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Describe the difference between univariate, bivariate and multivariate analysis?

Posted by Chris Adams

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Categories: Business Analysis, Data Analysis & Modeling, Analytical and Problem Solving Skills, General


Three categories of data analysis include univariate analysis, bivariate analysis, and multivariate analysis.

Univarate Analysis

Univariate analysis is the simplest form of data analysis where the data being analyzed contains only one variable. Since it's a single variable it doesn’t deal with causes or relationships.  The main purpose of univariate analysis is to describe the data and find patterns that exist within it.
You can think of the variable as a category that your data falls into. One example of a variable in univariate analysis might be "age". Another might be "height". Univariate analysis would not look at these two variables at the same time, nor would it look at the relationship between them.
Some ways you can describe patterns found in univariate data include looking at mean, mode, median, range, variance, maximum, minimum, quartiles, and standard deviation. Additionally, some ways you may display univariate data include frequency distribution tables, bar charts, histograms, frequency polygons, and pie charts.

Bivarate Analysis

Bivariate analysis is used to find out if there is a relationship between two different variables. Something as simple as creating a scatterplot by plotting one variable against another on a Cartesian plane (think X and Y axis) can sometimes give you a picture of what the data is trying to tell you. If the data seems to fit a line or curve then there is a relationship or correlation between the two variables.  For example, one might choose to plot caloric intake versus weight.

Multivariate Analysis

Multivariate analysis is the analysis of three or more variables.  There are many ways to perform multivariate analysis depending on your goals.  Some of these methods include:
  • Additive Tree
  • Canonical Correlation Analysis
  • Cluster Analysis
  • Correspondence Analysis / Multiple Correspondence Analysis
  • Factor Analysis
  • Generalized Procrustean Analysis
  • Multidimensional Scaling
  • Multiple Regression Analysis
  • Partial Least Square Regression
  • Principal Component Analysis / Regression / PARAFAC
  • Redundancy Analysis.

Chris Adams
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