A fundamental method of statistical data analysis is called univariate analysis. Here, there is only one variable in the data, so a cause-and-effect relationship is not present. Take a survey of a classroom, for instance. The analysts would want to make a count of how many boys and girls are present. Here, the only variable and variable quantity discussed in the data is the number. The primary goal of a univariate analysis is to identify patterns in the data by describing the data. The means, modes, medians, standard deviations, dispersion, etc. are examined in order to accomplish this.’
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The most basic type of data analysis is univariate analysis. Uni denotes one, so the data only contains one type of variable. Use of the data to describe is the main justification for univariate analysis. Data will be used in the analysis, which will then summarize the data and look for patterns.
In univariate analysis, a variable is merely a condition or subset of your data. It could be considered a “category.” For instance, the analysis could consider a variable like “age,” “height,” or “weight.” It does not, however, look at the relationship between different variables or examine more than one variable at once. Bivariate analysis is the study of two variables and their relationship. Multivariate analysis is the simultaneous consideration of three or more variables.
WHAT ARE THE STEPS IN UNIVARIATE ANALYSIS?
There are numerous ways to conduct a univariate analysis, and the majority of these methods are descriptive in nature. These are frequency polygons, frequency distribution tables, histograms, bar charts, and pie charts.