milk

<p>Dataset to illustrate the selection of variables in regression</p>

datasets

Exploratory data analysis methods to summarize, visualize and describe datasets. The main principal component methods are available, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical, Multiple Factor Analysis when variables are structured in groups, etc. and hierarchical cluster analysis. F. Husson, S. Le and J. Pages (2017).

Francois Husson

FactoMineR

Multivariate Exploratory Data Analysis and Data Mining

milk function

<p>Dataset with 85 rows and 6 columns :
85 milks described by the 5 variables: density, fat content, protein, casein, dry, yield</p>

milk: milk

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Examples