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Lynda Business Analytics Data Reduction Techniques Using Excel and R
Included Exercise Files
Skill Level : Advanced
Duration : 1h 2m
Language : English
Released : 4/14/2017
Size : 112,39 Mb
With businesses having to grapple with increasing amounts of data, the need for data reduction has intensified in recent years. To make sense of an overabundance of information, you can use cluster analysis—which allows you to develop inferences about a handful of groups instead of an entire population of individuals—as well as principal components analysis, which exposes latent variables.
In this course, Conrad Carlberg explains how to carry out cluster analysis and principal components analysis using Microsoft Excel, which tends to show more clearly what's going on in the analysis. Then he explains how to carry out the same analysis using R, the open-source statistical computing software, which is faster and richer in analysis options than Excel. Plus, he walks through how to merge the results of cluster analysis and factor analysis to help you break down a few underlying factors according to individuals' membership in just a few clusters.
Topics include:
Reviewing the problems created by an overabundance of data
Understanding the rationale for clustering and principal components analysis
Using Excel to extract principal components
Using R to extract principal components
Using R for cluster analysis
Using Excel for cluster analysis
Setting up confusion tables in Excel
Using cluster analysis and factor analysis in concert
