if it makes sense to look at the data one variable at a time. This type of data is ideal for univariate analysis.

### 2. Working with summary statistics

If using summary statistics to analyze univariate data, first look at either measures of dispersion or measures of central tendency, as these are the most common methods.

### 3. Looking at causes

While univariate analysis is simple, you still have to look at relationships and causes.

### What’s the difference between univariate, bivariate, and multivariate analysis?

Multivariate focuses on multiple variables at a time, whereas bivariate compares two, and univariate summarizes one variable.

### What types of analyses are used to analyze univariate data?

Some types of analysis would be summary statistics, pie charts, frequency polygons, histograms, bar charts, and frequency dispersion tables.

### How can you tell if the data is bivariate?

If a data set has two variables to analyze, then it is bivariate.

## The simplicity and power of univariate analysis

Univariate analysis is generally agreed upon to be the simplest form of data analysis in statistics. Though it is simple, it is powerful for finding patterns when looking at dispersion, standard deviation, mean, mode, and median. If you anticipate ever working with only one variable in a data set or that it will ever make sense to tackle each one individually, be sure to have knowledge of univariate analysis as one of your skill sets.