/explore
|
You are here 
|
algobeans.com | ||
| | | | |
dm13450.github.io
Principal component analysis (PCA) reduces a dataset to its main components. When we apply it to a dataset of different currencies it helps us understand how each currency drives the overall portfolio and what currency might be a common factor. |
|
| | | | | ||
| | | | |
statsandr.com
Make the the most correlated variables stand out via a correlogram. See also how to enhance a correlation plot to show significant correlations among variables |
|
| | | | | ||
| | | | |
sportscidata.com
Recently Dan Weaving and the research group at Leeds Beckett University put out a paper outlining how to perform a type of dimension reduction on training load data: principal component analysis (PCA). The benefit of such an analysis is it can reduce a large number of metrics into a more manageable dataset. This may uncover... |
|
| | | | | ||
| | | | |
codeandculture.wordpress.com
A coauthor and I are doing power analyses based on a pilot test and we quickly realized that it's really hard to calculate a power analysis for anything much more exotic than a t-test of means. As I usually do when there's no obvious solution, I decided to just brute force it with a Monte... |
|
| | | |||
