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# Univariate Analysis

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 a cause-and-effect relationship. 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 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 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 the simplest form to analyze data. Uni means one, and this means that the data has only one kind of variable. The primary reason for univariate analysis is to use the data to describe. The analysis will take data, summarize it, and then find some pattern in the data.

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Univariate analysis can be done in different ways, and some of them are enlisted below:

1. 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 and help us find out patterns in the data.

1. 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. The bar graph looks at the data set and makes comparisons. For example, it may be used to see what part is taking the maximum budget?

1. Histogram

The histogram is the same as a bar chart which analyzes 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.

1. 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 category size, and the entire pie is 100 percent.

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Univariate analysis is the most straightforward kind of data analysis in the field of statistics. This could be either descriptive or inferential, as is the case in any data analysis in statistics. The critical 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 is not complex, it may sometimes give some misleading information, especially if there are more variables involved.Â

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Examples
Measures of central tendency
Problem Score Companies Time Status
Change in mean and median 30
2:15
New average 30
1:52
Suitable mean 30
1:55
Measures of variability
Problem Score Companies Time Status
How much did he score? 30
2:46
Distribution analysis: univariate
Problem Score Companies Time Status
Median over mean 30
2:21
Difference 30
0:38
Univariate 30
1:33
Missing info 30
1:05
!univariate 30
1:48
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