Day 9_TEAM_C_Descriptive Analysis
-By Akash Singh
Discriminant Analysis?
Discriminant Analysis may be used for two objectives:
-Assess the adequacy of classification, given the
group memberships of the objects under study
-Assigning objects to one of a number of (known)
groups of objects.
It is an imperative tool in SPSS that helps in the prediction of
the outcome. It can thus have a descriptive or a predictive objective.
Each method has a
different approach; however the basic framework of all the techniques is the
same:
In the given flow:
•
Selection
of panel members
•
Term
generation (or selection of appropriate lexicon)
•
Concept
formation
•
Testing of
panel consonance
•
Evaluation
of products
PANEL SELECTION:
The selection of panel
members is very important to the quality of the data obtained. Potential
members need to be screened for their ability to discriminate between similar
samples, rate products for intensity and identify tastes and aromas. Equally,
or possibly more, important than a panelists’ sensory acuity is their
motivation. A panelist who feels they are required to participate may not
perform as well as and equally skilled panelist who feels motivated to
participate.
PANEL TRAINING:
Panel training encompasses term generation,
concept alignment and panel testing phases. The amount of training required is
dependent upon the method used as well as the product(s) to be tested. A
company with an in-house descriptive panel may spend several months or more
training a panel over a wide range of products, rather than training the panel
specifically for each product as needed.
DA involves the determination of a
linear equation like regression that will predict which group the case belongs
to. The form of the equation or function is:
D = C + nX1 + nX2 + nX3+….niXi
Where, D = Discriminant function
n = Discriminant coefficient
X = Respondent’s score for that variable
C= Constant
i = the number of predictor variables
What is the purpose of Descriptive
Analysis?
•
Predictive DA addresses the question
of how to assign new cases to groups.
•
To determine the most thrifty way to
distinguish between groups.
•
To
classify cases into groups. Statistical significance tests using chi-square
enable you see how well the function separates the groups.
•
To test theory whether cases are
classified as predicted.
Submitted by: Team C
Akash Singh
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