Multivariate Statistics
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PCA
[see principal component anlysis]
ACP
[véase análisis de componentes principales]
adjusted principal inertias
arch effect
aspect ratio
asymmetric biplot/map
asymmetric map
biplot
biplot axis
biplot vector
bootstrappping
Burt matrix
CA
calibration
canonical correspondence analysis (CCA)
CCA
centroid
chi-square distance
chi-square statistic
classical scaling
concatenated table
contingency ratio
contingency table
contribution (or standard) biplot
contribution to inertia
contribution to variance
correspondence analysis (CA)
covariance biplot
dimension
dimension reduction
dimensionality
dissimilarity
distance
double centring
doubling
dual biplots
dummy variable
eigenvalue
Euclidean distance
form biplot
generalized linear model (GLM)
generalized linear model biplot
GLM
gradient
identification condition
independence model (also called the “homogeneity hypothesis”)
indicator matrix
inertia
interactive coding
JCA
joint correspondence analysis (JCA)
left matrix
linear discriminant analysis
link vector
log-ratio
log-ratio analysis
log-ratio distance
Mahalanobis distance
map
mass
MCA
MDS
monotonically increasing function
multidimensional scaling (MDS)
multiple correspondence analysis (MCA)
nested principal axes
normalization
optimal scale
outlier
partial bootstrap
PCA
permutation test
principal axis
principal component analysis (PCA)
principal coordinates
principal inertia
profile
projection
projection matrix
rank
RDA
redundancy analysis (RDA)
regression biplot
right matrix
scalar product
scree plot
simplex
singular value decomposition (SVD)
skew-symmetric matrix
standard coordinates
standardized regression coefficient
subset correspondence analysis
supplementary point
supplementary variable
SVD
symmetric map
target matrix
transition formula
transition relationship
triangular inequality
triplot
vertex
Ward clustering
weighted Euclidean distance
