This dataset contains 18 rows and 8 columns, 4 rows are considered as illustrative, 3 columns are considered as illustrative.

## Warning: package 'FactoMineR' was built under R version 4.0.3

1. Study of the outliers

The detection of outliers does not apply to CA results.

2. Inertia distribution

The inertia of the first dimensions shows if there are strong relationships between variables and suggests the number of dimensions that should be studied.

The first two dimensions of analyse express 78.17% of the total dataset inertia ; that means that 78.17% of the rows (or columns) cloud total variability is explained by the plane. The inertia observed on the first plane is smaller than the reference value that equals 82.57%, therefore low in comparison (the reference value is the 0.95-quantile of the inertia percentages distribution obtained by simulating 400 data tables of equivalent size on the basis of a uniform distribution). However, the inertia related to the first dimension is greater than the reference value 55.33%. Even if the inertia projected on the first plane is not significant, these explained by the first dimension is significant.

Figure 2 - Decomposition of the total inertia The first factor is major: it expresses itself 57.04% of the data variability. Note that in such a case, the variability related to the other components might be meaningless, despite of a high percentage.

An estimation of the right number of axis to interpret suggests to restrict the analysis to the description of the first 1 axis. These axis present an amount of inertia greater than those obtained by the 0.95-quantile of random distributions (57.04% against 55.33%). This observation suggests that only this axis is carrying a real information. As a consequence, the description will stand to these axis.

3. Description of the dimension 1

Figure 3.1 - Overlayed factor map (CA) The rows in light blue are considered as active whereas those in dark blue are illustrative. The columns in light red are considered as active whereas those in dark red are illustrative. The labeled rows are those with the higher contribution to the plane construction. The labeled columns are those the best shown on the plane.

The dimension 1 opposes factors such as circumstances and economic (to the right of the graph, characterized by a strongly positive coordinate on the axis) to factors such as unemployment and work (to the left of the graph, characterized by a strongly negative coordinate on the axis).

The group in which the factors circumstances and economic stand (characterized by a positive coordinate on the axis) is sharing :

The group in which the factors unemployment and work stand (characterized by a negative coordinate on the axis) is sharing :

Note that the factor fear is highly correlated with the dimension (correlation of 0.9). This factor could therefore summarize itself the dimension 1.

Annexes

dimdesc(res, axes = 1:1)
$`Dim 1`
$`Dim 1`$row
                     coord
hard          -0.249984356
finances      -0.236995598
unemployment  -0.212227692
work          -0.211677086
employment    -0.136754598
money         -0.115267468
housing       -0.006680991
egoism         0.059889455
health         0.111651752
disagreement   0.146277736
future         0.176449413
fear           0.203347917
comfort        0.209670471
war            0.216824026
to_live        0.308306674
economic       0.353963920
circumstances  0.400922001
world          0.523304472

$`Dim 1`$col
                          coord
unqualified         -0.20931790
more_fifty          -0.17706810
cep                 -0.13857658
fifty               -0.01706444
thirty               0.10541339
bepc                 0.10875778
university           0.23123279
high_school_diploma  0.27403930

Figure 4 - List of variables characterizing the dimensions of the analysis.