# mrSess_25 by pawan922

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```									WELCOME
FRIENDS
Multivariate measures of
association

• Factor Analysis
• Cluster Analysis

Cluster Analysis
Factor analysis
• The term factor analysis was first introduced
by Thurston in 1931.
• The main applications of this technique are:
1- To reduce the number of variables to a
manageable number.
2- To detect the structure in the relationship
between variables.
Factor analysis
• METHOD: It can be done in many ways. The
approach used mostly principle component analysis. In
this the given set of variables is reduced to another set
of composite variable.
•   These variables are not correlated to one another. These
new variables known as factors account for the variance
of entire data.
•   The best combination of variables forms the first factor
& accounts for most of variance.
•   The next factor is chosen such that it is the best
combination that accounts for variance unexplained by
the first factor.
•   This process is repeated until all the variance is
accounted for, but researchers stops after most of the
variances accounted for.
Cluster analysis
• Cluster analysis was developed to cater to
getting the answer of- How to organize
observed data into meaningful structure.
• Unlike Discriminant analysis begins with it
doesn’t have prior hypothesis.
• Cluster analysis starts with an
undifferentiated group of people, events &
objects & recognizes them into homogenous
group.
Cluster analysis
• Method: Once the sample is selected the following
steps are through gone through to arrive @ cluster.
•   Variables on which objects, events or people are to be
measured should be defined.
•   Similarities among the elements should be calculated.
•   Mutually exclusive groups (cluster) should be made.
•   These clusters should be compared with each other &
they could be validated.
•   Different cluster methods result in different results so the
data must be understood well to differentiate
between the real & imposed cluster.
THANKS
• BY:
SANDEEP SAXENA
FACULTY MEMBER MARKETING