Discriminant analysis is a statistical technique used to classify cases into mutually exclusive groups based on a set of predictor variables. It determines which continuous variables discriminate between two or more naturally occurring groups. For example, it can predict whether a student will pass or fail an exam based on their marks in previous tests. The independent variables are those that are manipulated, like age, while the dependent variable depends on the independent variables, like height depending on age. Discriminant analysis is useful for problems that involve predicting categorical outcomes, like predicting if a loan applicant will default or a machine requires repair based on various indicators. The objectives of discriminant analysis are to determine differences between groups based on predictors, identify important predictors, create discriminant functions for classification,

1. A B D U L B A S I T
K H AT I R
( K U B S )
U N I V E R S I T Y O F
K A R A C H I

2. INTRODUCTION TO DISCRIMINANT
ANALYSIS
• Discriminant analysis, a loose derivation from the word discrimination, is a concept
widely used to classify levels of an outcome. Discriminant Function Analysis is used to
determine which continuous variables discriminate between two or more naturally
occurring groups. Discriminant analysis is a segmentation tool. It segments groups in a
way as to achieve maximum separation between them.
• For example, I may want to predict whether a student will “Pass” or “Fail” in an exam
based on the marks he has been scoring in the various class tests in the run up to the
final exam.

3. INDEPENDENT AND DEPENDENT
VARIABLE
• “The independent variable is the variable the experimenter manipulates (i.e. changes)
,assumed to have a direct effect on the dependent variable.” “The dependent variable
is the variable the experimenter measures in their experiment
• dependent variable is how tall you are at different ages.
• The dependent variable (height) depends on the independent variable (age).

4. WHY DISCRIMINANT ANALYSIS?
• Discriminant, as the name suggests, is a method of analyzing business problems, with the
goal of differentiating or discriminating the response variable into its distinct classes.
Typically Discriminant analysis is put to use when we already have predefined
classes/categories of response and we want to build a model that helps in distinctly
predicting the class, if any new observation comes into equation.
• Some relevant real life examples of where a Discriminant model can be used are
• 1. When we want to predict whether an applicant for a bank loan is likely to default or not.
• 2. Predict likelihood of a heart attack based on various health indicators.
• 3. Predict stability level—“Good”, “Requires Inspection” or “Requires Repair/Replacement”-
of an engine/machine based on various performance indicators.

5. PURPOSE AND OBJECTIVE OF
DISCRIMINATION ANALYSIS
• A researcher can usually predict to which category or group a subject belongs. For
example, if a recruiting manager wishes to know if the candidates will be high
performers or low performers, a researcher can utilize the discriminant analysis. In
short, the objectives of discriminant analysis can be summed up as follows:
• Figuring out whether there are major differences among the groups, as per the
independent variables.
• Determining which independent variable adds to most of the differences in between
groups
• Forming discriminant functions
• Assessing how accurate the classification is