Key
Features
and Results
Benefits
XLSTAT-ADA’s functions
Canonical correlation analysis

Studies the correlation between two
sets of variables

Extracts a set of canonical variables
that are as closely correlated with both
tables as possible and orthogonal to
each other.

Symmetrical method
Canonical correlation analysis
Recording of data on men in a training
center,
Two sets of data:
 The physiological data:
• Weight
• Waist
• Pulse

The exercises the men did:
• Chin-ups
• Sit-ups
• Jumps
Canonical correlation analysis

Men doing sit-ups or chin-ups
have usually a smaller waist.

In general people training more
have a smaller waist and weight.

Jumping seems to have an
impact on the weight but not as
much on the waist.
Redundancy analysis

Redundancy Analysis is an alternative
to Canonical Correlation Analysis.

Non-symmetric method.

The components extracted from X are
such that they are as closely correlated
with the variables of Y as possible.
Then, the components of Y are
extracted so that they are as closely
correlated with the components
extracted from X as possible.
Redundancy analysis

Same example as Canonical correlation analysis:
Recording of data on men in a training center,
Two sets of data:
 The physiological data:
• Weight
• Waist
• Pulse

The exercises the men did:
• Chin-ups
• Sit-ups
• Jumps
Redundancy analysis

Same relationships are
observed:
• Men doing more sit-ups or
chin-ups have usually a
smaller waist.
• In general people training
more have a smaller waist
and weight.
• Jumping seems to have an
impact on the weight but not
as much on the waist.

The larger the waist, the lower the pulse
Note that the first factor is explaining more variance than in canonical correlation
analysis (93,30)
Redundancy analysis

It is possible to project the
observations in the same
graphic.

It is easy to visualize which
men are doing more
exercises and the one being
fitter.
Canonical Correspondence Analysis

Canonical Correspondence Analysis
(CCA) was developed to allow
ecologists to relate the abundance of
species to environmental variables.

Principles of Canonical Correspondence
q
Analysis p
species
n
sites

T1
Contingency table
descriptive variables
n
sites
T2
CCA  simultaneous representation of the
sites, the objects, and the variables
describing the sites.
Canonical Correspondence Analysis

Canonical Correspondence Analysis
can be divided into two parts:
• A constrained analysis in a space which
number of dimensions is equal to q =
analysis of the relation between the two
tables T1 and T2.
• An unconstrained part = analysis of the
residuals.

XLSTAT-ADA offers as well:
• Partial CCA
• PLS-CCA
Canonical Correspondence Analysis

Contingency table:
• the counts of 10 species of insects
• on 12 different sites in a tropical region.

A second table includes 3 quantitative variables that describe
the 12 sites:
• altitude,
• humidity,
• and distance to the lake.
Canonical Correspondence Analysis

Some insects: insects 2,
4 and 5 prefer the humid
sites, such as sites 7 to
12, while some prefer dry
climates such as insects
1, 6, 8 and 10.

Insect 9 prefers a climate
with higher altitude
Principal coordinate analysis


Principal Coordinate Analysis is aimed
at graphically representing a
resemblance matrix between p
elements.
The algorithm can be divided into three
steps:
Principal coordinate analysis

Principal Coordinate Analysis is aimed at
graphically representing a resemblance
matrix between p elements.
 The algorithm can be divided into three
steps:
1. Computation of a distance matrix for
the p elements
p
p
x11 x12
x1p
0 d12
0
d1p
0
n
0
p
0
dp1 dp2
xn1 xn2
xnp
0
Principal coordinate analysis

Principal Coordinate Analysis is aimed at
graphically representing a resemblance
matrix between p elements.
 The algorithm can be divided into three
steps:
2. Centering of the matrix by rows and
columns
p
p
p
x11 x12
x1p
0 d12
0
-r1-c1
d1p
d1p-r1-cp
0
n
0
p
0
dp1 dp2
xn1 xn2
xnp
p
0
dij-ri-cj
dp1-rp-c1
-rp-cp
Principal coordinate analysis

Principal Coordinate Analysis is aimed at
graphically representing a resemblance
matrix between p elements.
 The algorithm can be divided into three
steps:
3. Eigen-decomposition of the
centered distance matrix
p
x11 x12
p
x1p
p
0 d12
0
-r1-c1
d1p
t
d1p-r1-cp
t
0
n
0
p
0
dp1 dp2
xn1 xn2
xnp
p
0
dij-ri-cj
dp1-rp-c1
p
-rp-cp
p
Principal coordinate analysis

Principal Coordinate Analysis is aimed at
graphically representing a resemblance
matrix between p elements.
 The algorithm can be divided into three
steps:
1.
2.
3.

