Tutorials

1

            Overview of Correspondence Analysis
   Independence and interaction between categorical variables.
   The two PCAs on contengency tables.
   What is expected from a graphic representation ?

2

           Mechanism of Correspondence Analysis
    Reformating data.
    The Chi-square distance.
    The two PCAs on rows and columns.

3

         Interpretation of Correspondence Analysis
    Plots.
    Interpretation of the total inertia.
    Eigenvalues.
    Inertia of the modalities.
    Quality of representation of the modalities.
    Inertia of the factors.

4

                                  An example (1)
   The data.
    Inertia.
    Interpretation of the factors.
    Summary of intyerpretation of the factors.

Tutorials

5

            An example (2): interpreting the modalities
    Quality of representation, square cosines.
    Distances to the origin.
    Heavy modalities.
    Neighboring modalities.

6

                   An example (3) : the combined plot   
    The basic idea.
    Neighboring modalities.
    Confirming with the contengency table.
    Summary of the analysis.

7

                                  Complements
    Supplementary variables.
    Ordinal variables.
    The Guttman effect.

Tutorials

1

       Overview of Principal Components Analysis
    Maximizing projected inertia.
    PCA on observations and on variables.
    Interpretation of a PCA.
    Other applications.

2

                         PCA on observations
   Maximizing the projected inertia on an axis.
   Principal Components and factorial planes.
   Quality of representation and influential observations

3

                         PCA on variables
   The space of variables.
   "Distance" between two variables.
   Circle of correlations.
   Detecting correlated, anticorrelated
   and uncorrelated variables.

Tutorials

4

                           Interpretion of a PCA
   Quality of a Principal Component.
   Interpreting a Principal Components.
   Plots of observations and plots of variables.
   Eigenvalues and communalities.

 5

                       Other applications of PCA
   Data compression and reconstruction.
   Data pre-processing.

Tutorials

1

The Minimum Distance rule.
    Sum-of-squares criterion.
   The K-means algorighm.
   Incremental K-means.

Tutorials

 2

Initialization of K-means (number and positions of centroids).
   Interpretation aids.