Making statistics intuitive
Imputation in statistics is the process of replacing missing data points with plausible values. This technique is crucial because missing values can bias the statistical results. When applied correctly, imputed data reduce this bias. [Read more…] about Imputation of Missing Values Overview
Principal Component Analysis (PCA) takes a large data set with many variables per observation and reduces them to a smaller set of summary indices. These indices retain most of the information in the original set of variables. Analysts refer to these new values as principal components. [Read more…] about Principal Component Analysis Guide & Example
Factor analysis uses the correlation structure amongst observed variables to model a smaller number of unobserved, latent variables known as factors. Researchers use this statistical method when subject-area knowledge suggests that latent factors cause observable variables to covary. Use factor analysis to identify the hidden variables. [Read more…] about Factor Analysis Guide with an Example