In statistics, there are three kinds of techniques that are used in the data univariate data analysis. These are univariate analysis, bivariate analysis, and multivariate analysis. How the data analysis technique is selected is based on the variable number and the data type. The statistical inquiry focus is also something to be considered. This article explains the details of the univariate analysis.

**What is Univariate Analysis?****How do you conduct Univariate Analysis?****Examples of univariate analysis**

So what is univariate analysis? Univariate analysis is a basic kind of analysis technique for statistical data. Here the data contains just one variable and does not have to deal with the relationship of a cause and effect. Like for example consider a survey of a classroom. The analysts would want to count the number of boys and girls in the room. The data here simply talks about the number which is a single variable and the variable quantity. The main objective of the univariate analysis is to describe the data in order to find out the patterns in the data. This is done by looking at the mean, mode, median, standard deviation, dispersion, etc.

Univariate analysis is basically the simplest form to analyze data. Uni means one and this means that the data has only one kind of variable. The major reason for univariate analysis is to use the data to describe. The analysis will take data, summarise it, and then find some pattern in the data.

A variable is simply a condition or subset of your data in univariate analysis. It can be thought of as a “category.” For example, the analysis could look at a variable such as “age,” or it can look at “height,” or “weight.” However, it does not examine more than one variable at a time, nor would it look at their relationship. The analysis of two variables and their relationship is termed bivariate analysis. If three or more variables are considered simultaneously, it is multivariate analysis.

Univariate analysis is conducted in many ways and most of these ways are of a descriptive nature. These are the Frequency Distribution Tables, Frequency Polygons, Histograms, Bar Charts and Pie Charts

Let us get into details here of the kind of analysis that is done to analyze univariate data.

**Summary Statistics**

The most common method for performing **univariate analysis** is summary statistics. The appropriate statistics are determined by the level of measurement or the nature of the information contained within the variables. The following are the two most common types of summary statistics:

**Measures of Dispersion**: These numbers describe how evenly distributed the values are in a dataset. The range, standard deviation, interquartile range, and variance are some examples.- Range -the difference between the max value and min value in a dataset
- Standard Deviation- an average measure of the spread
- Interquartile Range- the spread of the middle 50% of values

**Measures of central tendency**: These numbers describe the location of a dataset’s center or the middle value of the data set. The mean and median are two examples.

**Frequency distribution table**

Frequency means how often something takes place. The observation frequency tells the number of times for the occurrence of an event. The frequency distribution table may show categorical or qualitative and numeric or quantitative variables. The distribution gives a snapshot of the data and lets you find out the patterns.

**Bar chart**

The bar chart is represented in the form of rectangular bars. The graph will compare various categories. The graph could be plotted vertically or these could be plotted horizontally. In maximum cases, the bar will be plotted vertically. The horizontal or the x-axis will represent the category and the vertical y-axis represents the category’s value. The bar graph looks at the data set and makes comparisons. Like for example, it may be used to see what part is taking the maximum budget?

**Histogram**

The histogram is the same as a bar chart which analysis the data counts. The bar graph will count against categories and the histogram displays the categories into bins. The bin is capable of showing the number of data positions, the range, or the interval.

**Frequency Polygon**

The frequency polygon is pretty similar to the histogram. However, these can be used to compare the data sets or in order to display the cumulative frequency distribution. The frequency polygon will be represented as a line graph.

**Pie Chart**

The pie chart displays the data in a circular format. The graph is divided into pieces where each piece is proportional to the fraction of the complete category. So each slice of the pie in the pie chart is relative to categories size. The entire pie is 100 percent and when you add up each of the pie slices then it should also add up to 100.

Pie charts are used to understand how a group is broken down into small pieces.

The univariate data is the one that consists of just one variable. The analysis of univariate data is the simplest since the information has to deal with a single quantity only and the changes in it. It does have to study the relationship and cause and the analysis is used to describe the data and to find out the pattern that exists in it.

Like for example, the height of ten students in a class can be recorded and this is univariate data. There is only one variable which is the height and thus it does not have any relationship and cause attached to it. The description of the pattern that is found in this type of data is made by drawing out conclusions based on dispersion, central measures of tendency, spread, or data, and this is done through the histograms, frequency distribution table, bar charts, etc.

Univariate analysis works by examining its effect on a single variable on a given data set. Like for example, the frequency distribution table is a kind of univariate analysis. Here only one variable is involved in the data analysis. There could however be many alternate variables too like height, age, and weight. As soon as a secondary variable gets introduced in the analysis then this is a bivariate analysis. When there is three or more than three variable involved in the data analysis then this is the multivariate analysis.

Univariate is a common term that you use in statistics to describe a type of data that contains only one attribute or characteristic. The salaries of people in the industry could be a univariate analysis example. The univariate data could also be used to calculate the mean age of the population in a village.

Univariate analysis is the simplest kind of data analysis in the field of statistics. This could be either descriptive or inferential in nature as is the case in any data analysis in statistics. The key thing about the univariate analysis to remember is that there is only one data involved here. While the univariate analysis may be easy to analyze and also is not complex, at times it may end up giving some misleading information especially if there are more variables involved. In this case, you need to move to the bivariate and the multivariate analysis that will be capable of analyzing the data better.

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