#Xlstat principal component analysis how to

LetвЂ™s say we are building a model to predict revenue using a simple linear regression algorithm, i.e., having only one independent variable advertising spend. In our world, where we make decisions based on this structured data, there is a potential danger of having too much information and even the risk of losing some important information. In other words, information is structured data, having logical reasoning that makes it coherent and can drive decisions. This context is what transforms data into information. These numbers and texts do not have any meaning in themselves without a context attached to them. But these are raw and unstructured figures. The ICC cricket match score that you checked is another form of data. The text message that you received today is one form of data. Once we look around our surroundings we will find data in any form, shape, and size.Дata is an indispensable variable in our life. Overview of Data and Dimension Reduction Techniques Which method to choose based on the problem objective?.Use Cases And Applications of Factor Analysis.Difference between PCA and Factor Analysis.Overview of Data and Dimension Reduction Techniques.We shall also look at the difference between these two methods and decide which method shall we use PCA or Factor Analysis? Table of Contents

#Xlstat principal component analysis how to

In this article, we will understand PCA and Factor analysis, their use cases, and how to apply these techniques. Both of these techniques help in minimizing information loss and have some similarities. Principal Component Analysis (PCA) and Factor Analysis (FA) are the two most prominent dimensionality reduction techniques available.