Computation of a distance matrix for the p elements
Centering of the matrix by rows and columns
Eigen-decomposition of the centered distance
matrix
The rescaled eigenvectors correspond to
the principal coordinates that can be used
to display the p objects in a space with 1,
2, p-1 dimensions.
Principal coordinate analysis

P1
P2
P3
P4
P5
5 products are graded by 10
individuals
Ind1
2
1
4
5
3
Ind2
3
1
2
5
4
Note that product 4 is preferable.
Ind3
1
2
4
3
5
Ind4
3
2
4
5
1
Ind5
3
2
4
5
1
Ind6
2
1
3
5
4
Ind7
1
3
4
2
5
Ind8
3
1
2
4
5
Ind9
3
1
2
5
4
Ind10
1
4
5
2
1
Average
2,2
1,8
3,4
4,1
3,3
Principal coordinate analysis

The results is a map of the
proximity of the 5 products.

P1 and P3 are the most
similar products.
Generalized Procrustes Analysis (GPA)

GPA is a pretreatment used to:
• reduce the scale effects
• and obtain a consensual configuration
on data where products have been
graded by several judges.

GPA compares the proximity between
the terms that are used by different
experts to describe products.
Generalized Procrustes Analysis (GPA)

10 experts graded 4 cheeses for 3
sensory attributes:
• Acidity
• Strangeness
• Hardness
Generalized Procrustes Analysis (GPA)

The products do not have
the exact same grade by
each expert
Generalized Procrustes Analysis (GPA)

A consensus can be found
for the position of each
product

Cheese 1 and 2 are the
strangest

Cheese 3 is the Hardest
Generalized Procrustes Analysis (GPA)

Strangeness is not graded in the
same way by the different experts

Acidity and Hardness are quite
reproducible
Multiple Factor Analysis (MFA)

MFA is a generalization of PCA
(Principal Component Analysis) and
MCA (Multiple Correspondence
Analysis).

MFA makes it possible to:
• Analyze several tables of variables
simultaneously,
• Obtain results that allow studying the
relationship between the observations,
the variables and tables.
Multiple Factor Analysis (MFA)

36 experts have graded 21 wines
analysed on several criteria:
• Olfactory (5 variables)
• Visual (3 variables)
• Taste (9 variables)
• Quality (2 variables)
Multiple Factor Analysis (MFA)

MFA groups the information on one chart
Multiple Factor Analysis (MFA)

MFA groups the information on one chart
Multiple Factor Analysis (MFA)

Wine 13 is in the direction
of the two quality variables
and is therefore the wine of
preference.
Multiple Factor Analysis (MFA)

The olfactory criteria are
often increasing the
distance between the
wines.
Penalty analysis

Identify potential directions for the
improvement of products, on the basis of
surveys performed on consumers or
experts.

Two types of data are used:
• Preference data (or liking scores) for a
product or for a characteristic of a product
• Data collected on a JAR (Just About
Right) scale
Penalty analysis
A type of potato chips is evaluated:
 By 150 consumers
 On a JAR scale (1 to 5) for 4 attributes:
• Saltiness,
• Sweetness,
• Acidity,
• Crunchiness.
 And on an overall liking (1 to 10) score
scale
Penalty analysis
Mean of Liking for JAR – Mean of Liking for too little
and too much
Semantic differential charts

The semantic differential method is a
visualization method to plot the
differences between individuals'
connotations for a given word.

This method can be used for:
• Analyzing experts’ agreement on the
perceptions of a product described by a
series of criteria on similar scales
• Analyzing customer satisfaction surveys
and segmentation
• Profiling products
Semantic differential charts

1 yoghurt

5 experts

6 attributes:
• Color
• Fruitiness
• Sweetness
• Unctuousness
• Taste
• Smell
Semantic differential charts
TURF analysis

TURF = Total Unduplicated Reach and
Frequency method

Highlight a line of products from a
complete range of products in order to
have the highest market share.

XLSTAT offers three algorithms to find
the best combination of products
TURF analysis


27 possible dishes
185 customers

"Would you buy this
product?" (1: No, not
at all to 5: Yes, quite
sure).

The goal is to obtain
a product line of 5
dishes maximizing
the reach
TURF analysis
Product characterization

Find which descriptors are discriminating
well a set of products and which the most
important characteristics of each product
are.

Check the influence on the scores of
attributes of:
• Session
• Product
• Judge

• Judge*Product
All computations are based on the
analysis of variance (ANOVA) model.
Product characterization

29 assessors

6 chocolate drinks

14 characteristics:
• Cocoa and milk taste and flavor
• Other flavors: Vanilla, Caramel
• Tastes: bitterness, astringency,
acidity, sweetness
• Texture: granular, crunchy, sticky,
melting
Product characterization
DOE for sensory data analysis

Designing an experiment is a
fundamental step to ensure that the
collected data will be statistically usable
in the best possible way.
DOE for sensory data analysis

Prepare a sensory evaluation where
judges (experts and/or consumers)
evaluate a set of products taking into
account:
• Number of judges to involve
• Maximum number of products that a judge
can evaluate during each session
• Which products will be evaluated by each of
the consumers in each session, and in what
order (carry-over)


Complete plans or incomplete block
designs, balanced or not.
Search optimal designs with A- or Defficiency
DOE for sensory data analysis



60 judges
8 products
Saturation: 3 products / judge
DOE for sensory data analysis
DOE for sensory data analysis
Let XLSTAT-ADA complete your advanced analytical
needs
info@xlstat.com