Correspondence Analysis
=======================
Correspondence analysis for categorical multivariate data.
**Inputs**
- Data: input dataset
**Outputs**
- Coordinates: coordinates of all components
[Correspondence Analysis](https://en.wikipedia.org/wiki/Correspondence_analysis) (CA) computes the CA linear transformation of the input data. While it is similar to PCA, CA computes linear transformation on discrete rather than on continuous data.
![](images/CorrespondenceAnalysis-stamped.png)
1. Select the variables you want to see plotted.
2. Select the component for each axis.
3. [Inertia](https://en.wikipedia.org/wiki/Sylvester%27s_law_of_inertia) values (percentage of independence from transformation, i.e. variables are in the same dimension).
4. Produce a report.
Example
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Below, is a simple comparison between the **Correspondence Analysis** and [Scatter Plot](../visualize/scatterplot.md) widgets on the *Titanic* dataset. While the [Scatter Plot](../visualize/scatterplot.md) shows fairly well which class and sex had a good survival rate and which one didn't, **Correspondence Analysis** can plot several variables in a 2-D graph, thus making it easy to see the relations between variable values. It is clear from the graph that "no", "male" and "crew" are related to each other. The same goes for "yes", "female" and "first".
![](images/CorrespondenceAnalysis-Example.png)