Often, it is not helpful or informative to only look at all the variables in a dataset for correlations or covariances. A preferable approach is to derive new variables from the original variables that preserve most of the information given by their variances. Principal component analysis is a widely used and popular statistical method for reducing data with many dimensions (variables) by projecting the data with fewer dimensions using linear combinations of the variables, known as principal components.

## Singular Value Decomposition and R Example

SVD underpins many statistical and real-world

## How to Calculate the Inverse Matrix for 2×2 and 3×3 Matrices

The inverse of a number is its reciprocal. For example, the inverse of 8

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